Abstract— This paper combined artificial neural network

and regression modeling methods to predict electrical load.

We propose an approach for specific day, week and/or month

load forecasting for electrical companies taking into account

the historical load. Therefore, a modified technique, based on

artificial neural network (ANN) combined with linear

regression, is applied on the KSA electrical network dependent

on its historical data to predict the electrical load demand

forecasting up to year 2020. This technique was compared

with extrapolation of trend curves as a traditional method

(Linear regression models). Application results show that the

proposed method is feasible and effective. The application of

neural networks prediction shows the capability and the

efficiently of the proposed techniques to obtain the predicting

load demand up to year 2020.

Keywords— Electrical load, time series prediction, neural

networks, multiple regressions.

I. INTRODUCTION

OAD forecasting problem is receiving great and growing

attention as being an important and primary tool in power

system planning and operation. Importance of load forecasting

becomes more significant in developing countries with high

growth rate such as KSA. Owing to the importance of load

forecasting, various models have been proposed for the short-

term load forecasting in the last decades, such as regression-

based methods [1-2], Box Jenkins model [3], time-series

approaches [4], Kalman filters [5], expert system techniques

[6], neural network models [7, 8, 9], fuzzy logic [10], and

fuzzy-neural network structures [11]. Recently, applications of

hybrid ANNs model with statistical methods or other

intelligent approaches have received attentions. Examples of

such systems are hybrids with Bayesian inference [12], self-

organizing map [13], wavelet transform [14], and particle

swarm optimization [15].

A price forecasting system for electric market participants

Saeed Badran is with Electrical Engineering Department

Faculty of Engineering, Al-Baha University, 15456 Al-Baha, Kingdom of

Saudi Arabia, (corresponding author to provide phone: +966504588910; fax:

+96670554557; e-mail: badran07@gmail.com).

Ossama Abouelatta, was Production Engineering and Mechanical Design

Department, Faculty of Engineering, Mansoura University, 35516 Mansoura,

Egypt. He is now with the Department of Mechanical Engineering, Faculty of

Engineering, Al-Baha University, 15456 Al-Baha, Kingdom of Saudi Arabia,

(e-mail: abouelatta@mans.edu.eg).

was proposed by Lin et al. [9], to reduce the risk of price

volatility. A hybrid neural network model based on self-

organizing map has been presented by Amin-Naseri and

Soroush [13], for daily electrical peak load forecasting. The

results proved the superiority and effectiveness of their

proposed hybrid model. The results showed that the suggested

clustering approach significantly improves the forecasting

results on regression analysis too. Xiaoxing and Caixin et al.

[16] proposed a dynamic and intelligent data cleaning model

based on data mining theory. The rapid and dynamic

performance of the model makes it suitable for real time

calculation, and the efficiency and accuracy of the model is

proved by test results of electrical load data analysis.

The second kind of prediction is known as medium-term

forecasting. There are several methods of medium-term load

forecasting such as time-series approaches [17], neural

network models [18], and Fourier series approach [19].

Almeshaiei and Soltan [1], presented a pragmatic methodology

that can be used as a guide to construct electric power load

forecasting models. Some results are reported to guide

forecasting future needs of this network. A new technique is

proposed by Abu-Shikhah and Elkarmi [20] that uses hourly

loads of successive years to predict hourly loads and peak load

for the next selected time span. The proposed method can be

implemented to the hourly loads of any power system.

Pedregal and Trapero [21] developed a general multi-rate

methodology in order to forecast optimally load demand series

sampled at an hourly rate for a mid-term horizon. The results

showed that this method produces a notable reduction on the

prediction error and its variability.

The third kind of prediction is known as long-term forecasting.

The major methods for long-term load forecasting are time-

series approach [22], intelligent methods [23], neuro-fuzzy

approach [24], dynamic simulation theory [25], hierarchical

neural model [26], and support vector machines [27]. Their

results demonstrated the effectiveness of the methodology

handling this type of problems. A mathematical method is

proposed by Filik et al. [2], for modeling and forecasting

electric energy demand in which it enables the possibility of

making short-, medium-, and long-term hourly load forecasting

within a single framework. Abou El-Ela et al. [28] introduced

what so called a proposed optimization technique, for

predicting the peak load demand and planning of transmission

line systems. The application on a selected network showed

the capability and the efficiently of the proposed techniques to

Neural Network Integrated with Regression

Methods to Forecast Electrical Load

Saeed Badran, and Ossama Abouelatta

L

International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012

60

obtain the predicting peak load demand and the optimal

planning of transmission lines of the selected network up to

year 2017. The objective of this study is aimed to develop a

generalized method for precise load forecasting within the

horizons of short-, medium-, and long-terms, all in hourly

accuracy.

