Forecasting Energy Consumption in Dwellings -...

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250 T. Szelitzky and E.H. Dulf 9. Finite Element Method Magnetics : Documentation: FEMM Reference Manual, http:/ / www.femm.info/Archives/doc/manua142.pdf 10. Kelemen, A., Kutasi, N.: Induction-heating voltage inverter with hybrid LLC resonant load, the D-Q model. In: Pollack Periodica, vol. 2(1), pp. 27-37. Akademiai Kiad6, Budapest (2007) 11. Grajales, L., Lee, F.e.: Control System Design And Small-Signal Analysis of a Phase- Shift-Controlled Series-Resonant Inverter For Induction Heating. In: 26th Annual IEEE Power Electronics Specialists Conference Record, vol. I, pp. 450--456 (1995) 12. Festila, c., Both, R., Dulf, E.H., Cordos, R.: Adaptive Robust Stability for Extremum Control System with a modified Implementation. In: 18th International Conference on Control Systems and Computer Science, vol. I, pp. 227-331. Politehnica Press, B (20 II ) 13. Dulf. E.H.: Robust Control- Case Studie (Romanian). Mediamira, Cluj Napoca (2007) 14. Gu, D.-W., Petrov, P.H., Konstantinov, M.M.: Robust Control Design with MATLAB®. Springer, London (2005) 15. Zhou, K., Doyle, 1.c.: Essentials of Robust Control. Prentice Hall, Englewood Cliffs (1997) 16. Szelitzky, T., Dulf, E.H., Inoan, 1. , Festila, C., Neaga, A.O.: Robust Control in Fre- quency Controlled Induction Heating Inverters. In: 18th International Conference on Control Systems and Computer Science, vol. I, pp. 298-301. Politehnica Press, Bucu- resti (2011) 17. Sanchez Pena, R., Sznaier, M.: Robust Systems: Theory and Applications. John Wiley & Sons Inc., New York (1998) Forecasting Energy Consumption in Dwellings Nicoleta Arghira I, Stephane Ploix 2 , Ioana I, and Sergiu Stelian Uiescu 1 1 Automatic Control and Computers Faculty, University Politehnica of Bucharest, 313 Splaiu Independentei, 060042 Bucharest, Romania 2 Grenoble Institute of Technology, 46 Avenue Felix Viallet, 38031 Grenoble Cedex I, France [email protected], [email protected], {ioana,Iliescu}@shiva.pub.ro Abstract. Energy consumption is a major issue nowadays. The importance of fo- recasting energy consumption from end-user to power system operator becomes more obvious than ever. The consumption in the residential sector represents a significant percentage in the total electricity demand in Europe and all over the world and it is expected to grow. So, the prediction of energy consumption be- comes a key component in the management (e.g. power flow) of the electrical grid. This paper presents different methods for prediction of energy consumption of electrical appliances used in dwellings. A stochastic approach is used since fo- recasting the consumption for a single appliance is more difficult that predicting the overall consumption. Different basic predictors are presented and a stochastic predictor is proposed and tested according to a prediction precision criterion. The enhancement of forecast precision is done by segmentation and aggregation of da- ta. Several experiments are conducted for different appliances in the house and the results are discussed. Keywords: energy consumption, stochastic process, energy forecast, smart home. 1 Introduction Energy consumption is a major issue nowadays. The biggest concern of power sys- tem operators is to maintain the balance between generation and load. Power grids today are controlling generation to match load at any particular time. If until now in peak demand periods the equilibrium was kept by cutting loads, the development of communication and technology gives the possibility of controlling the energy de- mand also. The load-following strategy becomes more difficult as more renewable generation is added to the grid. Intermittent renewable energy sources like wind and solar generation can't be scheduled and can't be predicted with certainty. So, in the last decade, the concept of demand dispatch gains importance all around the world. Demand dispatch is the capability to aggregate and precisely control (or dispatch) 1 Dumitrache (Ed.): Adv. in Intelligent Control Systems & Computer Science, AlSC 187, pp. 251-264. springerlink.com © Springer-Verlag Berlin Heidelberg 2013

Transcript of Forecasting Energy Consumption in Dwellings -...

Page 1: Forecasting Energy Consumption in Dwellings - Indesenindesen.ats.com.ro/rezultate/articole/upb/2013_scan_springer_book.pdf · Finite Element Method Magnetics : Documentation: FEMM

250 T Szelitzky and EH Dulf

9 Finite Element Method Magnetics Documentation FEMM Reference Manual http wwwfemminfoArchivesdocmanua142pdf

10 Kelemen A Kutasi N Induction-heating voltage inverter with hybrid LLC resonant load the D-Q model In Pollack Periodica vol 2(1) pp 27-37 Akademiai Kiad6

Budapest (2007) 11 Grajales L Lee Fe Control System Design And Small-Signal Analysis of a Phaseshy

Shift-Controlled Series-Resonant Inverter For Induction Heating In 26th Annual IEEE Power Electronics Specialists Conference Record vol I pp 450--456 (1995)

12 Festila c Both R Dulf EH Cordos R Adaptive Robust Stability for Extremum Control System with a modified Implementation In 18th International Conference on Control Systems and Computer Science vol I pp 227-331 Politehnica Press

Bucure~ti (20 II ) 13 Dulf EH Robust Control- Case Studie (Romanian) Mediamira Cluj Napoca (2007) 14 Gu D-W Petrov PH Konstantinov MM Robust Control Design with

MA TLABreg Springer London (2005) 15 Zhou K Doyle 1c Essentials of Robust Control Prentice Hall Englewood Cliffs

(1997) 16 Szelitzky T Dulf EH Inoan 1 Festila C Neaga AO Robust Control in Freshy

quency Controlled Induction Heating Inverters In 18th International Conference on Control Systems and Computer Science vol I pp 298-301 Politehnica Press Bucushy

resti (2011) 17 Sanchez Pena R Sznaier M Robust Systems Theory and Applications John Wiley

amp Sons Inc New York (1998)

Forecasting Energy Consumption in Dwellings

Nicoleta Arghira I Stephane Ploix2 Ioana Fagar~an I and Sergiu Stelian Uiescu 1

1 Automatic Control and Computers Faculty University Politehnica of Bucharest 313 Splaiu Independentei 060042 Bucharest Romania

2 Grenoble Institute of Technology 46 Avenue Felix Viallet 38031 Grenoble Cedex I France arghiranicoletagrnailcom stephaneploixgrenoble-inpfr ioanaIliescushivapubro

Abstract Energy consumption is a major issue nowadays The importance of foshyrecasting energy consumption from end-user to power system operator becomes more obvious than ever The consumption in the residential sector represents a significant percentage in the total electricity demand in Europe and all over the world and it is expected to grow So the prediction of energy consumption beshycomes a key component in the management (eg power flow) of the electrical grid This paper presents different methods for prediction of energy consumption of electrical appliances used in dwellings A stochastic approach is used since foshyrecasting the consumption for a single appliance is more difficult that predicting the overall consumption Different basic predictors are presented and a stochastic predictor is proposed and tested according to a prediction precision criterion The enhancement of forecast precision is done by segmentation and aggregation of dashyta Several experiments are conducted for different appliances in the house and the results are discussed

Keywords energy consumption stochastic process energy forecast smart home

1 Introduction

Energy consumption is a major issue nowadays The biggest concern of power sysshytem operators is to maintain the balance between generation and load Power grids today are controlling generation to match load at any particular time If until now in peak demand periods the equilibrium was kept by cutting loads the development of communication and technology gives the possibility of controlling the energy deshymand also The load-following strategy becomes more difficult as more renewable generation is added to the grid Intermittent renewable energy sources like wind and solar generation cant be scheduled and cant be predicted with certainty So in the last decade the concept of demand dispatch gains importance all around the world Demand dispatch is the capability to aggregate and precisely control (or dispatch)

1 Dumitrache (Ed) Adv in Intelligent Control Systems amp Computer Science AlSC 187 pp 251-264 springerlinkcom copy Springer-Verlag Berlin Heidelberg 2013

252 253

N Arghira et al

mdividualloads on command Unlike traditional demand response demand dispatch is active and deployed at all the time not just at peak times [I

The importance of forecasting energy consumption from end-user to power system operator becomes more obvious than ever The control system used by the electrical grid operator has always had a forecast function for the demand at a large scale But with the involvement of the demand side resources and taking into account the consumers comfort the prediction has to be done at a smaller scale (eg electricity provider)

It was noticed that In terms of energy consumption the residential sector represhysents an important part of the total electricity demand In this context a proper prediction of energy demand in dwellings sector is very important A bottom-up approach [2) can be used first the prediction is done for each appliance in a home then the forecast will be made for the total energy consumed in a home and finally a prediction can be made regarding the households supplied by a certain energy provider It is more difficult to predict the consumption of each appliance that the overall consumption but there can be a great save of energy when considering a dynamic demand side management The energy savings depend on the type of appliance some can be shut down some can be postponed and some cannot be changed (3)

The purpose of this paper is to predict the energy consumption in houses for the next 24 hours as the energy price in the day-ahead market is set for each hourly interval with one day in advance The prediction of the next day energy consumpshytion for different services in a house is an important part of a home automation system as seen in [3) [4) presents a household energy control system with three layers anticipative layer reactive layer and device layer The anticipative layer is mainly concerned with the predictions of energy consumption

Several papers propose methods for energy prediction but few of them considshyer the energy consumption at a house scale and even less papers are regarding the forecast for some electrical devices in dwellings In [5) a short term energy predicshytion at house level is done using with support vector machines In (6) the forecast for some residential consumers is done for 24 hours or a week using neural netshyworks but still at house level A prediction based on Bayesian Networks for a sinshygle appliance in dwellings is considered in (7) An enriched learning algorithm which proposes a general way to take expert knowledge into account is shown in (8) Although some conclusions can be drown from this papers concerning the forecast of energy consumption for different appliances a more general method has to be found

This paper presents a stochastic method which predicts the consumption for electrical services in dwellings Since the learning period is critical for the foreshycast the best historical data interval is searched also The method was tested for all the appliances for which data was available

Forecasting Energy Consumption in Dwellings

2 Energy Consumption in Smart Grids

This paragraph presents the smart grid and smart home concepts in order to better understand the importance of energy prediction for appliances ill dwellings

21 Smart Grid

The concept of smart grid appeared as an answer to the new power system chalshylenges Better control systems which include advanced protection functions (9) new measurement systems improved communication and modern technology have to be proposed Smart grid integrates the use of sensors communications computational ability and control in order to enhance the overall functionali ty of the electric power system (10) Smart grid initiatives seek to improve operations maintenance and planning using modern technology in order to better manage energy use and costs [1 1] Many governments sustain modem networks in the global context of energy saving and environment issues United States Department of Energy have defined the functions required for smart grids in (12) the ability to heal itself to motivate consumers to actively participate in operations of the grid to resist attack to provide higher power quality to accommodate a1l generation and storage options to enable electricity markets to flourish to manage more effishyciently the assets and costs Figure 1 depicts the important components of the smart grid (13)

Fig 1 The smart grid components

ConSidering the fact that energy resources are limited there are many policies for energy saving on European and world scale A good method to lilnit or change the energy consumption habits is the energy price The energy market is a powershyful instrument that sets the prices between the energy producers and energy supshypliers and consumers (CIC4) figure 2 It has an important role in the power system nowadays but it is a complex bidding rules mechanism which sometimes is difficult to follow The energy market is divided into different categories but

254 255 N Arghira et al

the Day Ahead Market or Spot Market is of great interest This type of energy marshyket in volves bidding the energy consumption of the next day It is a very complex mechanism which requires a very good knowledge of the demand for the power suppliers The participation in the day ahead energy market imposes a dynamic energy management (hour by hour changes in the energy productionconsumption ratio)

INpoundRG Y MARKET

F ig 2 Energy market in the power system

22 Smart Home

Smart homes are buildings equipped with a home automation system (HAS) able to optimize the energy management in a dwelling A home automation system basically consists of household appliances linked via a communication network alshylowing interactions for control purposes Thanks to this network a load manageshyment mechanism can be carried out taking into account the users preferences This system consists of a set of appliances fitted with micro-controllers able to communicate two-way via standard protocols

The home automation system should be able to take the best decisions in order to meet the energy consumption needs of the consumers in an economical manner Energy management problem can be formulated as scheduling problem where energy is considered as a resource shared by appliances and device energy deshymands considered as tasks [3] presents a three-layer household energy control system capable both to satisfy the maximum available electrical energy constraint and to maximize user satisfaction criteria The proposed architecture contains an equipment layer a protection layer and an anticipation layer The equipment layer composed by existing control systems is responsible of adjusting equipment conshytrols in order to reach given set points in spite of perturbations The protection layer is responsible of decision making in case of violation of predefined constraints dealing either with energy or with comfort The anticipation layer is responsible of managing predicted events dealing with electric sources or with electric loads in order to avoid the use of protection layer

Forecasting Energy Consumption in Dwellings

~- coo tJU)J

F ig 3 Architecture of a power manager

23 Challenges for Energy Providers - Importance of Energy Prediction

Energy providersretailers have the difficult task of dealing directly with the endshyusers and the electricity market They have to take decisions in real time for a reli shyable and profitable operation of the grid (no congestions or load shedding) One metllod of getting the consumers controlled at all times is by changing the tariffs during the day Figure 4 shows the interactions between the power retailers consumcrs - smart homes and the energy market in the spirit of cost control

POWER REfAILER IEne~~~1

1 Estimate ampicrgy Price

I 22 Computed ~ n(rgy consumption I 221 FSlimate gJobal energy demand

~ 21 Adjust consumption to get ~ the best compromise picecomforl

I 222 lluy enfrgy (Quantity 11me period)

222 1 Proides t nerg) (Quan ti ly F)e 1lY price)

3 Irorm (l1me period F)ergy prices)

Fig 4 1nteractions power retailer - smart home - energy market

257 256

Considering the actions between the actors of the energy market at the supply level it is obvious the importance of load prediction in the smart If dwellings are equipped wi th home automation systems capable of decisions the estimation of energy demand will be easier The prediction of the HAS must give accurate forecast for the appliances in the house in order build a reliable model and estimation on a larger scale

3 Load Forecasting

Load forecasting occupies a central position in terms of planning and operation electric utilities Load forecas ts are extremely important for energy Transport System Operators (TSOs) financial institutions and other paruCIPaDl in electric energy generation transmission distribution and markets 13 This tion presents a classification for load forecasting and the challenges of prediction for electrical appliances in dwellings

31 Classification of lAJad Forecasting

When considering the time period of forecast a classification into three can be done short-term forecasts which are usually from one hour to one medium forecasts which are usually from one day to a year and long-term casts which are lo nger than a year The forecasts for different time horizons important for different operations within a utility company

A review and categorization of electric load forecasting techniques is in [14-15J There are explained several methods for energy forecasting but of them concerns a single appliance in a home This paper tries to find a predict energy consumption for each device in a dwelling

32 Issues Concerning Energy Forecast in Dwellings

Predicting the energy consumption in tbe housing sector is a difficult task it vaJi es a lot depending on season weather conditions and user behaviour context of the home automation system the forecast for each electrical eq has to be done

The energy consumption of electrical devices varies a lot during a year or smaller periods of time It is difficult to find similarities in terms of between intervals of time In order to find patterns to help with predicting consumption the Euclidian distances are computed between each 2 days evaluated period data Several equipments in houses were tested but shows the differences for the freezer in house 2000949 Some knowledge has to be produced for a proper forecast

Forecasting Energy Consumption in Dwellings

t- -_u _ t m

Fig 5 Euclidian Distances between energy consumption values - freezer house 2000949

Forecasting Energy Consumption of Services in Dwellings

seen in previous section predicting the exact value of consumed energy is difshyso the aim of this paper is predicting whether one service will consume enshy

or not during each hour of the next 24 hours The predictor performance is based on recorded data which concern the energy

mption of appliances in 100 households in France during a full year The database comes from Residential Monitoring to Decrease Energy Use and

Emissions in Europe (REMODECE) which is a European database on pSidential consumption This database stores the characterization of residential

consumption by end-user and by country The information for each is recorded each 10 minutes and concerns the energy consumption for the

~ppliances and also the weather conditions (temperature wind strength wind flilection humidity)

Computing the Prediction Precision

designing a predictor it is important to set up a method for assessing the of a predictor because it clarifies the objectives For computing the

of a predictor test data are first to be considered Because of the predicshyrequired by an anticipative energy management system tbe test data are the

energy consumption for an appliance over a full year Figure 6 shows a set test data

258 259 N Arghira et aJ

Total sJte non-halogen Mghting ennrgy coneunpfo-n 20009G5 in hours

~ ~

-8 I ~

i 100 I III

i 1 11 111111111 illl]IIIII11 Ii

middot~OO~I ~ ~1h111ulII~__y jll~dlll Illi I((I HDI 1~ 100 he tisklry ihour]

Fig 6 Historical data for the energy consumption - non-halogen lighting in house 2000905

In order to evaluate the performance of predictors some concepts have to be defined

Let h be the current hour and e(h) be a binary value which is equal to 0 if the considered appliance is actually consuming energy during the hour hand 1 othershywise Let PaCh) be a prediction provided by the predictor a which is equal to 1 if the considered appliance is predicted as consuming power during the hour hand 0 otherwise The precision of the predictor is then expressed by

24 (25-i)e(h+i)- p (hh+24+i) J[ (h) - ( 1)za - =1 275

Any predictor a relies on an historical sliding time window of n hours used to preshyh n h

dict the d+ 1 predictions It can be denoted a - bull The number n has to be adshyjusted because if it is too large seasonal phenomena may disappear and if it is too short data set will not be sufficient to yield a precise prediction

