A short term load-forecasting (STLF) program that uses an integrated artificial neural network (ANN) approach in forecasting load is discussed. This method was used to overcome the drawbacks of traditional methods such as heavy computational time, large amount of memory space, and explicit relationships between different variables. It was found that the forecasted load obtained from the integrated architecture is better than that from the single ANN.

Shanti K. Swarup

Department of Electrical Engineering

Atmospheric humidity

Computer simulation

Electric utilities

Neural Networks

Regression analysis

scheduling

Thermal effects

time series analysis

Wind effects

AUTOMATIC GENERATION CONTROL (AGC)

SHORT TERM LOAD FORECASTING (STLF)

Electric load forecasting

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Integrated ANN approach to forecast load

IEEE Computer Applications in Power

An integrated Artificial Neural Network (ANN) approach to Short-Term Load Forecasting (STLF) is proposed in this paper. Four modules consisting of the Basic ANN, Peak and Valley ANN, Averager and Forecaster and Adaptive Combiner form the integrated method for load forecasting. The Basic ANN uses the historical data of load and temperature to predict the next 24h load, while the Peak and Valley ANN uses the past peak and valley data of load and temperatures, respectively. The Averager captures the average variation of the load from the previous load behaviour, while the adaptive combiner uses the weighted combination of outputs from the Basic ANN and the Forecaster, to forecast the final load. The regression based and time series methods are conceptually incorporated into the ANN to obtain an integrated load forecasting approach. © 2004 Elsevier B.V. All rights reserved.

Electric Power Systems Research

Effect of temperature on short term load forecasting using an integrated ANN

Integrated ANN: Approach to forecast load

Journal	IEEE Computer Applications in Power
ISSN	08950156
Open Access	No