Short term forecasting using neural network approach

9 December 2002

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

p. 12-16
https://doi.org/10.1109/ann.1991.213489

Abstract

One of the major problems facing the electric utility is the unknown future demand of electricity, which needs to be estimated correctly. The authors describe a neural network approach to improve short term forecasts of electricity demand. This network is based on the nonstatistical neural paradigm, back propagation, which is found to be effective for forecasting of electrical load. The load is decomposed into a daily pattern reflecting the difference in activity level during the day, a weekly pattern representing the day-of-the week effect on load, a trend component concerning the seasonal growth and a weather component reflecting the deviations in load due to weather fluctuations. The performance of this network has been compared with some commonly used conventional smoothing methods, and stochastic methods in order to demonstrate the superiority of this approach.

Keywords

This publication has 8 references indexed in Scilit:

Stock market prediction system with modular neural networks
Published by Institute of Electrical and Electronics Engineers (IEEE) ,1990
Stock price pattern recognition-a recurrent neural network approach
Published by Institute of Electrical and Electronics Engineers (IEEE) ,1990
ALFA: automated load forecasting assistant
IEEE Transactions on Power Systems, 1988
An expert system based algorithm for short term load forecast
IEEE Transactions on Power Systems, 1988
The Time Series Approach to Short Term Load Forecasting
IEEE Transactions on Power Systems, 1987
Short term load forecasting using multiple correlation models
IEEE Transactions on Power Apparatus and Systems, 1975
A Stochastic Approach to Peak Power-Demand Forecasting in Electric Utility Systems
IEEE Transactions on Power Apparatus and Systems, 1971
Short-Term Load Forecasting Using General Exponential Smoothing
IEEE Transactions on Power Apparatus and Systems, 1971