The proposed algorithm for assessing a predictor a involves the fo llowing steps

1 Set the time window dimension to n hours within the period for which the historical data was registered where n goes from 24 to 36424

2 Compute the predictions for the data corresponding to the historical sliding time window

3 Compute the predictor precision n(h) based on the ne lCt day data for all possible hours h and compute an average precision for the predictor

42 Prediction with Basic Predictors

Since the informati on regarding thc energy consumption is very dependent on user behavior a stochastic approach will be tried Two trivial predictors are tested one

Forecasting Energy Consumption in Dwellings

that considers that the service will consume all the time in the future and one which considers the service will never consume

421 The Wiu Always Consume Predictor

This type of predictor involves considering that the appliance will consume energy permanently The prediction is computed based on a set of test data and refers to the probabili ty of the service to consume energy The prediction pihh+24) is elCshypressed

Pa (hh+24) = 1 h =12 24 (2)

Figure 7 shows the prediction precision Jra(h) for each time window of the test

data considered in days (the prediction precision for a sliding window of 1 day 2 days etc) This curve was obtained using the previously presented algorithm for assessing the predictor

422 The Will Never Consume Predictor

Thi s predictor assumes the service will not consume at all in the nelCt day The prediction is computed based on a set of test data and refers to the probability of the service not to consume) The prediction p(hh+24) is computed

Po Ch h+ 24) = 0 h = 12 24 (3)

It can be denoted that the value of precision for this predictor is the complement of the will always consume predictor precision Figure 8 shows the prediction preshycision for cach time window of the test data considered in days for this predictor Bcst precision is reached for a historical interval of aprolCimatelyl OO days

r r ~i ____ ~ r

Fig 7 Prediction precision assuming the Fig 8 Prediction probability assuming the service will consume continuously service will never consume

43 The Proposed Predictor

An inhabitant in the house interacts with various electrical devices as part of his routine activities Thus energy consumption can be modeled as a stochastic process

261 260 N Arghira et aI

In this context the proposed predictor specifies the probability of the appliance to consume on an hourly base We consider the following prediction form ula

n)Ch h+24) po(hh+24) gt PJ h=1224n(h)

(4)P (hh+24)= n (h h+24) P (h h+24) $ Po

(1 1- I bull a t

nth)1 Where n(h) is the considered number of hours h in the test period ndhh+24) is the number of limes the service did consume during hour h of the historical data and Pa is a set threshold Figure 9 shows the prediction precision of the proposed predictor related to the basic predictors in the previous subsections

E---middot~middot-7middotmiddotmiddotmiddotq I I ----r

bull _-- _ ~_____~_ _ ~-a- _ __________ ~~~ _I

- - ~ ---shyI I I I-==~It ---------~ - ~ ~ -- ~~~I

I I I I I 1

Imiddot -- - 1- - - ~ - - ~ - - - - - - - - -~ t I I I

_~ _ __ ~ ___ L __ _ _ _ _ h _ _~ ~

I I

-middot-middot-middot middot~ -middot-middot1middotmiddotI middot middot middot --middot middot middott middot middot middot -middotmiddot-middot~middot-middot-middot- middotmiddotmiddotmiddot middot I~ ~

Fig 9 Prediction precision using the proposed predictor

44 Enhancing the Forecast Precision

In order to increase the precision some similarities between data are considered and clustering methods are applied

441 Segmentation of Data

While mining the available data some pattern of recurrence is searched in order to immiddot prove the prediction A temporal segmentation can be used to introduce knowledge in the predictor for instance the use of the oven may be different for rainy Saturdays The segmentation of data can be made considering different aspects such as the seashyson month period of the day (daynight) type of day (weekday weekend) The obshyjective of this operation is to reduce the average dispersion in order to improve the prediction After the segmentation is done we will merge the segments thaI are similar using a clustering algorithm in order to gather the non-meaningful segments

temporal segmentation that considers each day of the week as a partition was done For each segment the hourly predictions are made considering the proshyposed predictor A k-Means clustering algorithm is applied in order to group the similar consumption days

Forecasting Energy Consumption in Dwellings

The KmiddotMeans algorithm assumes a fixed number of clusters specified in adshyvance Each cluster is defined by its cluster center and clustering proceeds by asshysigning each of the input data to the cluster with the closest centre The grouping is done by minimizing the sum of squares of distances between data and the corresponding cluster centroid This algorithm is based on the euclidean distance

ED(X n = JI (x - y -1 (5)

Where X Y are vectors n is the vector length and XiYi arc their components The center of each cluster is then re-estimated as the centroid of the points

assigned to it The process is then iterated until a convergence criterion is accomplished ED is a set threshold

ED(Xy) 5 ED (6)

442 The Prediction Precision after Clustering

After applying the iterative kmiddotMeans algorithm two clusters are obtained In the presented case cluster C) groups week days data and cluster C

2 gathers Saturday

and Sunday data After the clusters are obtained the initial data corresponding to the energy consumption is divided into 2 sets according to the number of clusters and the considered segments The prediction precision is computed for the proshyposed predictor for each of the clusters Figure 10 a and b show the prediction precision for the new test data The blue curve represents the precision for the proposed predictor and the other two are the curves for the basic predictors

Figures 9 and 10 a b present the prediction precision in 3 situations for initial energy consumption data for merged segments in cluster CI da ta and for merged segments in cluster C2 data When comparing the obtained curves for the proshyposed predictor the precision of the predictor increases after the clustering is done This proves that the segmentation of data and then the merging of similar partitions is a good method for increasing the prediction performance

iii PredlcWl Pr-JCiMon 1V 200C905 C1 Ptedc1i0l PrecSloln lV 2tJOO91)$ C2

r

-- ~----- - - ~ -- - ~--~ [3~ _ _ ___ _ --~_ c_

i=~ =~ == = ~ ~middotmiddot-jI - shy

Imiddot 1~~t~~ ~middot ~[T~middot~middotT-1- ---1- -

Jr bull _ ~ L __1_ _ I _ _ _ L _ _ bull L _ -=-~ 1bullbull bull 7 bullbull-_ -bullbull ~ _- I-~ -

middotmiddotmiddotmiddotmiddotmiddotmiddotmiddot middotmiddot IlI middot-bullbull7 (bullbullbull7bull71bullbull-bullbull- _ bullbull o- bull -- noshy

~-

a CllISler CI b CiUSler C2

Fig 10 Prediction precision comparison for the three predictors - clustered data

263 262 N Arghtra et al

5 Experiments and Discussion

This section presents the results for different electrical appliances in the house when the proposed predictor is used in comparison with the basic predictors will never consume and will consume continuousl y There are several services which were tested but this paper will show the results for appliances representashytive for their class the fridge as it consumes all the time the boiler as its consumption can be delayed and the lighting as it has a regular usage in a house

The prediction precision is computed as explained in subsection 41 for a slid shying time window between I and 364 days covering all the historical data avai lashyble The tests show that the prediction acts in a special manner depending on the type of the electrical appliance (16)

The prediction precision with the proposed predictor for the refrigerator (fig 13) is lower than the precision assuming permanent consumption for time winshydows higher than 2 days This implies that the prediction for the fridge should be done considering a short period of hi storical data (e g two weeks) in order to get a high precision This conclusion was expected since the energy consumption for this appliance is dependent on the season so a short period of time is significant for prediction

The pcrfonnance of prediction for the overall lighting consumption (figures 11) is hi gher than the performance of the basic predictors for the entire recorded data intervaL For the gas boiler (figure 12) a good precision is obtained for almost all the considered periods of historical data

As a general observation the maximum value for prediction accuracy is obshytained for a short period of historical data - almost in all studied cases the best precision was reached for one day of recorded data Using this result will decrease the computation time since the learning time is short

PoodidiM p Gas Solelt 2000949 011 Pr_ Prod_ 2OOOl 0 Tota si IggtIOIO II lt1gt Ii ~

~~--=- - - t- - - - - - - - - t shy

r r==~~bull J _ _ _ 1_ _ _ _1 __ bull J

bull __1 _ _ _ L __ __

1 ___ w

_ _ _ _ J _ 1__ _ __ _ _ - -1 shyi

~

~__ j _ __ _ _ J _ _ _ I _ _ _

Uj bull - ~ -~ ~~----~--- ~--- -~=~~ -- - shybull __ _ ~~ r __ _ _ bull - _ _ _ 1 _ _ _

j _ _

~I I ~ I J _ _-t~~~~-r-~~~middot~ ~+~~-~~~~~~~~~~ I 1

---- 1 ---- ~ --_~- _ 1IIfIot

-1 middotmiddot(1111

Fig 11 Prediction precision of the boiler Fig 12 Prediction precision of lighting con-elec tricity consumption in house 2000949 sumption in house 2000910

Forecasting Energy Consumption in Dwellings

Fridgil catwmptlon 2000949 houtty probablibM i f I I

middot -1 - _ L __ J _~_ middot _ _ J _ _ L __ ~I

I

E 1I1 - ~ - - -===~~-~~Iimiddot-- -- ~ ~

I

I I I I I It I I I I I bull I - - - - -r - - 1- -- r- - - - r --

I bull I I I

- - --- -- -- ~ - - - ~--~- - -f- - ~

I

==-=---- -----~-~~--- -~

Fig 13 Prediction precision of the refrigerator consumption in house 2000949

6 Conclusions

Predicting the energy consumption in dwellings is an essential part in the power management of the grid as the consumption in the residential sector represents a siguificant percentage in the total electricity demand The development of the smart grid is not possible without a good prediction of energy consumption The trend nowadays is to get the prediction of energy consumption not only at house level but at household appliance leveL

The prediction of energy consumption in housing is very dependent on inhabishytants behavior so a stochastic method for prediction has been presented in this paper The paper discusses about how to evaluate the precision of a predictor in the day +1 power management context Different basic predictors are presented and tested for the available historical data A relevant predictor is presented

Segmentation of data is done considering the patterns in energy consumption Also the historical data is divided according to the results of the k-means clustershying algorithm After testing the predictor on the new clustered data the precision of the predictor improves

Further work involves testing the proposed predictor for all the appliances in a house in order to decide the proper way for prediction at the equipment level

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philosophy in power system operation - A survey Scientific Bulletin of University POLITEHNJCA Buchares t Series C Electrical Engineering 73(2) 153- 166 (2011)

14 Feinberg EA Genethliou D Load Forecasting Applied mathematics for restrucshy

tured electric power systems pp 269-285 (2005) http www Spr i ngerLinkcomdoi 1010070-387-23471-3

15 AI fares KH Nazeeruddin M Electric load forecasting literature survey and classishyfication of methods In ternational Journal of Systems Science 33( 1)23-34 (2002)

16 Arghira N Ploi S Fagar3ltjan I Iliescu SS Prediction of energy consumption in homes In 18th Int Conf Control Systems and Computer Science Bucharest vol 1 pp 211-217 (2011 ) ISSN 2066-4451

Modelling and Composed Recursive Model Free Control for the Anaerobic Digestion Process

2Haoping W an g l B oyko Kachev bull Yang Tian l

2 3Ivan Simeonov and Nicolai Christov

1 Automation School Nanjing University of Science and Technology 200 Xiao Ling Wei St 210094 Nanjing China

2 The StephanAngeloff Institute of Microbiology - Bulgarian Academy of Sciences bl 26 Acad G Bonchev St 1113 Sofia Bulgaria

3 LAGIS UMR eNRS 8219 Universite Lille1 Sciences et Technologies 59655 Villeneuve d Ascq France hpwangnjusteducn kalchevmicrobiobasbg

Abstract This paper preseots a modelling and new composite recursive model free controller for trajectory tracking and disturbance compensation for the Anaerobic Digestion Process of cattle dung The used model is on the basis of a fifth-order continuous anaerobic digestion model And the proposed controller comprises a recursive model free controller based stabilization component and a time delay control based compensation component with recursi ve calculation structure which does not require any knowledge o f the model parameters Computer simulation examples illustrate the performance and robustness of the proposed approach

Keywords Anaerobic digestion composed recursive controller piecewise continuous systems recursive model free controller

1 Introduction

EnvirolUnental problems (ex air and water pollution) and energy shortage are nowadays recognized worldwide issues Several possibilities are available to treat these difficulties from nowadays different treatment ways biological processes are surely among the most used sustainable and efficient systems Anaerobic digestion (AD) which is a biotechnological process is widely used in life sciences wastewater treatment and a promising method for solving energy shortage and ecological protection problems in agriculture and agro-industry [1 ] In such kind of processes usually camed out in Continuously Stirred Tank bio-Reactors (CSTR) the organic matter is de-polluted by microorganisms into biogas (mainly methane ClL and carbon dioxide CO2) and compost in the absence of oxygen [2] [3]

AD is a very unstable process with regard to the bioreactor operation This is due to the complicated interactions between different microbial species as well as to the

1 Dumltrache (Ed ) Adv in lntelligent Control Systems amp Computer Science AlSC 187 pp 265-278 springcrlinkcom copy Springer-Verlag Berlin Heidelberg 2013

Page 2: Forecasting Energy Consumption in Dwellings - Indesenindesen.ats.com.ro/rezultate/articole/upb/2013_scan_springer_book.pdf · Finite Element Method Magnetics : Documentation: FEMM

252 253

N Arghira et al

mdividualloads on command Unlike traditional demand response demand dispatch is active and deployed at all the time not just at peak times [I

The importance of forecasting energy consumption from end-user to power system operator becomes more obvious than ever The control system used by the electrical grid operator has always had a forecast function for the demand at a large scale But with the involvement of the demand side resources and taking into account the consumers comfort the prediction has to be done at a smaller scale (eg electricity provider)

It was noticed that In terms of energy consumption the residential sector represhysents an important part of the total electricity demand In this context a proper prediction of energy demand in dwellings sector is very important A bottom-up approach [2) can be used first the prediction is done for each appliance in a home then the forecast will be made for the total energy consumed in a home and finally a prediction can be made regarding the households supplied by a certain energy provider It is more difficult to predict the consumption of each appliance that the overall consumption but there can be a great save of energy when considering a dynamic demand side management The energy savings depend on the type of appliance some can be shut down some can be postponed and some cannot be changed (3)

The purpose of this paper is to predict the energy consumption in houses for the next 24 hours as the energy price in the day-ahead market is set for each hourly interval with one day in advance The prediction of the next day energy consumpshytion for different services in a house is an important part of a home automation system as seen in [3) [4) presents a household energy control system with three layers anticipative layer reactive layer and device layer The anticipative layer is mainly concerned with the predictions of energy consumption

Several papers propose methods for energy prediction but few of them considshyer the energy consumption at a house scale and even less papers are regarding the forecast for some electrical devices in dwellings In [5) a short term energy predicshytion at house level is done using with support vector machines In (6) the forecast for some residential consumers is done for 24 hours or a week using neural netshyworks but still at house level A prediction based on Bayesian Networks for a sinshygle appliance in dwellings is considered in (7) An enriched learning algorithm which proposes a general way to take expert knowledge into account is shown in (8) Although some conclusions can be drown from this papers concerning the forecast of energy consumption for different appliances a more general method has to be found

This paper presents a stochastic method which predicts the consumption for electrical services in dwellings Since the learning period is critical for the foreshycast the best historical data interval is searched also The method was tested for all the appliances for which data was available

Forecasting Energy Consumption in Dwellings

2 Energy Consumption in Smart Grids

This paragraph presents the smart grid and smart home concepts in order to better understand the importance of energy prediction for appliances ill dwellings

21 Smart Grid

The concept of smart grid appeared as an answer to the new power system chalshylenges Better control systems which include advanced protection functions (9) new measurement systems improved communication and modern technology have to be proposed Smart grid integrates the use of sensors communications computational ability and control in order to enhance the overall functionali ty of the electric power system (10) Smart grid initiatives seek to improve operations maintenance and planning using modern technology in order to better manage energy use and costs [1 1] Many governments sustain modem networks in the global context of energy saving and environment issues United States Department of Energy have defined the functions required for smart grids in (12) the ability to heal itself to motivate consumers to actively participate in operations of the grid to resist attack to provide higher power quality to accommodate a1l generation and storage options to enable electricity markets to flourish to manage more effishyciently the assets and costs Figure 1 depicts the important components of the smart grid (13)

Fig 1 The smart grid components

ConSidering the fact that energy resources are limited there are many policies for energy saving on European and world scale A good method to lilnit or change the energy consumption habits is the energy price The energy market is a powershyful instrument that sets the prices between the energy producers and energy supshypliers and consumers (CIC4) figure 2 It has an important role in the power system nowadays but it is a complex bidding rules mechanism which sometimes is difficult to follow The energy market is divided into different categories but

254 255 N Arghira et al

the Day Ahead Market or Spot Market is of great interest This type of energy marshyket in volves bidding the energy consumption of the next day It is a very complex mechanism which requires a very good knowledge of the demand for the power suppliers The participation in the day ahead energy market imposes a dynamic energy management (hour by hour changes in the energy productionconsumption ratio)

INpoundRG Y MARKET

F ig 2 Energy market in the power system

22 Smart Home

Smart homes are buildings equipped with a home automation system (HAS) able to optimize the energy management in a dwelling A home automation system basically consists of household appliances linked via a communication network alshylowing interactions for control purposes Thanks to this network a load manageshyment mechanism can be carried out taking into account the users preferences This system consists of a set of appliances fitted with micro-controllers able to communicate two-way via standard protocols

