Application of artificial neural networks to historical data analysis for short-term electric load forecasting

The paper illustrates two different Artificial Neural Networks (ANN) architectures for electric Short-Term Load Forecasting (STLF). Two multi-layer perceptron ANN using the back-propagation learning algorithm have been implemented which provide different, although complementary, forecasting approaches (static and dynamic). In order to test the potentialities of the architectures implemented, the ANN have been applied to the Short-Term Forecasting of Italian hourly electric load. The importance of this load (peak demands up to about 38 000 MW) requires tools for STLF which must be as more accurate and precise as possible. This fact has imposed the adoprequires tools for STLF which must be as more accurate and precise as possible. This fact has imposed the adoption of some algorithmic enhancements to the basic back-propagation algorithm formulation. Since an adequate formulation of the influence exerted on hourly electric load by the main meteorological and climatic factors is not known at present, the data set used for ANN training phase has concerned only historical series of electric hourly demand. The paper illustrates the two ANN architectures as well as the computational platforms used for implementation. Finally, some results obtained from the application of the two ANN to the short-term forecasting of Italian electric load relevant to three different weeks of the year 1993 are comparatively reported.