A NEURAL-NET BASED SHORT-TERM LOAD FORECASTING USING MOVING WINDOW PROCEDURE

An improved neural-net approach based on a combined unsupervised/super vised learning concept is proposed. A 'moving window' procedure is app lied to the most recent load and weather information for creating trai ning set data base. A forecasting lead time that varies from 16 hours to 88 hours is introduced to produce the short term electric load fore casting that meets requirements of real electric utility operating pra ctice. The unsupervised learning (UL) is used to identify days with si milar daily load patterns. A feed forward three-layer neural net is de signed to predict 24-hour loads within the supervised learning (SL) ph ase. The effectiveness of proposed methods is demonstrated by comparis on of forecasted hourly loads in every single day during 1991 with dat a realized in the same period in the Electric Power Utility of Serbia (EPS). A better choice of input features and more appropriate training set selection procedure allow significant improvement in forecasting results comparing with our previous UL/SL concept characterized by a f ixed neural-net structure and absence of re-training procedure. The im provement is illustrated by reduction of average error in daily energy forecasting for 0.83% and reduction of 90th percentile of 2.04%.