The home automation system should be able to take the best decisions in order to meet the energy consumption needs of the consumers in an economical manner Energy management problem can be formulated as scheduling problem where energy is considered as a resource shared by appliances and device energy deshymands considered as tasks [3] presents a three-layer household energy control system capable both to satisfy the maximum available electrical energy constraint and to maximize user satisfaction criteria The proposed architecture contains an equipment layer a protection layer and an anticipation layer The equipment layer composed by existing control systems is responsible of adjusting equipment conshytrols in order to reach given set points in spite of perturbations The protection layer is responsible of decision making in case of violation of predefined constraints dealing either with energy or with comfort The anticipation layer is responsible of managing predicted events dealing with electric sources or with electric loads in order to avoid the use of protection layer

Forecasting Energy Consumption in Dwellings

~- coo tJU)J

F ig 3 Architecture of a power manager

23 Challenges for Energy Providers - Importance of Energy Prediction

Energy providersretailers have the difficult task of dealing directly with the endshyusers and the electricity market They have to take decisions in real time for a reli shyable and profitable operation of the grid (no congestions or load shedding) One metllod of getting the consumers controlled at all times is by changing the tariffs during the day Figure 4 shows the interactions between the power retailers consumcrs - smart homes and the energy market in the spirit of cost control

POWER REfAILER IEne~~~1

1 Estimate ampicrgy Price

I 22 Computed ~ n(rgy consumption I 221 FSlimate gJobal energy demand

~ 21 Adjust consumption to get ~ the best compromise picecomforl

I 222 lluy enfrgy (Quantity 11me period)

222 1 Proides t nerg) (Quan ti ly F)e 1lY price)

3 Irorm (l1me period F)ergy prices)

Fig 4 1nteractions power retailer - smart home - energy market

257 256

Considering the actions between the actors of the energy market at the supply level it is obvious the importance of load prediction in the smart If dwellings are equipped wi th home automation systems capable of decisions the estimation of energy demand will be easier The prediction of the HAS must give accurate forecast for the appliances in the house in order build a reliable model and estimation on a larger scale

3 Load Forecasting

Load forecasting occupies a central position in terms of planning and operation electric utilities Load forecas ts are extremely important for energy Transport System Operators (TSOs) financial institutions and other paruCIPaDl in electric energy generation transmission distribution and markets 13 This tion presents a classification for load forecasting and the challenges of prediction for electrical appliances in dwellings

31 Classification of lAJad Forecasting

When considering the time period of forecast a classification into three can be done short-term forecasts which are usually from one hour to one medium forecasts which are usually from one day to a year and long-term casts which are lo nger than a year The forecasts for different time horizons important for different operations within a utility company

A review and categorization of electric load forecasting techniques is in [14-15J There are explained several methods for energy forecasting but of them concerns a single appliance in a home This paper tries to find a predict energy consumption for each device in a dwelling

32 Issues Concerning Energy Forecast in Dwellings

Predicting the energy consumption in tbe housing sector is a difficult task it vaJi es a lot depending on season weather conditions and user behaviour context of the home automation system the forecast for each electrical eq has to be done

The energy consumption of electrical devices varies a lot during a year or smaller periods of time It is difficult to find similarities in terms of between intervals of time In order to find patterns to help with predicting consumption the Euclidian distances are computed between each 2 days evaluated period data Several equipments in houses were tested but shows the differences for the freezer in house 2000949 Some knowledge has to be produced for a proper forecast

Forecasting Energy Consumption in Dwellings

t- -_u _ t m

Fig 5 Euclidian Distances between energy consumption values - freezer house 2000949

Forecasting Energy Consumption of Services in Dwellings

seen in previous section predicting the exact value of consumed energy is difshyso the aim of this paper is predicting whether one service will consume enshy

or not during each hour of the next 24 hours The predictor performance is based on recorded data which concern the energy

mption of appliances in 100 households in France during a full year The database comes from Residential Monitoring to Decrease Energy Use and

Emissions in Europe (REMODECE) which is a European database on pSidential consumption This database stores the characterization of residential

consumption by end-user and by country The information for each is recorded each 10 minutes and concerns the energy consumption for the

~ppliances and also the weather conditions (temperature wind strength wind flilection humidity)

Computing the Prediction Precision

designing a predictor it is important to set up a method for assessing the of a predictor because it clarifies the objectives For computing the

of a predictor test data are first to be considered Because of the predicshyrequired by an anticipative energy management system tbe test data are the

energy consumption for an appliance over a full year Figure 6 shows a set test data

258 259 N Arghira et aJ

Total sJte non-halogen Mghting ennrgy coneunpfo-n 20009G5 in hours

~ ~

-8 I ~

i 100 I III

i 1 11 111111111 illl]IIIII11 Ii

middot~OO~I ~ ~1h111ulII~__y jll~dlll Illi I((I HDI 1~ 100 he tisklry ihour]

Fig 6 Historical data for the energy consumption - non-halogen lighting in house 2000905

In order to evaluate the performance of predictors some concepts have to be defined

Let h be the current hour and e(h) be a binary value which is equal to 0 if the considered appliance is actually consuming energy during the hour hand 1 othershywise Let PaCh) be a prediction provided by the predictor a which is equal to 1 if the considered appliance is predicted as consuming power during the hour hand 0 otherwise The precision of the predictor is then expressed by

24 (25-i)e(h+i)- p (hh+24+i) J[ (h) - ( 1)za - =1 275

Any predictor a relies on an historical sliding time window of n hours used to preshyh n h

dict the d+ 1 predictions It can be denoted a - bull The number n has to be adshyjusted because if it is too large seasonal phenomena may disappear and if it is too short data set will not be sufficient to yield a precise prediction

The proposed algorithm for assessing a predictor a involves the fo llowing steps

1 Set the time window dimension to n hours within the period for which the historical data was registered where n goes from 24 to 36424

2 Compute the predictions for the data corresponding to the historical sliding time window

3 Compute the predictor precision n(h) based on the ne lCt day data for all possible hours h and compute an average precision for the predictor

42 Prediction with Basic Predictors

Since the informati on regarding thc energy consumption is very dependent on user behavior a stochastic approach will be tried Two trivial predictors are tested one

Forecasting Energy Consumption in Dwellings

that considers that the service will consume all the time in the future and one which considers the service will never consume

421 The Wiu Always Consume Predictor

This type of predictor involves considering that the appliance will consume energy permanently The prediction is computed based on a set of test data and refers to the probabili ty of the service to consume energy The prediction pihh+24) is elCshypressed

Pa (hh+24) = 1 h =12 24 (2)

Figure 7 shows the prediction precision Jra(h) for each time window of the test

data considered in days (the prediction precision for a sliding window of 1 day 2 days etc) This curve was obtained using the previously presented algorithm for assessing the predictor

422 The Will Never Consume Predictor

Thi s predictor assumes the service will not consume at all in the nelCt day The prediction is computed based on a set of test data and refers to the probability of the service not to consume) The prediction p(hh+24) is computed

Po Ch h+ 24) = 0 h = 12 24 (3)

It can be denoted that the value of precision for this predictor is the complement of the will always consume predictor precision Figure 8 shows the prediction preshycision for cach time window of the test data considered in days for this predictor Bcst precision is reached for a historical interval of aprolCimatelyl OO days

r r ~i ____ ~ r

Fig 7 Prediction precision assuming the Fig 8 Prediction probability assuming the service will consume continuously service will never consume

43 The Proposed Predictor

An inhabitant in the house interacts with various electrical devices as part of his routine activities Thus energy consumption can be modeled as a stochastic process

261 260 N Arghira et aI

In this context the proposed predictor specifies the probability of the appliance to consume on an hourly base We consider the following prediction form ula

n)Ch h+24) po(hh+24) gt PJ h=1224n(h)

(4)P (hh+24)= n (h h+24) P (h h+24) $ Po

(1 1- I bull a t

nth)1 Where n(h) is the considered number of hours h in the test period ndhh+24) is the number of limes the service did consume during hour h of the historical data and Pa is a set threshold Figure 9 shows the prediction precision of the proposed predictor related to the basic predictors in the previous subsections

E---middot~middot-7middotmiddotmiddotmiddotq I I ----r

bull _-- _ ~_____~_ _ ~-a- _ __________ ~~~ _I

- - ~ ---shyI I I I-==~It ---------~ - ~ ~ -- ~~~I

I I I I I 1

Imiddot -- - 1- - - ~ - - ~ - - - - - - - - -~ t I I I

_~ _ __ ~ ___ L __ _ _ _ _ h _ _~ ~

I I

-middot-middot-middot middot~ -middot-middot1middotmiddotI middot middot middot --middot middot middott middot middot middot -middotmiddot-middot~middot-middot-middot- middotmiddotmiddotmiddot middot I~ ~

Fig 9 Prediction precision using the proposed predictor

44 Enhancing the Forecast Precision

In order to increase the precision some similarities between data are considered and clustering methods are applied

441 Segmentation of Data

While mining the available data some pattern of recurrence is searched in order to immiddot prove the prediction A temporal segmentation can be used to introduce knowledge in the predictor for instance the use of the oven may be different for rainy Saturdays The segmentation of data can be made considering different aspects such as the seashyson month period of the day (daynight) type of day (weekday weekend) The obshyjective of this operation is to reduce the average dispersion in order to improve the prediction After the segmentation is done we will merge the segments thaI are similar using a clustering algorithm in order to gather the non-meaningful segments

temporal segmentation that considers each day of the week as a partition was done For each segment the hourly predictions are made considering the proshyposed predictor A k-Means clustering algorithm is applied in order to group the similar consumption days

Forecasting Energy Consumption in Dwellings

The KmiddotMeans algorithm assumes a fixed number of clusters specified in adshyvance Each cluster is defined by its cluster center and clustering proceeds by asshysigning each of the input data to the cluster with the closest centre The grouping is done by minimizing the sum of squares of distances between data and the corresponding cluster centroid This algorithm is based on the euclidean distance

ED(X n = JI (x - y -1 (5)

Where X Y are vectors n is the vector length and XiYi arc their components The center of each cluster is then re-estimated as the centroid of the points

assigned to it The process is then iterated until a convergence criterion is accomplished ED is a set threshold

ED(Xy) 5 ED (6)

442 The Prediction Precision after Clustering

After applying the iterative kmiddotMeans algorithm two clusters are obtained In the presented case cluster C) groups week days data and cluster C

2 gathers Saturday

and Sunday data After the clusters are obtained the initial data corresponding to the energy consumption is divided into 2 sets according to the number of clusters and the considered segments The prediction precision is computed for the proshyposed predictor for each of the clusters Figure 10 a and b show the prediction precision for the new test data The blue curve represents the precision for the proposed predictor and the other two are the curves for the basic predictors

Figures 9 and 10 a b present the prediction precision in 3 situations for initial energy consumption data for merged segments in cluster CI da ta and for merged segments in cluster C2 data When comparing the obtained curves for the proshyposed predictor the precision of the predictor increases after the clustering is done This proves that the segmentation of data and then the merging of similar partitions is a good method for increasing the prediction performance

iii PredlcWl Pr-JCiMon 1V 200C905 C1 Ptedc1i0l PrecSloln lV 2tJOO91)$ C2

r

-- ~----- - - ~ -- - ~--~ [3~ _ _ ___ _ --~_ c_

i=~ =~ == = ~ ~middotmiddot-jI - shy

Imiddot 1~~t~~ ~middot ~[T~middot~middotT-1- ---1- -

Jr bull _ ~ L __1_ _ I _ _ _ L _ _ bull L _ -=-~ 1bullbull bull 7 bullbull-_ -bullbull ~ _- I-~ -

middotmiddotmiddotmiddotmiddotmiddotmiddotmiddot middotmiddot IlI middot-bullbull7 (bullbullbull7bull71bullbull-bullbull- _ bullbull o- bull -- noshy

~-

a CllISler CI b CiUSler C2

Fig 10 Prediction precision comparison for the three predictors - clustered data

263 262 N Arghtra et al

5 Experiments and Discussion

This section presents the results for different electrical appliances in the house when the proposed predictor is used in comparison with the basic predictors will never consume and will consume continuousl y There are several services which were tested but this paper will show the results for appliances representashytive for their class the fridge as it consumes all the time the boiler as its consumption can be delayed and the lighting as it has a regular usage in a house

The prediction precision is computed as explained in subsection 41 for a slid shying time window between I and 364 days covering all the historical data avai lashyble The tests show that the prediction acts in a special manner depending on the type of the electrical appliance (16)

The prediction precision with the proposed predictor for the refrigerator (fig 13) is lower than the precision assuming permanent consumption for time winshydows higher than 2 days This implies that the prediction for the fridge should be done considering a short period of hi storical data (e g two weeks) in order to get a high precision This conclusion was expected since the energy consumption for this appliance is dependent on the season so a short period of time is significant for prediction

The pcrfonnance of prediction for the overall lighting consumption (figures 11) is hi gher than the performance of the basic predictors for the entire recorded data intervaL For the gas boiler (figure 12) a good precision is obtained for almost all the considered periods of historical data

As a general observation the maximum value for prediction accuracy is obshytained for a short period of historical data - almost in all studied cases the best precision was reached for one day of recorded data Using this result will decrease the computation time since the learning time is short

PoodidiM p Gas Solelt 2000949 011 Pr_ Prod_ 2OOOl 0 Tota si IggtIOIO II lt1gt Ii ~

~~--=- - - t- - - - - - - - - t shy

r r==~~bull J _ _ _ 1_ _ _ _1 __ bull J

bull __1 _ _ _ L __ __

1 ___ w

_ _ _ _ J _ 1__ _ __ _ _ - -1 shyi

~

~__ j _ __ _ _ J _ _ _ I _ _ _

Uj bull - ~ -~ ~~----~--- ~--- -~=~~ -- - shybull __ _ ~~ r __ _ _ bull - _ _ _ 1 _ _ _

j _ _

~I I ~ I J _ _-t~~~~-r-~~~middot~ ~+~~-~~~~~~~~~~ I 1

---- 1 ---- ~ --_~- _ 1IIfIot

-1 middotmiddot(1111

Fig 11 Prediction precision of the boiler Fig 12 Prediction precision of lighting con-elec tricity consumption in house 2000949 sumption in house 2000910

Forecasting Energy Consumption in Dwellings

Fridgil catwmptlon 2000949 houtty probablibM i f I I

middot -1 - _ L __ J _~_ middot _ _ J _ _ L __ ~I

I

E 1I1 - ~ - - -===~~-~~Iimiddot-- -- ~ ~

I

I I I I I It I I I I I bull I - - - - -r - - 1- -- r- - - - r --

I bull I I I

- - --- -- -- ~ - - - ~--~- - -f- - ~

I

==-=---- -----~-~~--- -~

Fig 13 Prediction precision of the refrigerator consumption in house 2000949

6 Conclusions

Predicting the energy consumption in dwellings is an essential part in the power management of the grid as the consumption in the residential sector represents a siguificant percentage in the total electricity demand The development of the smart grid is not possible without a good prediction of energy consumption The trend nowadays is to get the prediction of energy consumption not only at house level but at household appliance leveL

The prediction of energy consumption in housing is very dependent on inhabishytants behavior so a stochastic method for prediction has been presented in this paper The paper discusses about how to evaluate the precision of a predictor in the day +1 power management context Different basic predictors are presented and tested for the available historical data A relevant predictor is presented

Segmentation of data is done considering the patterns in energy consumption Also the historical data is divided according to the results of the k-means clustershying algorithm After testing the predictor on the new clustered data the precision of the predictor improves

Further work involves testing the proposed predictor for all the appliances in a house in order to decide the proper way for prediction at the equipment level

References

I Brooks 1 Lu E Reicher D Spirakis C Weihl B Demand Dispatch IEEE Powshyer and Energy Magazine 8(3) 20-29 (20 10) ISSN 1540-7977

2 Iliescu SS Fagarasan I Modem approaches in power system control In IEEE Int Conf on Automation Quali ty and testing Robotics Cluj voL I pp 41 -44 (2008)

3 Long Ha D Ploix S Zamai E Jacomino M Realtimes dynamic optimization for demand-side load management International Journal of Management Science and Engineering Management 3(4) 243-252 (2008) ISSN 1750-9653

264 N Arghira et al

4 Abras S Ploix S Pesty S Jacomino M A multi-agent design for a home automashytion system dedicated to power management In lF1P Conference on Artificial Intellishy

gence Applications and Innovations Greece (2007) 5 Jain A Salish B Clustering based Short Term Load Forecasting using Support Vec shy

tor Machines In IEEE PowerTech Bucharest (2009) 6 Jigoria-Oprea D Kilyeni S Dan F Electric energy forecast for residential users

Journal of Sustainable Energy TI(2) (2011) 7 Hawarah L Ploi S Jacomino M User Behavior Prediction in Energy Consumpshy

tion in Housing Using Bayesian Networks vol 113 pp 372-379 (2010) http www springerLink comdoil0loo7978-3-642-13208-7 _47

8 Basu K Guillame-Berty M Joumaa H Ploix S Crowley J Predicting home sershyvice demands from appliance usage data In International Conference on Information and Communication Technologies and Applications lCTA 201 1 USA (2011)

9 Popescu V Oprea L Costianu DR Transmission Network Capacity Enhancement by Special Protection Schemes In 18th Int ConL Control Systems and Computer Science Bucharest vol I pp 205-210 (2011) ISSN 2066-4451

10 GeWngs CW The smart grid enabling energy efficiency and demand response USA

(2009) ISBN 0-88 173-624-4 II Ipakchi A Al buych F Grid of the future IEEE Power and Energy Magazine 8(3)

