Short term forecasting using neural network approach

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.<
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