Past Work

G-1 GDesired O/PDesired O/P

• Design and Simulation of Three Link Robot….(at Indian Space Research Satellite Centre, Bangalore, India)

• Load flow study of a Nuclear Power Plant….(at Rajasthan Atomic Power Station, Kota, Rajasthan, India).

• To Design and study Linear Induction Motor (LIM) for the Magnetic Levitated Vehicle….B.Tech Thesis work… (University Gold Medal for securing First Class First Position in the University).

• Temperature Control System using ANN.

• Short Term Load Forecasting Using Artificial Neural Network…M.Tech Thesis work.

• Short Term Load Forecasting Using Fuzzy Neural Network….(follow up of the earlier work).

SHORT TERM LOAD FORECASTING USING ANN

What’s Load Forecasting?

• Tell the Future!• Short-term load forecasting (STLF) is for

hour to hour forecasting and important to daily maintaining of power plant

• A STLF forecaster calculates the estimated load for each hours of the day, the daily peak load, or the daily or weekly energy generation.

Taxonomy of Load Forecasting

• Spatial forecasting : forecasting future load distribution in a special region, such as a state, a region, or the whole country.

• Temporal forecasting is dealing with forecasting load for a specific supplier or collection of consumers in future hours, days, months, or even years.

Taxonomy of LoadForecasting (Cont)

• Temporal forecasting:

Long-term load forecasting (LTLF): mainly for system planning. Typically covers a period of 10 to 20 years.

Medium-term load forecasting (MTLF): mainly for the scheduling of fuel supplies and maintenance. Usually covers a few weeks.

Short-term load forecasting (STLF): for the day-today operation and scheduling of the power system.

WHY Short-term load forecasting

• An central problem in the operation and planning of electrical power generation.

• To minimize the operating cost, electric supplier will use forecasted load to control the number of running generator unit.

• STLF is important to supplier because they canuse the forecasted load to control the number ofgenerators in operation

shut up some unit when forecasted load is low

start up of new unit when forecasted load is high.

(HOW) Forecasting Methods

• Expert Judgments

• Linear Models• Linear Regression• Time Series Approach

• Nonlinear Models• Artificial Neural Networks• Nonlinear Regression• Fuzzy Approach• Bayesian Network Approach

Source-RTE France

Week DaysWeekend

Source-RTE France

First Peak

Second Peak

Daily Consumption

Mainly Industrial Load Residential + Commercial Load

Night Off Peak

Afternoon Off Peak

Determining factors• Calendar

Seasonal variationDaily variationWeekly CycleHolidays

• Economical or environmental• Weather

TemperatureCloud cover or sunshineHumidity

• Unforeseeable random event

L(n) = f( past(L), Calendar, Weather,Other)

Why…. Neural Network?• Absence of the Mathematical Model of Load

• The Load is function of a lot of factorsL(n) = f (past(L), Calendar, Weather, Other)

• f is complex and unknown, and relation is non linear.

• Traditional computationally economic approaches, such as regression and interpolation, may not give sufficient accurate result. Conversely, complex algorithmic methods with heavy computational burden can converge slowly and may diverge in certain cases, thus, not suitable for real time applications.

• Use Black Box….i.e. Neural network to approximate f !

Major Impediments in Building ANN

• Limited ability to extrapolate modelled relationship beyond the training data domain.

• Results depend on the neural network designe.g. Number of the layers, Size of the hidden layer, Number of the inputs in the input layer etc. We do not have any clear information in this regard.

• It is a Black Box…in the sense that the internal layers of the neural network are always opaque to the user, the mapping rules are thus difficult to understand.

Neural Network Architecture

Input layer

Hidden layer

Output layer

1( )1

( ) ( ) (1 ( ) )

xf xe

f x f x f x

−=−

′ = −

( )k kf w x∑

Forecasted Load

STLF Using ANN (1st Approach)

0 5 10 15 20 255.5

6

6.5

7

7.5

8x 10

4

Hour of the day

MW

Actual Load

data1

data2

664007200023:00

634006920022:00

671007200021:00

705007490020:00

737007720019:00

710007600018:00

663007200017:00

667007170016:00

682007380015:00

704007640014:00

719007890013:00

721007820012:00

721007830011:00

727007900010:00

730007790009:00

730007610008:00

674007100007:00

603006690006:00

570006410005:00

571006440004:00

590006650003:00

617006890002:00

608006730001:00

642007050000:00

20/01/200623/12/2005Hour

LOAD IN MEGAWATT

Source: RTE France

0 5 10 15 20 25-4000

-2000

0

2000

4000

6000

8000

Hour of the Day

Incr

emen

t in

Load

Comparison of the load increment

data1data2

-2411-200023:00

3000280022:00

-3700-280021:00

-3400-290020:00

-3200-230019:00

2700120018:00

4700400017:00

-40030016:00

-1500-210015:00

-2200-260014:00

-1500-250013:00

-20070012:00

0-10011:00

-600-70010:00

-300110009:00

0180008:00

5600510007:00

7100410006:00

3300280005:00

-100-30004:00

-1900-210003:00

-2700-240002:00

900160001:00

-3400-320000:00

20/01/200623/12/2005Hour

Increment in MWatt

Source: RTE France

STLF Using ANN (Proposed Approach)

Results (Using both Approaches)

Conclusion• The results obtained using the proposed approach are

closer to the actual load, thus, strengthening the idea of proposed approach.

• It was observed that the algorithm of the second approach was more robust as compared to the first approach.

• It is less sensitive to the requirement of having training data representative of the entire spectrum of possible load and weather conditions.

STLF Using Fuzzy Neural Network

The input I11, I12 has five membership functions each. I11represents the load increment at the kth hour and I12represents the forecasted load increment at the same hour. The forecasted load increment was obtained using the traditional ANN.

Results

““The best way to predict The best way to predict the future is to invent it”the future is to invent it”

THANKS