20-29 (2009) doilOl I09MPE2008931384 ISSN 1540-777 12 National Energy Technology Laboratory A Vis ion for the M odem Grid (2007) 13 Arghira N Hossu D F1Ig~an I lliescu S S Costianu DR Modern SCADA

philosophy in power system operation - A survey Scientific Bulletin of University POLITEHNJCA Buchares t Series C Electrical Engineering 73(2) 153- 166 (2011)

14 Feinberg EA Genethliou D Load Forecasting Applied mathematics for restrucshy

tured electric power systems pp 269-285 (2005) http www Spr i ngerLinkcomdoi 1010070-387-23471-3

15 AI fares KH Nazeeruddin M Electric load forecasting literature survey and classishyfication of methods In ternational Journal of Systems Science 33( 1)23-34 (2002)

16 Arghira N Ploi S Fagar3ltjan I Iliescu SS Prediction of energy consumption in homes In 18th Int Conf Control Systems and Computer Science Bucharest vol 1 pp 211-217 (2011 ) ISSN 2066-4451

Modelling and Composed Recursive Model Free Control for the Anaerobic Digestion Process

2Haoping W an g l B oyko Kachev bull Yang Tian l

2 3Ivan Simeonov and Nicolai Christov

1 Automation School Nanjing University of Science and Technology 200 Xiao Ling Wei St 210094 Nanjing China

2 The StephanAngeloff Institute of Microbiology - Bulgarian Academy of Sciences bl 26 Acad G Bonchev St 1113 Sofia Bulgaria

3 LAGIS UMR eNRS 8219 Universite Lille1 Sciences et Technologies 59655 Villeneuve d Ascq France hpwangnjusteducn kalchevmicrobiobasbg

Abstract This paper preseots a modelling and new composite recursive model free controller for trajectory tracking and disturbance compensation for the Anaerobic Digestion Process of cattle dung The used model is on the basis of a fifth-order continuous anaerobic digestion model And the proposed controller comprises a recursive model free controller based stabilization component and a time delay control based compensation component with recursi ve calculation structure which does not require any knowledge o f the model parameters Computer simulation examples illustrate the performance and robustness of the proposed approach

Keywords Anaerobic digestion composed recursive controller piecewise continuous systems recursive model free controller

1 Introduction

EnvirolUnental problems (ex air and water pollution) and energy shortage are nowadays recognized worldwide issues Several possibilities are available to treat these difficulties from nowadays different treatment ways biological processes are surely among the most used sustainable and efficient systems Anaerobic digestion (AD) which is a biotechnological process is widely used in life sciences wastewater treatment and a promising method for solving energy shortage and ecological protection problems in agriculture and agro-industry [1 ] In such kind of processes usually camed out in Continuously Stirred Tank bio-Reactors (CSTR) the organic matter is de-polluted by microorganisms into biogas (mainly methane ClL and carbon dioxide CO2) and compost in the absence of oxygen [2] [3]

AD is a very unstable process with regard to the bioreactor operation This is due to the complicated interactions between different microbial species as well as to the

1 Dumltrache (Ed ) Adv in lntelligent Control Systems amp Computer Science AlSC 187 pp 265-278 springcrlinkcom copy Springer-Verlag Berlin Heidelberg 2013

Page 3: Forecasting Energy Consumption in Dwellings - Indesenindesen.ats.com.ro/rezultate/articole/upb/2013_scan_springer_book.pdf · Finite Element Method Magnetics : Documentation: FEMM

254 255 N Arghira et al

the Day Ahead Market or Spot Market is of great interest This type of energy marshyket in volves bidding the energy consumption of the next day It is a very complex mechanism which requires a very good knowledge of the demand for the power suppliers The participation in the day ahead energy market imposes a dynamic energy management (hour by hour changes in the energy productionconsumption ratio)

INpoundRG Y MARKET

F ig 2 Energy market in the power system

22 Smart Home

Smart homes are buildings equipped with a home automation system (HAS) able to optimize the energy management in a dwelling A home automation system basically consists of household appliances linked via a communication network alshylowing interactions for control purposes Thanks to this network a load manageshyment mechanism can be carried out taking into account the users preferences This system consists of a set of appliances fitted with micro-controllers able to communicate two-way via standard protocols

The home automation system should be able to take the best decisions in order to meet the energy consumption needs of the consumers in an economical manner Energy management problem can be formulated as scheduling problem where energy is considered as a resource shared by appliances and device energy deshymands considered as tasks [3] presents a three-layer household energy control system capable both to satisfy the maximum available electrical energy constraint and to maximize user satisfaction criteria The proposed architecture contains an equipment layer a protection layer and an anticipation layer The equipment layer composed by existing control systems is responsible of adjusting equipment conshytrols in order to reach given set points in spite of perturbations The protection layer is responsible of decision making in case of violation of predefined constraints dealing either with energy or with comfort The anticipation layer is responsible of managing predicted events dealing with electric sources or with electric loads in order to avoid the use of protection layer

Forecasting Energy Consumption in Dwellings

~- coo tJU)J

F ig 3 Architecture of a power manager

23 Challenges for Energy Providers - Importance of Energy Prediction

Energy providersretailers have the difficult task of dealing directly with the endshyusers and the electricity market They have to take decisions in real time for a reli shyable and profitable operation of the grid (no congestions or load shedding) One metllod of getting the consumers controlled at all times is by changing the tariffs during the day Figure 4 shows the interactions between the power retailers consumcrs - smart homes and the energy market in the spirit of cost control

POWER REfAILER IEne~~~1

1 Estimate ampicrgy Price

I 22 Computed ~ n(rgy consumption I 221 FSlimate gJobal energy demand

~ 21 Adjust consumption to get ~ the best compromise picecomforl

I 222 lluy enfrgy (Quantity 11me period)

222 1 Proides t nerg) (Quan ti ly F)e 1lY price)

3 Irorm (l1me period F)ergy prices)

Fig 4 1nteractions power retailer - smart home - energy market

257 256

Considering the actions between the actors of the energy market at the supply level it is obvious the importance of load prediction in the smart If dwellings are equipped wi th home automation systems capable of decisions the estimation of energy demand will be easier The prediction of the HAS must give accurate forecast for the appliances in the house in order build a reliable model and estimation on a larger scale

3 Load Forecasting

Load forecasting occupies a central position in terms of planning and operation electric utilities Load forecas ts are extremely important for energy Transport System Operators (TSOs) financial institutions and other paruCIPaDl in electric energy generation transmission distribution and markets 13 This tion presents a classification for load forecasting and the challenges of prediction for electrical appliances in dwellings

31 Classification of lAJad Forecasting

When considering the time period of forecast a classification into three can be done short-term forecasts which are usually from one hour to one medium forecasts which are usually from one day to a year and long-term casts which are lo nger than a year The forecasts for different time horizons important for different operations within a utility company

A review and categorization of electric load forecasting techniques is in [14-15J There are explained several methods for energy forecasting but of them concerns a single appliance in a home This paper tries to find a predict energy consumption for each device in a dwelling

32 Issues Concerning Energy Forecast in Dwellings

Predicting the energy consumption in tbe housing sector is a difficult task it vaJi es a lot depending on season weather conditions and user behaviour context of the home automation system the forecast for each electrical eq has to be done

The energy consumption of electrical devices varies a lot during a year or smaller periods of time It is difficult to find similarities in terms of between intervals of time In order to find patterns to help with predicting consumption the Euclidian distances are computed between each 2 days evaluated period data Several equipments in houses were tested but shows the differences for the freezer in house 2000949 Some knowledge has to be produced for a proper forecast

Forecasting Energy Consumption in Dwellings

t- -_u _ t m

Fig 5 Euclidian Distances between energy consumption values - freezer house 2000949

Forecasting Energy Consumption of Services in Dwellings

seen in previous section predicting the exact value of consumed energy is difshyso the aim of this paper is predicting whether one service will consume enshy

or not during each hour of the next 24 hours The predictor performance is based on recorded data which concern the energy

mption of appliances in 100 households in France during a full year The database comes from Residential Monitoring to Decrease Energy Use and

Emissions in Europe (REMODECE) which is a European database on pSidential consumption This database stores the characterization of residential

consumption by end-user and by country The information for each is recorded each 10 minutes and concerns the energy consumption for the

~ppliances and also the weather conditions (temperature wind strength wind flilection humidity)

Computing the Prediction Precision

designing a predictor it is important to set up a method for assessing the of a predictor because it clarifies the objectives For computing the

of a predictor test data are first to be considered Because of the predicshyrequired by an anticipative energy management system tbe test data are the

energy consumption for an appliance over a full year Figure 6 shows a set test data

258 259 N Arghira et aJ

Total sJte non-halogen Mghting ennrgy coneunpfo-n 20009G5 in hours

~ ~

-8 I ~

i 100 I III

i 1 11 111111111 illl]IIIII11 Ii

middot~OO~I ~ ~1h111ulII~__y jll~dlll Illi I((I HDI 1~ 100 he tisklry ihour]

Fig 6 Historical data for the energy consumption - non-halogen lighting in house 2000905

In order to evaluate the performance of predictors some concepts have to be defined

Let h be the current hour and e(h) be a binary value which is equal to 0 if the considered appliance is actually consuming energy during the hour hand 1 othershywise Let PaCh) be a prediction provided by the predictor a which is equal to 1 if the considered appliance is predicted as consuming power during the hour hand 0 otherwise The precision of the predictor is then expressed by

24 (25-i)e(h+i)- p (hh+24+i) J[ (h) - ( 1)za - =1 275

Any predictor a relies on an historical sliding time window of n hours used to preshyh n h

dict the d+ 1 predictions It can be denoted a - bull The number n has to be adshyjusted because if it is too large seasonal phenomena may disappear and if it is too short data set will not be sufficient to yield a precise prediction

The proposed algorithm for assessing a predictor a involves the fo llowing steps

1 Set the time window dimension to n hours within the period for which the historical data was registered where n goes from 24 to 36424

2 Compute the predictions for the data corresponding to the historical sliding time window

3 Compute the predictor precision n(h) based on the ne lCt day data for all possible hours h and compute an average precision for the predictor

42 Prediction with Basic Predictors

Since the informati on regarding thc energy consumption is very dependent on user behavior a stochastic approach will be tried Two trivial predictors are tested one

Forecasting Energy Consumption in Dwellings

that considers that the service will consume all the time in the future and one which considers the service will never consume

421 The Wiu Always Consume Predictor

This type of predictor involves considering that the appliance will consume energy permanently The prediction is computed based on a set of test data and refers to the probabili ty of the service to consume energy The prediction pihh+24) is elCshypressed

Pa (hh+24) = 1 h =12 24 (2)

Figure 7 shows the prediction precision Jra(h) for each time window of the test

data considered in days (the prediction precision for a sliding window of 1 day 2 days etc) This curve was obtained using the previously presented algorithm for assessing the predictor

422 The Will Never Consume Predictor

Thi s predictor assumes the service will not consume at all in the nelCt day The prediction is computed based on a set of test data and refers to the probability of the service not to consume) The prediction p(hh+24) is computed

Po Ch h+ 24) = 0 h = 12 24 (3)

It can be denoted that the value of precision for this predictor is the complement of the will always consume predictor precision Figure 8 shows the prediction preshycision for cach time window of the test data considered in days for this predictor Bcst precision is reached for a historical interval of aprolCimatelyl OO days

r r ~i ____ ~ r

Fig 7 Prediction precision assuming the Fig 8 Prediction probability assuming the service will consume continuously service will never consume

43 The Proposed Predictor

An inhabitant in the house interacts with various electrical devices as part of his routine activities Thus energy consumption can be modeled as a stochastic process

261 260 N Arghira et aI

In this context the proposed predictor specifies the probability of the appliance to consume on an hourly base We consider the following prediction form ula

n)Ch h+24) po(hh+24) gt PJ h=1224n(h)

(4)P (hh+24)= n (h h+24) P (h h+24) $ Po

(1 1- I bull a t

nth)1 Where n(h) is the considered number of hours h in the test period ndhh+24) is the number of limes the service did consume during hour h of the historical data and Pa is a set threshold Figure 9 shows the prediction precision of the proposed predictor related to the basic predictors in the previous subsections

E---middot~middot-7middotmiddotmiddotmiddotq I I ----r

bull _-- _ ~_____~_ _ ~-a- _ __________ ~~~ _I

- - ~ ---shyI I I I-==~It ---------~ - ~ ~ -- ~~~I

I I I I I 1

Imiddot -- - 1- - - ~ - - ~ - - - - - - - - -~ t I I I

_~ _ __ ~ ___ L __ _ _ _ _ h _ _~ ~

I I

-middot-middot-middot middot~ -middot-middot1middotmiddotI middot middot middot --middot middot middott middot middot middot -middotmiddot-middot~middot-middot-middot- middotmiddotmiddotmiddot middot I~ ~

Fig 9 Prediction precision using the proposed predictor

44 Enhancing the Forecast Precision

In order to increase the precision some similarities between data are considered and clustering methods are applied

441 Segmentation of Data

While mining the available data some pattern of recurrence is searched in order to immiddot prove the prediction A temporal segmentation can be used to introduce knowledge in the predictor for instance the use of the oven may be different for rainy Saturdays The segmentation of data can be made considering different aspects such as the seashyson month period of the day (daynight) type of day (weekday weekend) The obshyjective of this operation is to reduce the average dispersion in order to improve the prediction After the segmentation is done we will merge the segments thaI are similar using a clustering algorithm in order to gather the non-meaningful segments

temporal segmentation that considers each day of the week as a partition was done For each segment the hourly predictions are made considering the proshyposed predictor A k-Means clustering algorithm is applied in order to group the similar consumption days

Forecasting Energy Consumption in Dwellings

The KmiddotMeans algorithm assumes a fixed number of clusters specified in adshyvance Each cluster is defined by its cluster center and clustering proceeds by asshysigning each of the input data to the cluster with the closest centre The grouping is done by minimizing the sum of squares of distances between data and the corresponding cluster centroid This algorithm is based on the euclidean distance

ED(X n = JI (x - y -1 (5)

Where X Y are vectors n is the vector length and XiYi arc their components The center of each cluster is then re-estimated as the centroid of the points

assigned to it The process is then iterated until a convergence criterion is accomplished ED is a set threshold

ED(Xy) 5 ED (6)

442 The Prediction Precision after Clustering

After applying the iterative kmiddotMeans algorithm two clusters are obtained In the presented case cluster C) groups week days data and cluster C

2 gathers Saturday

and Sunday data After the clusters are obtained the initial data corresponding to the energy consumption is divided into 2 sets according to the number of clusters and the considered segments The prediction precision is computed for the proshyposed predictor for each of the clusters Figure 10 a and b show the prediction precision for the new test data The blue curve represents the precision for the proposed predictor and the other two are the curves for the basic predictors

Figures 9 and 10 a b present the prediction precision in 3 situations for initial energy consumption data for merged segments in cluster CI da ta and for merged segments in cluster C2 data When comparing the obtained curves for the proshyposed predictor the precision of the predictor increases after the clustering is done This proves that the segmentation of data and then the merging of similar partitions is a good method for increasing the prediction performance

iii PredlcWl Pr-JCiMon 1V 200C905 C1 Ptedc1i0l PrecSloln lV 2tJOO91)$ C2

r

-- ~----- - - ~ -- - ~--~ [3~ _ _ ___ _ --~_ c_

i=~ =~ == = ~ ~middotmiddot-jI - shy

Imiddot 1~~t~~ ~middot ~[T~middot~middotT-1- ---1- -

Jr bull _ ~ L __1_ _ I _ _ _ L _ _ bull L _ -=-~ 1bullbull bull 7 bullbull-_ -bullbull ~ _- I-~ -

middotmiddotmiddotmiddotmiddotmiddotmiddotmiddot middotmiddot IlI middot-bullbull7 (bullbullbull7bull71bullbull-bullbull- _ bullbull o- bull -- noshy

~-

a CllISler CI b CiUSler C2

Fig 10 Prediction precision comparison for the three predictors - clustered data

263 262 N Arghtra et al

5 Experiments and Discussion

This section presents the results for different electrical appliances in the house when the proposed predictor is used in comparison with the basic predictors will never consume and will consume continuousl y There are several services which were tested but this paper will show the results for appliances representashytive for their class the fridge as it consumes all the time the boiler as its consumption can be delayed and the lighting as it has a regular usage in a house

The prediction precision is computed as explained in subsection 41 for a slid shying time window between I and 364 days covering all the historical data avai lashyble The tests show that the prediction acts in a special manner depending on the type of the electrical appliance (16)

The prediction precision with the proposed predictor for the refrigerator (fig 13) is lower than the precision assuming permanent consumption for time winshydows higher than 2 days This implies that the prediction for the fridge should be done considering a short period of hi storical data (e g two weeks) in order to get a high precision This conclusion was expected since the energy consumption for this appliance is dependent on the season so a short period of time is significant for prediction

The pcrfonnance of prediction for the overall lighting consumption (figures 11) is hi gher than the performance of the basic predictors for the entire recorded data intervaL For the gas boiler (figure 12) a good precision is obtained for almost all the considered periods of historical data

As a general observation the maximum value for prediction accuracy is obshytained for a short period of historical data - almost in all studied cases the best precision was reached for one day of recorded data Using this result will decrease the computation time since the learning time is short

PoodidiM p Gas Solelt 2000949 011 Pr_ Prod_ 2OOOl 0 Tota si IggtIOIO II lt1gt Ii ~