II. ELECTRIC LOAD FORECASTING

Load forecasting problem is receiving great and growing

attention as being an important and primary tool in power

system planning and operation. Importance of load forecasting

becomes more significant in developing countries such as

KSA. The accuracy of load forecasting is crucial due to its

direct influence on generation planning, and for its economic

impacts. In the modern system operation, the advance

technology of computer has been extensively applied in the

field of power system planning, monitoring and control.

Nowadays, most operation of electric utility utilizes the energy

management system. The traditional way for power engineers

to perform the system analysis is to use mathematical model.

This model is usually difficult especially when dealing with

large systems. Handling these problems with mathematical

model is therefore not realistic.

Due to the ability of ANN model to perform pattern

recognition, prediction and optimization in a fast and efficient

manner, it has become one of the main topics of interest for

many researchers to investigate its application in many fields

including power system. Some examples of utilizing ANN in

power system applications are: Load forecasting, fault

classification, power system assessment, real time harmonic

evaluation, power factor correction, load scheduling, design of

transmission lines, and power system planning.

Load forecast has been an attractive research topic for many

decades and in many countries all over the world, especially in

fast developing countries with higher load growth rate. Load

forecast can be generally classified into four categories based

on the forecasting time, Table I.

III. ARTIFICIAL NEURAL NETWORKS

An Artificial Neural Network (ANN) is a computational model

that attempts to account for the parallel nature of the human

brain. Specifically, it is a network of highly interconnecting

processing elements (neurons) operating in parallel, Fig. 1. An

ANN can be used to solve problems involving complex

relationships between variables. The particular type of ANN

used in this study is a supervised one, wherein the observation

(target) is specified, and the ANN is trained to minimize the

error between the ANN output and the target, resulting in an

optimal solution (assuming the global minimum is reached.)

This is accomplished by adjusting the connections between the

elements, which involves an adjustment to the weights

(w11,1…w

11,z). In theory, this adjustment process can be viewed

as a form of ‘learning’. Thus, the ANN is considered to be a

form of artificial intelligence. ANNs were selected for this

study owing to their ability to model non-linear relationships.

The relationship between the input and output parameters in

this study is highly non-linear.

Inputs Hidden Layer Output Layer

. .

. .

P1

w11,z b

1 b

2

a1 n

1 w2

1,1 n2 a

2

w11,1 P2

P3

Pz

a1=f1(w1p+b1) A2=f2(w2

p a1+b2)

f1 f2

Fig. 1 A 2-layer ANN with multiple inputs and single hidden and

output neurons

IV. NETWORK DESIGN

Forecasting of electricity demand has become one of the major

research fields in electrical engineering. The supply industry

requires forecasts with lead times that range from the short

term (a few minutes, hours, or days ahead) to the long term (up

to 20 years ahead). Load forecasting is however a difficult task

because of complexity of load series that have high non-

linearity relation among variables and load exhibits several

levels of seasonality. In addition, there are many important

exogenous variables that must be considered, especially

weather-related variables. One of the promising tools to

achieve a good load forecasting is the ANN which achieved

great success in dealing with non-linear problems such as load

forecasting problem.

A. Data analysis

The power system has a complicated behavior and the load

is influenced by many factors. The energy consumption served

by the utility can be generally categorized into industrial,

commercial and residential loads. The demand of commercial

and industrial activities basically relies on the level of

production, which is somewhat steady and relatively easy to be

estimated.

There are many factors that affect load changes. They can

be generally classified as calendar, weather and random

factors. Fig. 2 shows hourly load variations from 1/1/2006 to

31/12/2006 of Jeddah city in KSA. Fig. 2(a) shows the hourly

TABLE I

CLASSIFICATION OF LOAD FORECAST BASED ON THE FORECASTING TIME

Load

forecast Period Importance

Long 1-10 Years

To calculate and to allocate the

required future capacity.

To plan for new power stations to

face customer requirements.

Plays an essential role to determine

future budget.

Medium 1-week to few

months

Fuel allocation and maintenance

schedules.

Short 1-hour to 1-week

Accurate for power system

operation.

To evaluate economic dispatch,

hydro-thermal co-ordination, unit

commitment, transaction.

To analysis system security among

other mandatory function.