~~--=- - - t- - - - - - - - - t shy

r r==~~bull J _ _ _ 1_ _ _ _1 __ bull J

bull __1 _ _ _ L __ __

1 ___ w

_ _ _ _ J _ 1__ _ __ _ _ - -1 shyi

~

~__ j _ __ _ _ J _ _ _ I _ _ _

Uj bull - ~ -~ ~~----~--- ~--- -~=~~ -- - shybull __ _ ~~ r __ _ _ bull - _ _ _ 1 _ _ _

j _ _

~I I ~ I J _ _-t~~~~-r-~~~middot~ ~+~~-~~~~~~~~~~ I 1

---- 1 ---- ~ --_~- _ 1IIfIot

-1 middotmiddot(1111

Fig 11 Prediction precision of the boiler Fig 12 Prediction precision of lighting con-elec tricity consumption in house 2000949 sumption in house 2000910

Forecasting Energy Consumption in Dwellings

Fridgil catwmptlon 2000949 houtty probablibM i f I I

middot -1 - _ L __ J _~_ middot _ _ J _ _ L __ ~I

I

E 1I1 - ~ - - -===~~-~~Iimiddot-- -- ~ ~

I

I I I I I It I I I I I bull I - - - - -r - - 1- -- r- - - - r --

I bull I I I

- - --- -- -- ~ - - - ~--~- - -f- - ~

I

==-=---- -----~-~~--- -~

Fig 13 Prediction precision of the refrigerator consumption in house 2000949

6 Conclusions

Predicting the energy consumption in dwellings is an essential part in the power management of the grid as the consumption in the residential sector represents a siguificant percentage in the total electricity demand The development of the smart grid is not possible without a good prediction of energy consumption The trend nowadays is to get the prediction of energy consumption not only at house level but at household appliance leveL

The prediction of energy consumption in housing is very dependent on inhabishytants behavior so a stochastic method for prediction has been presented in this paper The paper discusses about how to evaluate the precision of a predictor in the day +1 power management context Different basic predictors are presented and tested for the available historical data A relevant predictor is presented

Segmentation of data is done considering the patterns in energy consumption Also the historical data is divided according to the results of the k-means clustershying algorithm After testing the predictor on the new clustered data the precision of the predictor improves

Further work involves testing the proposed predictor for all the appliances in a house in order to decide the proper way for prediction at the equipment level

References

I Brooks 1 Lu E Reicher D Spirakis C Weihl B Demand Dispatch IEEE Powshyer and Energy Magazine 8(3) 20-29 (20 10) ISSN 1540-7977

2 Iliescu SS Fagarasan I Modem approaches in power system control In IEEE Int Conf on Automation Quali ty and testing Robotics Cluj voL I pp 41 -44 (2008)

3 Long Ha D Ploix S Zamai E Jacomino M Realtimes dynamic optimization for demand-side load management International Journal of Management Science and Engineering Management 3(4) 243-252 (2008) ISSN 1750-9653

264 N Arghira et al

4 Abras S Ploix S Pesty S Jacomino M A multi-agent design for a home automashytion system dedicated to power management In lF1P Conference on Artificial Intellishy

gence Applications and Innovations Greece (2007) 5 Jain A Salish B Clustering based Short Term Load Forecasting using Support Vec shy

tor Machines In IEEE PowerTech Bucharest (2009) 6 Jigoria-Oprea D Kilyeni S Dan F Electric energy forecast for residential users

Journal of Sustainable Energy TI(2) (2011) 7 Hawarah L Ploi S Jacomino M User Behavior Prediction in Energy Consumpshy

tion in Housing Using Bayesian Networks vol 113 pp 372-379 (2010) http www springerLink comdoil0loo7978-3-642-13208-7 _47

8 Basu K Guillame-Berty M Joumaa H Ploix S Crowley J Predicting home sershyvice demands from appliance usage data In International Conference on Information and Communication Technologies and Applications lCTA 201 1 USA (2011)

9 Popescu V Oprea L Costianu DR Transmission Network Capacity Enhancement by Special Protection Schemes In 18th Int ConL Control Systems and Computer Science Bucharest vol I pp 205-210 (2011) ISSN 2066-4451

10 GeWngs CW The smart grid enabling energy efficiency and demand response USA

(2009) ISBN 0-88 173-624-4 II Ipakchi A Al buych F Grid of the future IEEE Power and Energy Magazine 8(3)

20-29 (2009) doilOl I09MPE2008931384 ISSN 1540-777 12 National Energy Technology Laboratory A Vis ion for the M odem Grid (2007) 13 Arghira N Hossu D F1Ig~an I lliescu S S Costianu DR Modern SCADA

philosophy in power system operation - A survey Scientific Bulletin of University POLITEHNJCA Buchares t Series C Electrical Engineering 73(2) 153- 166 (2011)

14 Feinberg EA Genethliou D Load Forecasting Applied mathematics for restrucshy

tured electric power systems pp 269-285 (2005) http www Spr i ngerLinkcomdoi 1010070-387-23471-3

15 AI fares KH Nazeeruddin M Electric load forecasting literature survey and classishyfication of methods In ternational Journal of Systems Science 33( 1)23-34 (2002)

16 Arghira N Ploi S Fagar3ltjan I Iliescu SS Prediction of energy consumption in homes In 18th Int Conf Control Systems and Computer Science Bucharest vol 1 pp 211-217 (2011 ) ISSN 2066-4451

Modelling and Composed Recursive Model Free Control for the Anaerobic Digestion Process

2Haoping W an g l B oyko Kachev bull Yang Tian l

2 3Ivan Simeonov and Nicolai Christov

1 Automation School Nanjing University of Science and Technology 200 Xiao Ling Wei St 210094 Nanjing China

2 The StephanAngeloff Institute of Microbiology - Bulgarian Academy of Sciences bl 26 Acad G Bonchev St 1113 Sofia Bulgaria

3 LAGIS UMR eNRS 8219 Universite Lille1 Sciences et Technologies 59655 Villeneuve d Ascq France hpwangnjusteducn kalchevmicrobiobasbg

Abstract This paper preseots a modelling and new composite recursive model free controller for trajectory tracking and disturbance compensation for the Anaerobic Digestion Process of cattle dung The used model is on the basis of a fifth-order continuous anaerobic digestion model And the proposed controller comprises a recursive model free controller based stabilization component and a time delay control based compensation component with recursi ve calculation structure which does not require any knowledge o f the model parameters Computer simulation examples illustrate the performance and robustness of the proposed approach

Keywords Anaerobic digestion composed recursive controller piecewise continuous systems recursive model free controller

1 Introduction

EnvirolUnental problems (ex air and water pollution) and energy shortage are nowadays recognized worldwide issues Several possibilities are available to treat these difficulties from nowadays different treatment ways biological processes are surely among the most used sustainable and efficient systems Anaerobic digestion (AD) which is a biotechnological process is widely used in life sciences wastewater treatment and a promising method for solving energy shortage and ecological protection problems in agriculture and agro-industry [1 ] In such kind of processes usually camed out in Continuously Stirred Tank bio-Reactors (CSTR) the organic matter is de-polluted by microorganisms into biogas (mainly methane ClL and carbon dioxide CO2) and compost in the absence of oxygen [2] [3]

AD is a very unstable process with regard to the bioreactor operation This is due to the complicated interactions between different microbial species as well as to the

1 Dumltrache (Ed ) Adv in lntelligent Control Systems amp Computer Science AlSC 187 pp 265-278 springcrlinkcom copy Springer-Verlag Berlin Heidelberg 2013

Page 4: Forecasting Energy Consumption in Dwellings - Indesenindesen.ats.com.ro/rezultate/articole/upb/2013_scan_springer_book.pdf · Finite Element Method Magnetics : Documentation: FEMM

257 256

Considering the actions between the actors of the energy market at the supply level it is obvious the importance of load prediction in the smart If dwellings are equipped wi th home automation systems capable of decisions the estimation of energy demand will be easier The prediction of the HAS must give accurate forecast for the appliances in the house in order build a reliable model and estimation on a larger scale

3 Load Forecasting

Load forecasting occupies a central position in terms of planning and operation electric utilities Load forecas ts are extremely important for energy Transport System Operators (TSOs) financial institutions and other paruCIPaDl in electric energy generation transmission distribution and markets 13 This tion presents a classification for load forecasting and the challenges of prediction for electrical appliances in dwellings

31 Classification of lAJad Forecasting

When considering the time period of forecast a classification into three can be done short-term forecasts which are usually from one hour to one medium forecasts which are usually from one day to a year and long-term casts which are lo nger than a year The forecasts for different time horizons important for different operations within a utility company

A review and categorization of electric load forecasting techniques is in [14-15J There are explained several methods for energy forecasting but of them concerns a single appliance in a home This paper tries to find a predict energy consumption for each device in a dwelling

32 Issues Concerning Energy Forecast in Dwellings

Predicting the energy consumption in tbe housing sector is a difficult task it vaJi es a lot depending on season weather conditions and user behaviour context of the home automation system the forecast for each electrical eq has to be done

The energy consumption of electrical devices varies a lot during a year or smaller periods of time It is difficult to find similarities in terms of between intervals of time In order to find patterns to help with predicting consumption the Euclidian distances are computed between each 2 days evaluated period data Several equipments in houses were tested but shows the differences for the freezer in house 2000949 Some knowledge has to be produced for a proper forecast

Forecasting Energy Consumption in Dwellings

t- -_u _ t m

Fig 5 Euclidian Distances between energy consumption values - freezer house 2000949

Forecasting Energy Consumption of Services in Dwellings

seen in previous section predicting the exact value of consumed energy is difshyso the aim of this paper is predicting whether one service will consume enshy

or not during each hour of the next 24 hours The predictor performance is based on recorded data which concern the energy

mption of appliances in 100 households in France during a full year The database comes from Residential Monitoring to Decrease Energy Use and

Emissions in Europe (REMODECE) which is a European database on pSidential consumption This database stores the characterization of residential

consumption by end-user and by country The information for each is recorded each 10 minutes and concerns the energy consumption for the

~ppliances and also the weather conditions (temperature wind strength wind flilection humidity)

Computing the Prediction Precision

designing a predictor it is important to set up a method for assessing the of a predictor because it clarifies the objectives For computing the

of a predictor test data are first to be considered Because of the predicshyrequired by an anticipative energy management system tbe test data are the

energy consumption for an appliance over a full year Figure 6 shows a set test data

258 259 N Arghira et aJ

Total sJte non-halogen Mghting ennrgy coneunpfo-n 20009G5 in hours

~ ~

-8 I ~

i 100 I III

i 1 11 111111111 illl]IIIII11 Ii

middot~OO~I ~ ~1h111ulII~__y jll~dlll Illi I((I HDI 1~ 100 he tisklry ihour]

Fig 6 Historical data for the energy consumption - non-halogen lighting in house 2000905

In order to evaluate the performance of predictors some concepts have to be defined

Let h be the current hour and e(h) be a binary value which is equal to 0 if the considered appliance is actually consuming energy during the hour hand 1 othershywise Let PaCh) be a prediction provided by the predictor a which is equal to 1 if the considered appliance is predicted as consuming power during the hour hand 0 otherwise The precision of the predictor is then expressed by

24 (25-i)e(h+i)- p (hh+24+i) J[ (h) - ( 1)za - =1 275

Any predictor a relies on an historical sliding time window of n hours used to preshyh n h

dict the d+ 1 predictions It can be denoted a - bull The number n has to be adshyjusted because if it is too large seasonal phenomena may disappear and if it is too short data set will not be sufficient to yield a precise prediction

The proposed algorithm for assessing a predictor a involves the fo llowing steps

1 Set the time window dimension to n hours within the period for which the historical data was registered where n goes from 24 to 36424

2 Compute the predictions for the data corresponding to the historical sliding time window

3 Compute the predictor precision n(h) based on the ne lCt day data for all possible hours h and compute an average precision for the predictor

42 Prediction with Basic Predictors

Since the informati on regarding thc energy consumption is very dependent on user behavior a stochastic approach will be tried Two trivial predictors are tested one

Forecasting Energy Consumption in Dwellings

that considers that the service will consume all the time in the future and one which considers the service will never consume

421 The Wiu Always Consume Predictor

This type of predictor involves considering that the appliance will consume energy permanently The prediction is computed based on a set of test data and refers to the probabili ty of the service to consume energy The prediction pihh+24) is elCshypressed

Pa (hh+24) = 1 h =12 24 (2)

Figure 7 shows the prediction precision Jra(h) for each time window of the test

data considered in days (the prediction precision for a sliding window of 1 day 2 days etc) This curve was obtained using the previously presented algorithm for assessing the predictor

422 The Will Never Consume Predictor

Thi s predictor assumes the service will not consume at all in the nelCt day The prediction is computed based on a set of test data and refers to the probability of the service not to consume) The prediction p(hh+24) is computed

Po Ch h+ 24) = 0 h = 12 24 (3)

It can be denoted that the value of precision for this predictor is the complement of the will always consume predictor precision Figure 8 shows the prediction preshycision for cach time window of the test data considered in days for this predictor Bcst precision is reached for a historical interval of aprolCimatelyl OO days

r r ~i ____ ~ r

Fig 7 Prediction precision assuming the Fig 8 Prediction probability assuming the service will consume continuously service will never consume

43 The Proposed Predictor

An inhabitant in the house interacts with various electrical devices as part of his routine activities Thus energy consumption can be modeled as a stochastic process

261 260 N Arghira et aI

In this context the proposed predictor specifies the probability of the appliance to consume on an hourly base We consider the following prediction form ula

n)Ch h+24) po(hh+24) gt PJ h=1224n(h)

(4)P (hh+24)= n (h h+24) P (h h+24) $ Po

(1 1- I bull a t

nth)1 Where n(h) is the considered number of hours h in the test period ndhh+24) is the number of limes the service did consume during hour h of the historical data and Pa is a set threshold Figure 9 shows the prediction precision of the proposed predictor related to the basic predictors in the previous subsections

E---middot~middot-7middotmiddotmiddotmiddotq I I ----r

bull _-- _ ~_____~_ _ ~-a- _ __________ ~~~ _I

- - ~ ---shyI I I I-==~It ---------~ - ~ ~ -- ~~~I

I I I I I 1

Imiddot -- - 1- - - ~ - - ~ - - - - - - - - -~ t I I I

_~ _ __ ~ ___ L __ _ _ _ _ h _ _~ ~

I I

-middot-middot-middot middot~ -middot-middot1middotmiddotI middot middot middot --middot middot middott middot middot middot -middotmiddot-middot~middot-middot-middot- middotmiddotmiddotmiddot middot I~ ~

Fig 9 Prediction precision using the proposed predictor

44 Enhancing the Forecast Precision

In order to increase the precision some similarities between data are considered and clustering methods are applied

441 Segmentation of Data

While mining the available data some pattern of recurrence is searched in order to immiddot prove the prediction A temporal segmentation can be used to introduce knowledge in the predictor for instance the use of the oven may be different for rainy Saturdays The segmentation of data can be made considering different aspects such as the seashyson month period of the day (daynight) type of day (weekday weekend) The obshyjective of this operation is to reduce the average dispersion in order to improve the prediction After the segmentation is done we will merge the segments thaI are similar using a clustering algorithm in order to gather the non-meaningful segments

temporal segmentation that considers each day of the week as a partition was done For each segment the hourly predictions are made considering the proshyposed predictor A k-Means clustering algorithm is applied in order to group the similar consumption days

Forecasting Energy Consumption in Dwellings

The KmiddotMeans algorithm assumes a fixed number of clusters specified in adshyvance Each cluster is defined by its cluster center and clustering proceeds by asshysigning each of the input data to the cluster with the closest centre The grouping is done by minimizing the sum of squares of distances between data and the corresponding cluster centroid This algorithm is based on the euclidean distance

ED(X n = JI (x - y -1 (5)

Where X Y are vectors n is the vector length and XiYi arc their components The center of each cluster is then re-estimated as the centroid of the points

assigned to it The process is then iterated until a convergence criterion is accomplished ED is a set threshold

ED(Xy) 5 ED (6)

442 The Prediction Precision after Clustering

After applying the iterative kmiddotMeans algorithm two clusters are obtained In the presented case cluster C) groups week days data and cluster C

2 gathers Saturday

and Sunday data After the clusters are obtained the initial data corresponding to the energy consumption is divided into 2 sets according to the number of clusters and the considered segments The prediction precision is computed for the proshyposed predictor for each of the clusters Figure 10 a and b show the prediction precision for the new test data The blue curve represents the precision for the proposed predictor and the other two are the curves for the basic predictors

Figures 9 and 10 a b present the prediction precision in 3 situations for initial energy consumption data for merged segments in cluster CI da ta and for merged segments in cluster C2 data When comparing the obtained curves for the proshyposed predictor the precision of the predictor increases after the clustering is done This proves that the segmentation of data and then the merging of similar partitions is a good method for increasing the prediction performance

iii PredlcWl Pr-JCiMon 1V 200C905 C1 Ptedc1i0l PrecSloln lV 2tJOO91)$ C2

r

-- ~----- - - ~ -- - ~--~ [3~ _ _ ___ _ --~_ c_

i=~ =~ == = ~ ~middotmiddot-jI - shy

Imiddot 1~~t~~ ~middot ~[T~middot~middotT-1- ---1- -

Jr bull _ ~ L __1_ _ I _ _ _ L _ _ bull L _ -=-~ 1bullbull bull 7 bullbull-_ -bullbull ~ _- I-~ -

middotmiddotmiddotmiddotmiddotmiddotmiddotmiddot middotmiddot IlI middot-bullbull7 (bullbullbull7bull71bullbull-bullbull- _ bullbull o- bull -- noshy