Very short 1-minute - 1- hour Energy management systems (EMS).

International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012

61

electric load variation whereas Fig. 2(b) shows the same data

after applying moving average filter to clear the presentation

of data. It can be observed that the load during summer is

higher than that in other seasons. Seasonal variation is mainly

due to temperature variance.

Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec.1000

2000

3000

4000

5000

Time (Month)

Ele

ctric

load

(M

W)

2006

(a) Hourly load over.

Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec.1000

2000

3000

4000

5000

Time (Month)

Ele

ctric

load

(M

W)

2006

(b) Hourly load over one year after applying moving average filter.

Fig. 2 Hourly load over one year (2006)

A detailed (zoomed) 1-D plot of one day period of years

from 2002 to 2006 is shown in Fig. 3. It must be noted that for

a lower complexity model, it is better to take the starting hour

of the day as 8:00 AM, which typically corresponds to the

minimum demand hour. Comparing Fig. 3(a) and (b), one can

see that the daily load shown in Fig. 3(b) is more useful in

terms of providing a simpler model. This strategy was already

applied in presenting the mesh plot of Fig. 4.

Load is generally higher during weekdays because there are

more social activities. In KSA as any Islamic country,

weekend is Friday and many of private sector and

governmental institutions consider Thursday and Friday as

weekends. As a result, the weekly load curve will be

completely different between Islamic countries and European

countries. Fig. 5(a) plots hourly load data for one week of

years from 2002 to 2006 in Jeddah, KSA. Fig. 5(b) plots

hourly load data for one week of years from 1988 to 2006 in

Jeddah, KSA.

B. Data preparations

Successful operation of load forecasters using ANN requires

an appropriate training data set and training algorithm. The

training data set should cover all ranges of the input patterns

sufficiently to provide the network knowledge to recognize

and generalize the relations among the variables in the

problem. In this work, a historical data from the city of Jeddah

in KSA from 1/1/1988 to 31/12/2006 were used.

C. Data preparations

Load forecasting is important for energy suppliers, financial

institutions, and other participants in electric energy

generation, transmission, distribution, and markets. The three

load forecasting types, which are short-, medium-, and long-

term, are very important for power planning and operation.

0.0 4.0 8.0 12.0 16.0 20.0 24.01000

1200

1400

1600

1800

2000

2200

2400

2600

2800

Time (Hours)

Ele

ctr

ic load (

MW

)

2002

2003

2004

2005

2006

(a) The day assumed to start mid-night.

8.0 12.0 16.0 20.0 24.0 4.0 8.01000

1200

1400

1600

1800

2000

2200

2400

2600

2800

Time (Hours)

Ele

ctr

ic load (

MW

)

2002

2003

2004

2005

2006

(b) The day assumed to start 8:00 AM.

Fig. 3 Hourly load data in 1-D time plot for one day of years from

2002 to 2006

05

1015

2025

1980

1990

2000

2010500

1000

1500

2000

2500

3000

Time (Hours)Year

Ele

ctr

ic load (

MW

)

(a) The day assumed to start mid-night.

8.0 12.0 16.0 20.0 24.0 4.0 8.0

1980

1990

2000

2010500

1000

1500

2000

2500

3000

Time (Hours)Year

Ele

ctr

ic load (

MW

)

(b) The day assumed to start 8:00 AM.

Fig. 4 2-D representation of hourly consecutively load data of years

from 1998 to 2006 for one day

International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012

62

Fri Sat Sun Mon Tue Wen Thr 1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

3000

Time (Hours)

Ele

ctr

ic load (

MW

)

2002

2003

2004

2005

2006

(a) 1-D representation of hourly consecutively load data for one week

of years from 2002 to 2006.

FriSat

SunMon

TueWen

Thr

1980

1990

2000

20100

500

1000

1500

2000

2500

3000

Time (Hours)Year

Ele

ctr

ic load (

MW

)

(b) 2-D representation of hourly consecutively load data for one week

of years from 1998 to 2006.

Fig. 5 Weekly cycle of load changing characteristics.

In neural network, learning, which extracts information from

the input data, is a crucial step that is badly affected through

the selection of initial weights and the stopping criteria of

learning. If a well-designed neural network is poorly trained,

the weight values will not be close to their optimum and the

performance of the neural network will suffer. In general,

initial weight is implemented with a random number generator

that provides a random value. To stop the training process, we

could either limit the number of iterations or set an acceptable

error level for the training phase.