~-

a CllISler CI b CiUSler C2

Fig 10 Prediction precision comparison for the three predictors - clustered data

263 262 N Arghtra et al

5 Experiments and Discussion

This section presents the results for different electrical appliances in the house when the proposed predictor is used in comparison with the basic predictors will never consume and will consume continuousl y There are several services which were tested but this paper will show the results for appliances representashytive for their class the fridge as it consumes all the time the boiler as its consumption can be delayed and the lighting as it has a regular usage in a house

The prediction precision is computed as explained in subsection 41 for a slid shying time window between I and 364 days covering all the historical data avai lashyble The tests show that the prediction acts in a special manner depending on the type of the electrical appliance (16)

The prediction precision with the proposed predictor for the refrigerator (fig 13) is lower than the precision assuming permanent consumption for time winshydows higher than 2 days This implies that the prediction for the fridge should be done considering a short period of hi storical data (e g two weeks) in order to get a high precision This conclusion was expected since the energy consumption for this appliance is dependent on the season so a short period of time is significant for prediction

The pcrfonnance of prediction for the overall lighting consumption (figures 11) is hi gher than the performance of the basic predictors for the entire recorded data intervaL For the gas boiler (figure 12) a good precision is obtained for almost all the considered periods of historical data

As a general observation the maximum value for prediction accuracy is obshytained for a short period of historical data - almost in all studied cases the best precision was reached for one day of recorded data Using this result will decrease the computation time since the learning time is short

PoodidiM p Gas Solelt 2000949 011 Pr_ Prod_ 2OOOl 0 Tota si IggtIOIO II lt1gt Ii ~

~~--=- - - t- - - - - - - - - t shy

r r==~~bull J _ _ _ 1_ _ _ _1 __ bull J

bull __1 _ _ _ L __ __

1 ___ w

_ _ _ _ J _ 1__ _ __ _ _ - -1 shyi

~

~__ j _ __ _ _ J _ _ _ I _ _ _

Uj bull - ~ -~ ~~----~--- ~--- -~=~~ -- - shybull __ _ ~~ r __ _ _ bull - _ _ _ 1 _ _ _

j _ _

~I I ~ I J _ _-t~~~~-r-~~~middot~ ~+~~-~~~~~~~~~~ I 1

---- 1 ---- ~ --_~- _ 1IIfIot

-1 middotmiddot(1111

Fig 11 Prediction precision of the boiler Fig 12 Prediction precision of lighting con-elec tricity consumption in house 2000949 sumption in house 2000910

Forecasting Energy Consumption in Dwellings

Fridgil catwmptlon 2000949 houtty probablibM i f I I

middot -1 - _ L __ J _~_ middot _ _ J _ _ L __ ~I

I

E 1I1 - ~ - - -===~~-~~Iimiddot-- -- ~ ~

I

I I I I I It I I I I I bull I - - - - -r - - 1- -- r- - - - r --

I bull I I I

- - --- -- -- ~ - - - ~--~- - -f- - ~

I

==-=---- -----~-~~--- -~

Fig 13 Prediction precision of the refrigerator consumption in house 2000949

6 Conclusions

Predicting the energy consumption in dwellings is an essential part in the power management of the grid as the consumption in the residential sector represents a siguificant percentage in the total electricity demand The development of the smart grid is not possible without a good prediction of energy consumption The trend nowadays is to get the prediction of energy consumption not only at house level but at household appliance leveL

The prediction of energy consumption in housing is very dependent on inhabishytants behavior so a stochastic method for prediction has been presented in this paper The paper discusses about how to evaluate the precision of a predictor in the day +1 power management context Different basic predictors are presented and tested for the available historical data A relevant predictor is presented

Segmentation of data is done considering the patterns in energy consumption Also the historical data is divided according to the results of the k-means clustershying algorithm After testing the predictor on the new clustered data the precision of the predictor improves

Further work involves testing the proposed predictor for all the appliances in a house in order to decide the proper way for prediction at the equipment level

References

I Brooks 1 Lu E Reicher D Spirakis C Weihl B Demand Dispatch IEEE Powshyer and Energy Magazine 8(3) 20-29 (20 10) ISSN 1540-7977

2 Iliescu SS Fagarasan I Modem approaches in power system control In IEEE Int Conf on Automation Quali ty and testing Robotics Cluj voL I pp 41 -44 (2008)

3 Long Ha D Ploix S Zamai E Jacomino M Realtimes dynamic optimization for demand-side load management International Journal of Management Science and Engineering Management 3(4) 243-252 (2008) ISSN 1750-9653

264 N Arghira et al

4 Abras S Ploix S Pesty S Jacomino M A multi-agent design for a home automashytion system dedicated to power management In lF1P Conference on Artificial Intellishy

gence Applications and Innovations Greece (2007) 5 Jain A Salish B Clustering based Short Term Load Forecasting using Support Vec shy

tor Machines In IEEE PowerTech Bucharest (2009) 6 Jigoria-Oprea D Kilyeni S Dan F Electric energy forecast for residential users

Journal of Sustainable Energy TI(2) (2011) 7 Hawarah L Ploi S Jacomino M User Behavior Prediction in Energy Consumpshy

tion in Housing Using Bayesian Networks vol 113 pp 372-379 (2010) http www springerLink comdoil0loo7978-3-642-13208-7 _47

8 Basu K Guillame-Berty M Joumaa H Ploix S Crowley J Predicting home sershyvice demands from appliance usage data In International Conference on Information and Communication Technologies and Applications lCTA 201 1 USA (2011)

9 Popescu V Oprea L Costianu DR Transmission Network Capacity Enhancement by Special Protection Schemes In 18th Int ConL Control Systems and Computer Science Bucharest vol I pp 205-210 (2011) ISSN 2066-4451

10 GeWngs CW The smart grid enabling energy efficiency and demand response USA

(2009) ISBN 0-88 173-624-4 II Ipakchi A Al buych F Grid of the future IEEE Power and Energy Magazine 8(3)

20-29 (2009) doilOl I09MPE2008931384 ISSN 1540-777 12 National Energy Technology Laboratory A Vis ion for the M odem Grid (2007) 13 Arghira N Hossu D F1Ig~an I lliescu S S Costianu DR Modern SCADA

philosophy in power system operation - A survey Scientific Bulletin of University POLITEHNJCA Buchares t Series C Electrical Engineering 73(2) 153- 166 (2011)

14 Feinberg EA Genethliou D Load Forecasting Applied mathematics for restrucshy

tured electric power systems pp 269-285 (2005) http www Spr i ngerLinkcomdoi 1010070-387-23471-3

15 AI fares KH Nazeeruddin M Electric load forecasting literature survey and classishyfication of methods In ternational Journal of Systems Science 33( 1)23-34 (2002)

16 Arghira N Ploi S Fagar3ltjan I Iliescu SS Prediction of energy consumption in homes In 18th Int Conf Control Systems and Computer Science Bucharest vol 1 pp 211-217 (2011 ) ISSN 2066-4451

Modelling and Composed Recursive Model Free Control for the Anaerobic Digestion Process

2Haoping W an g l B oyko Kachev bull Yang Tian l

2 3Ivan Simeonov and Nicolai Christov

1 Automation School Nanjing University of Science and Technology 200 Xiao Ling Wei St 210094 Nanjing China

2 The StephanAngeloff Institute of Microbiology - Bulgarian Academy of Sciences bl 26 Acad G Bonchev St 1113 Sofia Bulgaria

3 LAGIS UMR eNRS 8219 Universite Lille1 Sciences et Technologies 59655 Villeneuve d Ascq France hpwangnjusteducn kalchevmicrobiobasbg

Abstract This paper preseots a modelling and new composite recursive model free controller for trajectory tracking and disturbance compensation for the Anaerobic Digestion Process of cattle dung The used model is on the basis of a fifth-order continuous anaerobic digestion model And the proposed controller comprises a recursive model free controller based stabilization component and a time delay control based compensation component with recursi ve calculation structure which does not require any knowledge o f the model parameters Computer simulation examples illustrate the performance and robustness of the proposed approach

Keywords Anaerobic digestion composed recursive controller piecewise continuous systems recursive model free controller

1 Introduction

EnvirolUnental problems (ex air and water pollution) and energy shortage are nowadays recognized worldwide issues Several possibilities are available to treat these difficulties from nowadays different treatment ways biological processes are surely among the most used sustainable and efficient systems Anaerobic digestion (AD) which is a biotechnological process is widely used in life sciences wastewater treatment and a promising method for solving energy shortage and ecological protection problems in agriculture and agro-industry [1 ] In such kind of processes usually camed out in Continuously Stirred Tank bio-Reactors (CSTR) the organic matter is de-polluted by microorganisms into biogas (mainly methane ClL and carbon dioxide CO2) and compost in the absence of oxygen [2] [3]

AD is a very unstable process with regard to the bioreactor operation This is due to the complicated interactions between different microbial species as well as to the

1 Dumltrache (Ed ) Adv in lntelligent Control Systems amp Computer Science AlSC 187 pp 265-278 springcrlinkcom copy Springer-Verlag Berlin Heidelberg 2013

Page 5: Forecasting Energy Consumption in Dwellings - Indesenindesen.ats.com.ro/rezultate/articole/upb/2013_scan_springer_book.pdf · Finite Element Method Magnetics : Documentation: FEMM

258 259 N Arghira et aJ

Total sJte non-halogen Mghting ennrgy coneunpfo-n 20009G5 in hours

~ ~

-8 I ~

i 100 I III

i 1 11 111111111 illl]IIIII11 Ii

middot~OO~I ~ ~1h111ulII~__y jll~dlll Illi I((I HDI 1~ 100 he tisklry ihour]

Fig 6 Historical data for the energy consumption - non-halogen lighting in house 2000905

In order to evaluate the performance of predictors some concepts have to be defined

Let h be the current hour and e(h) be a binary value which is equal to 0 if the considered appliance is actually consuming energy during the hour hand 1 othershywise Let PaCh) be a prediction provided by the predictor a which is equal to 1 if the considered appliance is predicted as consuming power during the hour hand 0 otherwise The precision of the predictor is then expressed by

24 (25-i)e(h+i)- p (hh+24+i) J[ (h) - ( 1)za - =1 275

Any predictor a relies on an historical sliding time window of n hours used to preshyh n h

dict the d+ 1 predictions It can be denoted a - bull The number n has to be adshyjusted because if it is too large seasonal phenomena may disappear and if it is too short data set will not be sufficient to yield a precise prediction

The proposed algorithm for assessing a predictor a involves the fo llowing steps

1 Set the time window dimension to n hours within the period for which the historical data was registered where n goes from 24 to 36424

2 Compute the predictions for the data corresponding to the historical sliding time window

3 Compute the predictor precision n(h) based on the ne lCt day data for all possible hours h and compute an average precision for the predictor

42 Prediction with Basic Predictors

Since the informati on regarding thc energy consumption is very dependent on user behavior a stochastic approach will be tried Two trivial predictors are tested one

Forecasting Energy Consumption in Dwellings

that considers that the service will consume all the time in the future and one which considers the service will never consume

421 The Wiu Always Consume Predictor

This type of predictor involves considering that the appliance will consume energy permanently The prediction is computed based on a set of test data and refers to the probabili ty of the service to consume energy The prediction pihh+24) is elCshypressed

Pa (hh+24) = 1 h =12 24 (2)

Figure 7 shows the prediction precision Jra(h) for each time window of the test

data considered in days (the prediction precision for a sliding window of 1 day 2 days etc) This curve was obtained using the previously presented algorithm for assessing the predictor

422 The Will Never Consume Predictor

Thi s predictor assumes the service will not consume at all in the nelCt day The prediction is computed based on a set of test data and refers to the probability of the service not to consume) The prediction p(hh+24) is computed

Po Ch h+ 24) = 0 h = 12 24 (3)

It can be denoted that the value of precision for this predictor is the complement of the will always consume predictor precision Figure 8 shows the prediction preshycision for cach time window of the test data considered in days for this predictor Bcst precision is reached for a historical interval of aprolCimatelyl OO days

r r ~i ____ ~ r

Fig 7 Prediction precision assuming the Fig 8 Prediction probability assuming the service will consume continuously service will never consume

43 The Proposed Predictor

An inhabitant in the house interacts with various electrical devices as part of his routine activities Thus energy consumption can be modeled as a stochastic process

261 260 N Arghira et aI

In this context the proposed predictor specifies the probability of the appliance to consume on an hourly base We consider the following prediction form ula

n)Ch h+24) po(hh+24) gt PJ h=1224n(h)

(4)P (hh+24)= n (h h+24) P (h h+24) $ Po

(1 1- I bull a t

nth)1 Where n(h) is the considered number of hours h in the test period ndhh+24) is the number of limes the service did consume during hour h of the historical data and Pa is a set threshold Figure 9 shows the prediction precision of the proposed predictor related to the basic predictors in the previous subsections

E---middot~middot-7middotmiddotmiddotmiddotq I I ----r

bull _-- _ ~_____~_ _ ~-a- _ __________ ~~~ _I

- - ~ ---shyI I I I-==~It ---------~ - ~ ~ -- ~~~I

I I I I I 1

Imiddot -- - 1- - - ~ - - ~ - - - - - - - - -~ t I I I

_~ _ __ ~ ___ L __ _ _ _ _ h _ _~ ~

I I

-middot-middot-middot middot~ -middot-middot1middotmiddotI middot middot middot --middot middot middott middot middot middot -middotmiddot-middot~middot-middot-middot- middotmiddotmiddotmiddot middot I~ ~

Fig 9 Prediction precision using the proposed predictor

44 Enhancing the Forecast Precision

In order to increase the precision some similarities between data are considered and clustering methods are applied

441 Segmentation of Data

While mining the available data some pattern of recurrence is searched in order to immiddot prove the prediction A temporal segmentation can be used to introduce knowledge in the predictor for instance the use of the oven may be different for rainy Saturdays The segmentation of data can be made considering different aspects such as the seashyson month period of the day (daynight) type of day (weekday weekend) The obshyjective of this operation is to reduce the average dispersion in order to improve the prediction After the segmentation is done we will merge the segments thaI are similar using a clustering algorithm in order to gather the non-meaningful segments

temporal segmentation that considers each day of the week as a partition was done For each segment the hourly predictions are made considering the proshyposed predictor A k-Means clustering algorithm is applied in order to group the similar consumption days

Forecasting Energy Consumption in Dwellings

The KmiddotMeans algorithm assumes a fixed number of clusters specified in adshyvance Each cluster is defined by its cluster center and clustering proceeds by asshysigning each of the input data to the cluster with the closest centre The grouping is done by minimizing the sum of squares of distances between data and the corresponding cluster centroid This algorithm is based on the euclidean distance

ED(X n = JI (x - y -1 (5)

Where X Y are vectors n is the vector length and XiYi arc their components The center of each cluster is then re-estimated as the centroid of the points

assigned to it The process is then iterated until a convergence criterion is accomplished ED is a set threshold

ED(Xy) 5 ED (6)

442 The Prediction Precision after Clustering

After applying the iterative kmiddotMeans algorithm two clusters are obtained In the presented case cluster C) groups week days data and cluster C

2 gathers Saturday

and Sunday data After the clusters are obtained the initial data corresponding to the energy consumption is divided into 2 sets according to the number of clusters and the considered segments The prediction precision is computed for the proshyposed predictor for each of the clusters Figure 10 a and b show the prediction precision for the new test data The blue curve represents the precision for the proposed predictor and the other two are the curves for the basic predictors

Figures 9 and 10 a b present the prediction precision in 3 situations for initial energy consumption data for merged segments in cluster CI da ta and for merged segments in cluster C2 data When comparing the obtained curves for the proshyposed predictor the precision of the predictor increases after the clustering is done This proves that the segmentation of data and then the merging of similar partitions is a good method for increasing the prediction performance

iii PredlcWl Pr-JCiMon 1V 200C905 C1 Ptedc1i0l PrecSloln lV 2tJOO91)$ C2

r

-- ~----- - - ~ -- - ~--~ [3~ _ _ ___ _ --~_ c_

i=~ =~ == = ~ ~middotmiddot-jI - shy

Imiddot 1~~t~~ ~middot ~[T~middot~middotT-1- ---1- -

Jr bull _ ~ L __1_ _ I _ _ _ L _ _ bull L _ -=-~ 1bullbull bull 7 bullbull-_ -bullbull ~ _- I-~ -

middotmiddotmiddotmiddotmiddotmiddotmiddotmiddot middotmiddot IlI middot-bullbull7 (bullbullbull7bull71bullbull-bullbull- _ bullbull o- bull -- noshy

~-

a CllISler CI b CiUSler C2

Fig 10 Prediction precision comparison for the three predictors - clustered data

263 262 N Arghtra et al

5 Experiments and Discussion

This section presents the results for different electrical appliances in the house when the proposed predictor is used in comparison with the basic predictors will never consume and will consume continuousl y There are several services which were tested but this paper will show the results for appliances representashytive for their class the fridge as it consumes all the time the boiler as its consumption can be delayed and the lighting as it has a regular usage in a house

The prediction precision is computed as explained in subsection 41 for a slid shying time window between I and 364 days covering all the historical data avai lashyble The tests show that the prediction acts in a special manner depending on the type of the electrical appliance (16)

The prediction precision with the proposed predictor for the refrigerator (fig 13) is lower than the precision assuming permanent consumption for time winshydows higher than 2 days This implies that the prediction for the fridge should be done considering a short period of hi storical data (e g two weeks) in order to get a high precision This conclusion was expected since the energy consumption for this appliance is dependent on the season so a short period of time is significant for prediction

The pcrfonnance of prediction for the overall lighting consumption (figures 11) is hi gher than the performance of the basic predictors for the entire recorded data intervaL For the gas boiler (figure 12) a good precision is obtained for almost all the considered periods of historical data