The training and validation procedures for specific network

architectures were repeated in order to handle uncertainties of

the initial weights and stopping criteria. In the preliminary

investigation it was found that about 300 trials were enough to

find the best result. The performance efficiencies of each trial

were recorded and compared.

V. ELECTRIC LOAD FORECASTING

This work provides a unified approach that enables the

“hourly” resolution property for all of the mentioned forecast

ranges. The proposed method consists of a nested combination

of two methods for modeling and forecasting electric loads.

The two methods are: neural network and linear regression

models. The procedure of work could be summarized as

follows:

1. A neural network was applied using electric load data

from year 2002 to year 2006 to predict hourly load for one

day, one week, one month and one year. Fig. 6 show the

regression output of neural network training regression.

2. A linear regression models were derived for all cases (one

day, one week, one month and one year). An example for the

estimated maximum, minimum and average values are shown

in Fig. 7.

3. Then, the electric load was calculated as the predicted

neural network (Step 1) shifted by the predicted average value

calculated using (Step 2). Fig. 8 shows a graphical user

interface used to predict electric load.

500 1000 1500 2000 2500

500

1000

1500

2000

2500

Target

Ou

tpu

t ~

= 0

.91

*Ta

rge

t +

1.1

e+

00

2

Training: R=0.95568

Data

Fit

Y = T

500 1000 1500 2000 2500

500

1000

1500

2000

2500

Target

Ou

tpu

t ~

= 0

.91

*Ta

rge

t +

1e

+0

02

Validation: R=0.95217

Data

Fit

Y = T

500 1000 1500 2000 2500

500

1000

1500

2000

2500

Target

Ou

tpu

t ~

= 0

.9*T

arg

et

+ 1

.4e

+0

02

Test: R=0.93722

Data

Fit

Y = T

500 1000 1500 2000 2500

500

1000

1500

2000

2500

Target

Ou

tpu

t ~

= 0

.91

*Ta

rge

t +

1.1

e+

00

2

All: R=0.95238

Data

Fit

Y = T

Fig. 6 Neural network training regression plot.

Fig. 7 Maximum, average and minimum electric load from year 1988

to year 2006 during one working day.

International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012

63

Fig. 8 Graphical user interface of Electric load forecaster.

There is no guarantee that coefficients which are close to

optimal values will be found during the learning phase even

though the number of iterations is capped at a predefined

value. Therefore, the performances of the proposed models are

measured with four efficiency terms. Each term is estimated

from the predicted values of the model and the measured

discharges (targets). The accuracy of the proposed method is

tested using hourly actual load values for the years 1988–2006.

The forecasting results are obtained for the proposed model

variations and different years in terms of mean absolute

percentage Error (MAPE) and correlation coefficient (R),

whose definitions are given in (1) and (2), respectively.

Overall, the model responses are more precise if MAPE and R

are found to be close to 0 and 1, respectively.

N

i a

af

L

LL

NMAPE

1

1 (1)

2

1

2

1

2

1

)(

)()(

N

i

af

N

i

af

N

i

af

LL

LLLL

R (2)

where: N = Number of observations

Lf = Forecasted load (MW)

La = Actual load (MW)

Each model will be checked by two types of error to

guarantee the maximum accuracy and to ensure that the

forecasted load is near as possible to the actual load. This will

add more complications to the problem but in the same time it

adds more guarantee for the forecasting accuracy. Table II lists

MAPE and R for hourly loads of years between 1988 and 2006

for linear regression and neural network models. Comparing

the average MAPE and R for daily loads of years between

1988 and 2006, it was found that these values are less in case

of neural network than those resulting from linear regression

method.

VI. ELECTRIC LOAD FORECASTING

One of the primary tasks of an electric utility is to accurately

predict load requirements at all times. Results obtained from

load forecasting process are used in planning and operation.

Neural Network can learn to approximate any function just

by using example data that is representative of the desired task.

They are model free estimators, which are capable of solving

complex problem based on the presentation of a large number

of training data. Neural Networks estimate a function without

mathematical description of how the outputs functionally

depend on the inputs. They represent a good approach that is

potentially robust and fault tolerant. In this work, an electric

forecasting method based on neural network integrated with

simple linear regression model was implemented using

MATLAB. The system performs better results than some other

systems. The accuracy can further be improved if we take

more than one factor (calendar, temperature, humidity and

random factors) as input, which is large enough to incorporate

all the effects which can be quantified.