As a general observation the maximum value for prediction accuracy is obshytained for a short period of historical data - almost in all studied cases the best precision was reached for one day of recorded data Using this result will decrease the computation time since the learning time is short

PoodidiM p Gas Solelt 2000949 011 Pr_ Prod_ 2OOOl 0 Tota si IggtIOIO II lt1gt Ii ~

~~--=- - - t- - - - - - - - - t shy

r r==~~bull J _ _ _ 1_ _ _ _1 __ bull J

bull __1 _ _ _ L __ __

1 ___ w

_ _ _ _ J _ 1__ _ __ _ _ - -1 shyi

~

~__ j _ __ _ _ J _ _ _ I _ _ _

Uj bull - ~ -~ ~~----~--- ~--- -~=~~ -- - shybull __ _ ~~ r __ _ _ bull - _ _ _ 1 _ _ _

j _ _

~I I ~ I J _ _-t~~~~-r-~~~middot~ ~+~~-~~~~~~~~~~ I 1

---- 1 ---- ~ --_~- _ 1IIfIot

-1 middotmiddot(1111

Fig 11 Prediction precision of the boiler Fig 12 Prediction precision of lighting con-elec tricity consumption in house 2000949 sumption in house 2000910

Forecasting Energy Consumption in Dwellings

Fridgil catwmptlon 2000949 houtty probablibM i f I I

middot -1 - _ L __ J _~_ middot _ _ J _ _ L __ ~I

I

E 1I1 - ~ - - -===~~-~~Iimiddot-- -- ~ ~

I

I I I I I It I I I I I bull I - - - - -r - - 1- -- r- - - - r --

I bull I I I

- - --- -- -- ~ - - - ~--~- - -f- - ~

I

==-=---- -----~-~~--- -~

Fig 13 Prediction precision of the refrigerator consumption in house 2000949

6 Conclusions

Predicting the energy consumption in dwellings is an essential part in the power management of the grid as the consumption in the residential sector represents a siguificant percentage in the total electricity demand The development of the smart grid is not possible without a good prediction of energy consumption The trend nowadays is to get the prediction of energy consumption not only at house level but at household appliance leveL

The prediction of energy consumption in housing is very dependent on inhabishytants behavior so a stochastic method for prediction has been presented in this paper The paper discusses about how to evaluate the precision of a predictor in the day +1 power management context Different basic predictors are presented and tested for the available historical data A relevant predictor is presented

Segmentation of data is done considering the patterns in energy consumption Also the historical data is divided according to the results of the k-means clustershying algorithm After testing the predictor on the new clustered data the precision of the predictor improves

Further work involves testing the proposed predictor for all the appliances in a house in order to decide the proper way for prediction at the equipment level

References

I Brooks 1 Lu E Reicher D Spirakis C Weihl B Demand Dispatch IEEE Powshyer and Energy Magazine 8(3) 20-29 (20 10) ISSN 1540-7977

2 Iliescu SS Fagarasan I Modem approaches in power system control In IEEE Int Conf on Automation Quali ty and testing Robotics Cluj voL I pp 41 -44 (2008)

3 Long Ha D Ploix S Zamai E Jacomino M Realtimes dynamic optimization for demand-side load management International Journal of Management Science and Engineering Management 3(4) 243-252 (2008) ISSN 1750-9653

264 N Arghira et al

4 Abras S Ploix S Pesty S Jacomino M A multi-agent design for a home automashytion system dedicated to power management In lF1P Conference on Artificial Intellishy

gence Applications and Innovations Greece (2007) 5 Jain A Salish B Clustering based Short Term Load Forecasting using Support Vec shy

tor Machines In IEEE PowerTech Bucharest (2009) 6 Jigoria-Oprea D Kilyeni S Dan F Electric energy forecast for residential users

Journal of Sustainable Energy TI(2) (2011) 7 Hawarah L Ploi S Jacomino M User Behavior Prediction in Energy Consumpshy

tion in Housing Using Bayesian Networks vol 113 pp 372-379 (2010) http www springerLink comdoil0loo7978-3-642-13208-7 _47

8 Basu K Guillame-Berty M Joumaa H Ploix S Crowley J Predicting home sershyvice demands from appliance usage data In International Conference on Information and Communication Technologies and Applications lCTA 201 1 USA (2011)

9 Popescu V Oprea L Costianu DR Transmission Network Capacity Enhancement by Special Protection Schemes In 18th Int ConL Control Systems and Computer Science Bucharest vol I pp 205-210 (2011) ISSN 2066-4451

10 GeWngs CW The smart grid enabling energy efficiency and demand response USA

(2009) ISBN 0-88 173-624-4 II Ipakchi A Al buych F Grid of the future IEEE Power and Energy Magazine 8(3)

20-29 (2009) doilOl I09MPE2008931384 ISSN 1540-777 12 National Energy Technology Laboratory A Vis ion for the M odem Grid (2007) 13 Arghira N Hossu D F1Ig~an I lliescu S S Costianu DR Modern SCADA

philosophy in power system operation - A survey Scientific Bulletin of University POLITEHNJCA Buchares t Series C Electrical Engineering 73(2) 153- 166 (2011)

14 Feinberg EA Genethliou D Load Forecasting Applied mathematics for restrucshy

tured electric power systems pp 269-285 (2005) http www Spr i ngerLinkcomdoi 1010070-387-23471-3

15 AI fares KH Nazeeruddin M Electric load forecasting literature survey and classishyfication of methods In ternational Journal of Systems Science 33( 1)23-34 (2002)

16 Arghira N Ploi S Fagar3ltjan I Iliescu SS Prediction of energy consumption in homes In 18th Int Conf Control Systems and Computer Science Bucharest vol 1 pp 211-217 (2011 ) ISSN 2066-4451

Modelling and Composed Recursive Model Free Control for the Anaerobic Digestion Process

2Haoping W an g l B oyko Kachev bull Yang Tian l

2 3Ivan Simeonov and Nicolai Christov

1 Automation School Nanjing University of Science and Technology 200 Xiao Ling Wei St 210094 Nanjing China

2 The StephanAngeloff Institute of Microbiology - Bulgarian Academy of Sciences bl 26 Acad G Bonchev St 1113 Sofia Bulgaria

3 LAGIS UMR eNRS 8219 Universite Lille1 Sciences et Technologies 59655 Villeneuve d Ascq France hpwangnjusteducn kalchevmicrobiobasbg

Abstract This paper preseots a modelling and new composite recursive model free controller for trajectory tracking and disturbance compensation for the Anaerobic Digestion Process of cattle dung The used model is on the basis of a fifth-order continuous anaerobic digestion model And the proposed controller comprises a recursive model free controller based stabilization component and a time delay control based compensation component with recursi ve calculation structure which does not require any knowledge o f the model parameters Computer simulation examples illustrate the performance and robustness of the proposed approach

Keywords Anaerobic digestion composed recursive controller piecewise continuous systems recursive model free controller

1 Introduction

EnvirolUnental problems (ex air and water pollution) and energy shortage are nowadays recognized worldwide issues Several possibilities are available to treat these difficulties from nowadays different treatment ways biological processes are surely among the most used sustainable and efficient systems Anaerobic digestion (AD) which is a biotechnological process is widely used in life sciences wastewater treatment and a promising method for solving energy shortage and ecological protection problems in agriculture and agro-industry [1 ] In such kind of processes usually camed out in Continuously Stirred Tank bio-Reactors (CSTR) the organic matter is de-polluted by microorganisms into biogas (mainly methane ClL and carbon dioxide CO2) and compost in the absence of oxygen [2] [3]

AD is a very unstable process with regard to the bioreactor operation This is due to the complicated interactions between different microbial species as well as to the

1 Dumltrache (Ed ) Adv in lntelligent Control Systems amp Computer Science AlSC 187 pp 265-278 springcrlinkcom copy Springer-Verlag Berlin Heidelberg 2013

Page 6: Forecasting Energy Consumption in Dwellings - Indesenindesen.ats.com.ro/rezultate/articole/upb/2013_scan_springer_book.pdf · Finite Element Method Magnetics : Documentation: FEMM

261 260 N Arghira et aI

In this context the proposed predictor specifies the probability of the appliance to consume on an hourly base We consider the following prediction form ula

n)Ch h+24) po(hh+24) gt PJ h=1224n(h)

(4)P (hh+24)= n (h h+24) P (h h+24) $ Po

(1 1- I bull a t

nth)1 Where n(h) is the considered number of hours h in the test period ndhh+24) is the number of limes the service did consume during hour h of the historical data and Pa is a set threshold Figure 9 shows the prediction precision of the proposed predictor related to the basic predictors in the previous subsections

E---middot~middot-7middotmiddotmiddotmiddotq I I ----r

bull _-- _ ~_____~_ _ ~-a- _ __________ ~~~ _I

- - ~ ---shyI I I I-==~It ---------~ - ~ ~ -- ~~~I

I I I I I 1

Imiddot -- - 1- - - ~ - - ~ - - - - - - - - -~ t I I I

_~ _ __ ~ ___ L __ _ _ _ _ h _ _~ ~

I I

-middot-middot-middot middot~ -middot-middot1middotmiddotI middot middot middot --middot middot middott middot middot middot -middotmiddot-middot~middot-middot-middot- middotmiddotmiddotmiddot middot I~ ~

Fig 9 Prediction precision using the proposed predictor

44 Enhancing the Forecast Precision

In order to increase the precision some similarities between data are considered and clustering methods are applied

441 Segmentation of Data

While mining the available data some pattern of recurrence is searched in order to immiddot prove the prediction A temporal segmentation can be used to introduce knowledge in the predictor for instance the use of the oven may be different for rainy Saturdays The segmentation of data can be made considering different aspects such as the seashyson month period of the day (daynight) type of day (weekday weekend) The obshyjective of this operation is to reduce the average dispersion in order to improve the prediction After the segmentation is done we will merge the segments thaI are similar using a clustering algorithm in order to gather the non-meaningful segments

temporal segmentation that considers each day of the week as a partition was done For each segment the hourly predictions are made considering the proshyposed predictor A k-Means clustering algorithm is applied in order to group the similar consumption days

Forecasting Energy Consumption in Dwellings

The KmiddotMeans algorithm assumes a fixed number of clusters specified in adshyvance Each cluster is defined by its cluster center and clustering proceeds by asshysigning each of the input data to the cluster with the closest centre The grouping is done by minimizing the sum of squares of distances between data and the corresponding cluster centroid This algorithm is based on the euclidean distance

ED(X n = JI (x - y -1 (5)

Where X Y are vectors n is the vector length and XiYi arc their components The center of each cluster is then re-estimated as the centroid of the points

assigned to it The process is then iterated until a convergence criterion is accomplished ED is a set threshold

ED(Xy) 5 ED (6)

442 The Prediction Precision after Clustering

After applying the iterative kmiddotMeans algorithm two clusters are obtained In the presented case cluster C) groups week days data and cluster C

2 gathers Saturday

and Sunday data After the clusters are obtained the initial data corresponding to the energy consumption is divided into 2 sets according to the number of clusters and the considered segments The prediction precision is computed for the proshyposed predictor for each of the clusters Figure 10 a and b show the prediction precision for the new test data The blue curve represents the precision for the proposed predictor and the other two are the curves for the basic predictors

Figures 9 and 10 a b present the prediction precision in 3 situations for initial energy consumption data for merged segments in cluster CI da ta and for merged segments in cluster C2 data When comparing the obtained curves for the proshyposed predictor the precision of the predictor increases after the clustering is done This proves that the segmentation of data and then the merging of similar partitions is a good method for increasing the prediction performance

iii PredlcWl Pr-JCiMon 1V 200C905 C1 Ptedc1i0l PrecSloln lV 2tJOO91)$ C2

r

-- ~----- - - ~ -- - ~--~ [3~ _ _ ___ _ --~_ c_

i=~ =~ == = ~ ~middotmiddot-jI - shy

Imiddot 1~~t~~ ~middot ~[T~middot~middotT-1- ---1- -

Jr bull _ ~ L __1_ _ I _ _ _ L _ _ bull L _ -=-~ 1bullbull bull 7 bullbull-_ -bullbull ~ _- I-~ -

middotmiddotmiddotmiddotmiddotmiddotmiddotmiddot middotmiddot IlI middot-bullbull7 (bullbullbull7bull71bullbull-bullbull- _ bullbull o- bull -- noshy

~-

a CllISler CI b CiUSler C2

Fig 10 Prediction precision comparison for the three predictors - clustered data

263 262 N Arghtra et al

5 Experiments and Discussion

This section presents the results for different electrical appliances in the house when the proposed predictor is used in comparison with the basic predictors will never consume and will consume continuousl y There are several services which were tested but this paper will show the results for appliances representashytive for their class the fridge as it consumes all the time the boiler as its consumption can be delayed and the lighting as it has a regular usage in a house

The prediction precision is computed as explained in subsection 41 for a slid shying time window between I and 364 days covering all the historical data avai lashyble The tests show that the prediction acts in a special manner depending on the type of the electrical appliance (16)

The prediction precision with the proposed predictor for the refrigerator (fig 13) is lower than the precision assuming permanent consumption for time winshydows higher than 2 days This implies that the prediction for the fridge should be done considering a short period of hi storical data (e g two weeks) in order to get a high precision This conclusion was expected since the energy consumption for this appliance is dependent on the season so a short period of time is significant for prediction

The pcrfonnance of prediction for the overall lighting consumption (figures 11) is hi gher than the performance of the basic predictors for the entire recorded data intervaL For the gas boiler (figure 12) a good precision is obtained for almost all the considered periods of historical data

As a general observation the maximum value for prediction accuracy is obshytained for a short period of historical data - almost in all studied cases the best precision was reached for one day of recorded data Using this result will decrease the computation time since the learning time is short

PoodidiM p Gas Solelt 2000949 011 Pr_ Prod_ 2OOOl 0 Tota si IggtIOIO II lt1gt Ii ~

~~--=- - - t- - - - - - - - - t shy

r r==~~bull J _ _ _ 1_ _ _ _1 __ bull J

bull __1 _ _ _ L __ __

1 ___ w

_ _ _ _ J _ 1__ _ __ _ _ - -1 shyi

~

~__ j _ __ _ _ J _ _ _ I _ _ _

Uj bull - ~ -~ ~~----~--- ~--- -~=~~ -- - shybull __ _ ~~ r __ _ _ bull - _ _ _ 1 _ _ _

j _ _

~I I ~ I J _ _-t~~~~-r-~~~middot~ ~+~~-~~~~~~~~~~ I 1

---- 1 ---- ~ --_~- _ 1IIfIot

-1 middotmiddot(1111

Fig 11 Prediction precision of the boiler Fig 12 Prediction precision of lighting con-elec tricity consumption in house 2000949 sumption in house 2000910

Forecasting Energy Consumption in Dwellings

Fridgil catwmptlon 2000949 houtty probablibM i f I I

middot -1 - _ L __ J _~_ middot _ _ J _ _ L __ ~I

I

E 1I1 - ~ - - -===~~-~~Iimiddot-- -- ~ ~

I

I I I I I It I I I I I bull I - - - - -r - - 1- -- r- - - - r --

I bull I I I

- - --- -- -- ~ - - - ~--~- - -f- - ~

I

==-=---- -----~-~~--- -~

Fig 13 Prediction precision of the refrigerator consumption in house 2000949

6 Conclusions

Predicting the energy consumption in dwellings is an essential part in the power management of the grid as the consumption in the residential sector represents a siguificant percentage in the total electricity demand The development of the smart grid is not possible without a good prediction of energy consumption The trend nowadays is to get the prediction of energy consumption not only at house level but at household appliance leveL

The prediction of energy consumption in housing is very dependent on inhabishytants behavior so a stochastic method for prediction has been presented in this paper The paper discusses about how to evaluate the precision of a predictor in the day +1 power management context Different basic predictors are presented and tested for the available historical data A relevant predictor is presented

Segmentation of data is done considering the patterns in energy consumption Also the historical data is divided according to the results of the k-means clustershying algorithm After testing the predictor on the new clustered data the precision of the predictor improves

Further work involves testing the proposed predictor for all the appliances in a house in order to decide the proper way for prediction at the equipment level

References

I Brooks 1 Lu E Reicher D Spirakis C Weihl B Demand Dispatch IEEE Powshyer and Energy Magazine 8(3) 20-29 (20 10) ISSN 1540-7977

2 Iliescu SS Fagarasan I Modem approaches in power system control In IEEE Int Conf on Automation Quali ty and testing Robotics Cluj voL I pp 41 -44 (2008)

3 Long Ha D Ploix S Zamai E Jacomino M Realtimes dynamic optimization for demand-side load management International Journal of Management Science and Engineering Management 3(4) 243-252 (2008) ISSN 1750-9653

264 N Arghira et al

4 Abras S Ploix S Pesty S Jacomino M A multi-agent design for a home automashytion system dedicated to power management In lF1P Conference on Artificial Intellishy

gence Applications and Innovations Greece (2007) 5 Jain A Salish B Clustering based Short Term Load Forecasting using Support Vec shy

tor Machines In IEEE PowerTech Bucharest (2009) 6 Jigoria-Oprea D Kilyeni S Dan F Electric energy forecast for residential users

Journal of Sustainable Energy TI(2) (2011) 7 Hawarah L Ploi S Jacomino M User Behavior Prediction in Energy Consumpshy

tion in Housing Using Bayesian Networks vol 113 pp 372-379 (2010) http www springerLink comdoil0loo7978-3-642-13208-7 _47