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TABLE II

MAPE AND R FOR HOURLY LOADS OF YEARS BETWEEN 1988 AND 2006 IN

CASE OF LINEAR REGRESSION MODEL.

Year MAPE R

Reg. NN % Imp. Reg. NN % Imp.

1988 14.32 2.44 82.97 0.88 0.94 5.88

1989 14.35 3.38 76.47 0.88 0.95 7.76

1990 14.01 3.11 77.81 0.88 0.94 5.67

1991 14.03 3.28 76.64 0.88 0.94 5.76

1992 13.92 3.14 77.42 0.89 0.94 5.54

1993 13.39 3.33 75.10 0.90 0.95 5.39

1994 13.38 5.06 62.19 0.90 0.93 3.54

1995 13.04 4.13 68.31 0.91 0.95 4.84

1996 12.59 4.15 67.02 0.91 0.93 2.15

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1998 12.12 3.89 67.93 0.92 0.93 0.97

1999 12.13 5.27 56.56 0.92 0.88 -4.42

2000 12.62 4.94 60.87 0.92 0.91 -0.22

2001 11.98 4.58 61.80 0.92 0.93 1.71

2002 12.14 4.57 62.37 0.91 0.94 2.46

2003 12.15 5.89 51.58 0.91 0.95 4.41

2004 12.09 5.37 55.55 0.91 0.94 2.46

2005 11.58 7.58 34.55 0.91 0.96 5.40

2006 11.84 8.04 32.14 0.92 0.95 3.88

Ave. 12.85 4.58 63.56 0.90 0.94 3.38

International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012

64

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Saeed M. Badran joined the Department of

Electrical Engineering, Faculty of Engineering,

Albaha University, Albaha, KSA in 2009. He is

working as the dean Graduate Studies, Albaha

University and the dean of faculty of

engineering at the same university. Dr. Badran

graduated from the Electrical Engineering

Department of Bridgeport University,

Connecticut State, USA with B.Sc. in 1982. He

graduated from the Electrical Engineering

Department of Bridgeport University,

Connecticut State, with M.Sc. M.Sc. in 1983

and from the Biomedical Engineering and Systems Department, Toledo

University, Ohio state, with M.Sc. in 1986. He moved to West Virginia

University in USA and obtained his Ph.D. in Electrical Engineering in 1990..

In 1403 H, he joined Faculty of Applied Science and Engineering,

University of Umm Al-Qura, Makkah as a lecturer. Therefore, he joined again

to West Virginia University in USA as an assistant research in 1988-1989. He

worked in Electrical and Computer Engineering, University of Umm Al-Qura,

Makkah, in 1411 H. Dr. Badran joined King Fahd University of Petroleum &

Minerals as a visitor Professor in 1416 H – 1418 H. In 1997, he worked as a

consultant at Aramco, KSA.

Dr. Badran came back to Electrical Engineering and Computer

Department, University of Umm Al-Qura as an associate professor in 1420H

and head of the department from 1420H to 1422 H. He was at Putra

University, Malaysia, as a visitor Professor from 1429 H to 1430 H.

Ossama B. Abouelatta joined the Department

of Mechanical Engineering, Faculty of

Engineering, Albaha University, Albaha, KSA

in 2008. He graduated from the Production

Engineering and Mechanical Design

Department of Mansoura University with B.Sc.

and M.Sc. in 1986 and 1991, respectively. He

moved to Czech Technical University in Prague

(Czech Republic) in March 1996 and obtained

his Ph.D. in Manufacturing Engineering in

2000. In 1999, he joined Center for Precision

Technologies, University of Huddersfield, UK

as a visiting scholar, where he was responsible

for the extraction of critical points of the

surface topography..

He joined Production Engineering and Mechanical Design Department,

Faculty of Engineering, Mansoura University, Egypt as an demonstrator in

1986, as an assistant lecturer in 1992, as an assistant professor in 2000 and

finally as an associate professor in 2006. In 2007, he joined the Higher Delta

Institute for Engineering and Technology, Mansoura, Egypt. He taught many

under- and post-graduate mechanical engineering courses as metrology,

measurements, computer application and simulation, and manufacturing

engineering.

Dr. Abouelatta published more than sixty papers in refereed journals and

conferences. His present research interests include, surface characterization,

computer aided measurements and simulation, and biomedical applications

and measurements. He is a member of the International Committee on

Measurements and Instrumentation (ICMI) and an International Scientific

Committee of the International Symposium on Measurement Technology and

Intelligent Instruments (ISMTII) conference.

International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012

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