8 Basu K Guillame-Berty M Joumaa H Ploix S Crowley J Predicting home sershyvice demands from appliance usage data In International Conference on Information and Communication Technologies and Applications lCTA 201 1 USA (2011)

9 Popescu V Oprea L Costianu DR Transmission Network Capacity Enhancement by Special Protection Schemes In 18th Int ConL Control Systems and Computer Science Bucharest vol I pp 205-210 (2011) ISSN 2066-4451

10 GeWngs CW The smart grid enabling energy efficiency and demand response USA

(2009) ISBN 0-88 173-624-4 II Ipakchi A Al buych F Grid of the future IEEE Power and Energy Magazine 8(3)

20-29 (2009) doilOl I09MPE2008931384 ISSN 1540-777 12 National Energy Technology Laboratory A Vis ion for the M odem Grid (2007) 13 Arghira N Hossu D F1Ig~an I lliescu S S Costianu DR Modern SCADA

philosophy in power system operation - A survey Scientific Bulletin of University POLITEHNJCA Buchares t Series C Electrical Engineering 73(2) 153- 166 (2011)

14 Feinberg EA Genethliou D Load Forecasting Applied mathematics for restrucshy

tured electric power systems pp 269-285 (2005) http www Spr i ngerLinkcomdoi 1010070-387-23471-3

15 AI fares KH Nazeeruddin M Electric load forecasting literature survey and classishyfication of methods In ternational Journal of Systems Science 33( 1)23-34 (2002)

16 Arghira N Ploi S Fagar3ltjan I Iliescu SS Prediction of energy consumption in homes In 18th Int Conf Control Systems and Computer Science Bucharest vol 1 pp 211-217 (2011 ) ISSN 2066-4451

Modelling and Composed Recursive Model Free Control for the Anaerobic Digestion Process

2Haoping W an g l B oyko Kachev bull Yang Tian l

2 3Ivan Simeonov and Nicolai Christov

1 Automation School Nanjing University of Science and Technology 200 Xiao Ling Wei St 210094 Nanjing China

2 The StephanAngeloff Institute of Microbiology - Bulgarian Academy of Sciences bl 26 Acad G Bonchev St 1113 Sofia Bulgaria

3 LAGIS UMR eNRS 8219 Universite Lille1 Sciences et Technologies 59655 Villeneuve d Ascq France hpwangnjusteducn kalchevmicrobiobasbg

Abstract This paper preseots a modelling and new composite recursive model free controller for trajectory tracking and disturbance compensation for the Anaerobic Digestion Process of cattle dung The used model is on the basis of a fifth-order continuous anaerobic digestion model And the proposed controller comprises a recursive model free controller based stabilization component and a time delay control based compensation component with recursi ve calculation structure which does not require any knowledge o f the model parameters Computer simulation examples illustrate the performance and robustness of the proposed approach

Keywords Anaerobic digestion composed recursive controller piecewise continuous systems recursive model free controller

1 Introduction

EnvirolUnental problems (ex air and water pollution) and energy shortage are nowadays recognized worldwide issues Several possibilities are available to treat these difficulties from nowadays different treatment ways biological processes are surely among the most used sustainable and efficient systems Anaerobic digestion (AD) which is a biotechnological process is widely used in life sciences wastewater treatment and a promising method for solving energy shortage and ecological protection problems in agriculture and agro-industry [1 ] In such kind of processes usually camed out in Continuously Stirred Tank bio-Reactors (CSTR) the organic matter is de-polluted by microorganisms into biogas (mainly methane ClL and carbon dioxide CO2) and compost in the absence of oxygen [2] [3]

AD is a very unstable process with regard to the bioreactor operation This is due to the complicated interactions between different microbial species as well as to the

1 Dumltrache (Ed ) Adv in lntelligent Control Systems amp Computer Science AlSC 187 pp 265-278 springcrlinkcom copy Springer-Verlag Berlin Heidelberg 2013

Page 7: Forecasting Energy Consumption in Dwellings - Indesenindesen.ats.com.ro/rezultate/articole/upb/2013_scan_springer_book.pdf · Finite Element Method Magnetics : Documentation: FEMM

263 262 N Arghtra et al

5 Experiments and Discussion

This section presents the results for different electrical appliances in the house when the proposed predictor is used in comparison with the basic predictors will never consume and will consume continuousl y There are several services which were tested but this paper will show the results for appliances representashytive for their class the fridge as it consumes all the time the boiler as its consumption can be delayed and the lighting as it has a regular usage in a house

The prediction precision is computed as explained in subsection 41 for a slid shying time window between I and 364 days covering all the historical data avai lashyble The tests show that the prediction acts in a special manner depending on the type of the electrical appliance (16)

The prediction precision with the proposed predictor for the refrigerator (fig 13) is lower than the precision assuming permanent consumption for time winshydows higher than 2 days This implies that the prediction for the fridge should be done considering a short period of hi storical data (e g two weeks) in order to get a high precision This conclusion was expected since the energy consumption for this appliance is dependent on the season so a short period of time is significant for prediction

The pcrfonnance of prediction for the overall lighting consumption (figures 11) is hi gher than the performance of the basic predictors for the entire recorded data intervaL For the gas boiler (figure 12) a good precision is obtained for almost all the considered periods of historical data

As a general observation the maximum value for prediction accuracy is obshytained for a short period of historical data - almost in all studied cases the best precision was reached for one day of recorded data Using this result will decrease the computation time since the learning time is short

PoodidiM p Gas Solelt 2000949 011 Pr_ Prod_ 2OOOl 0 Tota si IggtIOIO II lt1gt Ii ~

~~--=- - - t- - - - - - - - - t shy

r r==~~bull J _ _ _ 1_ _ _ _1 __ bull J

bull __1 _ _ _ L __ __

1 ___ w

_ _ _ _ J _ 1__ _ __ _ _ - -1 shyi

~

~__ j _ __ _ _ J _ _ _ I _ _ _

Uj bull - ~ -~ ~~----~--- ~--- -~=~~ -- - shybull __ _ ~~ r __ _ _ bull - _ _ _ 1 _ _ _

j _ _

~I I ~ I J _ _-t~~~~-r-~~~middot~ ~+~~-~~~~~~~~~~ I 1

---- 1 ---- ~ --_~- _ 1IIfIot

-1 middotmiddot(1111

Fig 11 Prediction precision of the boiler Fig 12 Prediction precision of lighting con-elec tricity consumption in house 2000949 sumption in house 2000910

Forecasting Energy Consumption in Dwellings

Fridgil catwmptlon 2000949 houtty probablibM i f I I

middot -1 - _ L __ J _~_ middot _ _ J _ _ L __ ~I

I

E 1I1 - ~ - - -===~~-~~Iimiddot-- -- ~ ~

I

I I I I I It I I I I I bull I - - - - -r - - 1- -- r- - - - r --

I bull I I I

- - --- -- -- ~ - - - ~--~- - -f- - ~

I

==-=---- -----~-~~--- -~

Fig 13 Prediction precision of the refrigerator consumption in house 2000949

6 Conclusions

Predicting the energy consumption in dwellings is an essential part in the power management of the grid as the consumption in the residential sector represents a siguificant percentage in the total electricity demand The development of the smart grid is not possible without a good prediction of energy consumption The trend nowadays is to get the prediction of energy consumption not only at house level but at household appliance leveL

The prediction of energy consumption in housing is very dependent on inhabishytants behavior so a stochastic method for prediction has been presented in this paper The paper discusses about how to evaluate the precision of a predictor in the day +1 power management context Different basic predictors are presented and tested for the available historical data A relevant predictor is presented

Segmentation of data is done considering the patterns in energy consumption Also the historical data is divided according to the results of the k-means clustershying algorithm After testing the predictor on the new clustered data the precision of the predictor improves

Further work involves testing the proposed predictor for all the appliances in a house in order to decide the proper way for prediction at the equipment level

References

I Brooks 1 Lu E Reicher D Spirakis C Weihl B Demand Dispatch IEEE Powshyer and Energy Magazine 8(3) 20-29 (20 10) ISSN 1540-7977

2 Iliescu SS Fagarasan I Modem approaches in power system control In IEEE Int Conf on Automation Quali ty and testing Robotics Cluj voL I pp 41 -44 (2008)

3 Long Ha D Ploix S Zamai E Jacomino M Realtimes dynamic optimization for demand-side load management International Journal of Management Science and Engineering Management 3(4) 243-252 (2008) ISSN 1750-9653

264 N Arghira et al

4 Abras S Ploix S Pesty S Jacomino M A multi-agent design for a home automashytion system dedicated to power management In lF1P Conference on Artificial Intellishy

gence Applications and Innovations Greece (2007) 5 Jain A Salish B Clustering based Short Term Load Forecasting using Support Vec shy

tor Machines In IEEE PowerTech Bucharest (2009) 6 Jigoria-Oprea D Kilyeni S Dan F Electric energy forecast for residential users

Journal of Sustainable Energy TI(2) (2011) 7 Hawarah L Ploi S Jacomino M User Behavior Prediction in Energy Consumpshy

tion in Housing Using Bayesian Networks vol 113 pp 372-379 (2010) http www springerLink comdoil0loo7978-3-642-13208-7 _47

8 Basu K Guillame-Berty M Joumaa H Ploix S Crowley J Predicting home sershyvice demands from appliance usage data In International Conference on Information and Communication Technologies and Applications lCTA 201 1 USA (2011)

9 Popescu V Oprea L Costianu DR Transmission Network Capacity Enhancement by Special Protection Schemes In 18th Int ConL Control Systems and Computer Science Bucharest vol I pp 205-210 (2011) ISSN 2066-4451

10 GeWngs CW The smart grid enabling energy efficiency and demand response USA

(2009) ISBN 0-88 173-624-4 II Ipakchi A Al buych F Grid of the future IEEE Power and Energy Magazine 8(3)

20-29 (2009) doilOl I09MPE2008931384 ISSN 1540-777 12 National Energy Technology Laboratory A Vis ion for the M odem Grid (2007) 13 Arghira N Hossu D F1Ig~an I lliescu S S Costianu DR Modern SCADA

philosophy in power system operation - A survey Scientific Bulletin of University POLITEHNJCA Buchares t Series C Electrical Engineering 73(2) 153- 166 (2011)

14 Feinberg EA Genethliou D Load Forecasting Applied mathematics for restrucshy

tured electric power systems pp 269-285 (2005) http www Spr i ngerLinkcomdoi 1010070-387-23471-3

15 AI fares KH Nazeeruddin M Electric load forecasting literature survey and classishyfication of methods In ternational Journal of Systems Science 33( 1)23-34 (2002)

16 Arghira N Ploi S Fagar3ltjan I Iliescu SS Prediction of energy consumption in homes In 18th Int Conf Control Systems and Computer Science Bucharest vol 1 pp 211-217 (2011 ) ISSN 2066-4451

Modelling and Composed Recursive Model Free Control for the Anaerobic Digestion Process

2Haoping W an g l B oyko Kachev bull Yang Tian l

2 3Ivan Simeonov and Nicolai Christov

1 Automation School Nanjing University of Science and Technology 200 Xiao Ling Wei St 210094 Nanjing China

2 The StephanAngeloff Institute of Microbiology - Bulgarian Academy of Sciences bl 26 Acad G Bonchev St 1113 Sofia Bulgaria

3 LAGIS UMR eNRS 8219 Universite Lille1 Sciences et Technologies 59655 Villeneuve d Ascq France hpwangnjusteducn kalchevmicrobiobasbg

Abstract This paper preseots a modelling and new composite recursive model free controller for trajectory tracking and disturbance compensation for the Anaerobic Digestion Process of cattle dung The used model is on the basis of a fifth-order continuous anaerobic digestion model And the proposed controller comprises a recursive model free controller based stabilization component and a time delay control based compensation component with recursi ve calculation structure which does not require any knowledge o f the model parameters Computer simulation examples illustrate the performance and robustness of the proposed approach

Keywords Anaerobic digestion composed recursive controller piecewise continuous systems recursive model free controller

1 Introduction

EnvirolUnental problems (ex air and water pollution) and energy shortage are nowadays recognized worldwide issues Several possibilities are available to treat these difficulties from nowadays different treatment ways biological processes are surely among the most used sustainable and efficient systems Anaerobic digestion (AD) which is a biotechnological process is widely used in life sciences wastewater treatment and a promising method for solving energy shortage and ecological protection problems in agriculture and agro-industry [1 ] In such kind of processes usually camed out in Continuously Stirred Tank bio-Reactors (CSTR) the organic matter is de-polluted by microorganisms into biogas (mainly methane ClL and carbon dioxide CO2) and compost in the absence of oxygen [2] [3]

AD is a very unstable process with regard to the bioreactor operation This is due to the complicated interactions between different microbial species as well as to the

1 Dumltrache (Ed ) Adv in lntelligent Control Systems amp Computer Science AlSC 187 pp 265-278 springcrlinkcom copy Springer-Verlag Berlin Heidelberg 2013

Page 8: Forecasting Energy Consumption in Dwellings - Indesenindesen.ats.com.ro/rezultate/articole/upb/2013_scan_springer_book.pdf · Finite Element Method Magnetics : Documentation: FEMM

264 N Arghira et al

4 Abras S Ploix S Pesty S Jacomino M A multi-agent design for a home automashytion system dedicated to power management In lF1P Conference on Artificial Intellishy

gence Applications and Innovations Greece (2007) 5 Jain A Salish B Clustering based Short Term Load Forecasting using Support Vec shy

tor Machines In IEEE PowerTech Bucharest (2009) 6 Jigoria-Oprea D Kilyeni S Dan F Electric energy forecast for residential users

Journal of Sustainable Energy TI(2) (2011) 7 Hawarah L Ploi S Jacomino M User Behavior Prediction in Energy Consumpshy

tion in Housing Using Bayesian Networks vol 113 pp 372-379 (2010) http www springerLink comdoil0loo7978-3-642-13208-7 _47

8 Basu K Guillame-Berty M Joumaa H Ploix S Crowley J Predicting home sershyvice demands from appliance usage data In International Conference on Information and Communication Technologies and Applications lCTA 201 1 USA (2011)

9 Popescu V Oprea L Costianu DR Transmission Network Capacity Enhancement by Special Protection Schemes In 18th Int ConL Control Systems and Computer Science Bucharest vol I pp 205-210 (2011) ISSN 2066-4451

10 GeWngs CW The smart grid enabling energy efficiency and demand response USA

(2009) ISBN 0-88 173-624-4 II Ipakchi A Al buych F Grid of the future IEEE Power and Energy Magazine 8(3)

20-29 (2009) doilOl I09MPE2008931384 ISSN 1540-777 12 National Energy Technology Laboratory A Vis ion for the M odem Grid (2007) 13 Arghira N Hossu D F1Ig~an I lliescu S S Costianu DR Modern SCADA

philosophy in power system operation - A survey Scientific Bulletin of University POLITEHNJCA Buchares t Series C Electrical Engineering 73(2) 153- 166 (2011)

14 Feinberg EA Genethliou D Load Forecasting Applied mathematics for restrucshy

tured electric power systems pp 269-285 (2005) http www Spr i ngerLinkcomdoi 1010070-387-23471-3

15 AI fares KH Nazeeruddin M Electric load forecasting literature survey and classishyfication of methods In ternational Journal of Systems Science 33( 1)23-34 (2002)

16 Arghira N Ploi S Fagar3ltjan I Iliescu SS Prediction of energy consumption in homes In 18th Int Conf Control Systems and Computer Science Bucharest vol 1 pp 211-217 (2011 ) ISSN 2066-4451

Modelling and Composed Recursive Model Free Control for the Anaerobic Digestion Process

2Haoping W an g l B oyko Kachev bull Yang Tian l

2 3Ivan Simeonov and Nicolai Christov

1 Automation School Nanjing University of Science and Technology 200 Xiao Ling Wei St 210094 Nanjing China

2 The StephanAngeloff Institute of Microbiology - Bulgarian Academy of Sciences bl 26 Acad G Bonchev St 1113 Sofia Bulgaria

3 LAGIS UMR eNRS 8219 Universite Lille1 Sciences et Technologies 59655 Villeneuve d Ascq France hpwangnjusteducn kalchevmicrobiobasbg

Abstract This paper preseots a modelling and new composite recursive model free controller for trajectory tracking and disturbance compensation for the Anaerobic Digestion Process of cattle dung The used model is on the basis of a fifth-order continuous anaerobic digestion model And the proposed controller comprises a recursive model free controller based stabilization component and a time delay control based compensation component with recursi ve calculation structure which does not require any knowledge o f the model parameters Computer simulation examples illustrate the performance and robustness of the proposed approach

Keywords Anaerobic digestion composed recursive controller piecewise continuous systems recursive model free controller

1 Introduction

EnvirolUnental problems (ex air and water pollution) and energy shortage are nowadays recognized worldwide issues Several possibilities are available to treat these difficulties from nowadays different treatment ways biological processes are surely among the most used sustainable and efficient systems Anaerobic digestion (AD) which is a biotechnological process is widely used in life sciences wastewater treatment and a promising method for solving energy shortage and ecological protection problems in agriculture and agro-industry [1 ] In such kind of processes usually camed out in Continuously Stirred Tank bio-Reactors (CSTR) the organic matter is de-polluted by microorganisms into biogas (mainly methane ClL and carbon dioxide CO2) and compost in the absence of oxygen [2] [3]

AD is a very unstable process with regard to the bioreactor operation This is due to the complicated interactions between different microbial species as well as to the

1 Dumltrache (Ed ) Adv in lntelligent Control Systems amp Computer Science AlSC 187 pp 265-278 springcrlinkcom copy Springer-Verlag Berlin Heidelberg 2013