DECOMPOSITION APPROACH TO FORECASTING ELECTRIC POWER SYSTEM COMMERCIAL LOAD USING AN ARTIFICIAL NEURAL NETWORK

ABSTRACT

Different methods have been proposed and subsequently used in forecasting the power system load. Most of the reported work treat the system load at the bulk power level. In practice, the system load is decomposed into user-based sectors. These are domestic, commercial, industrial and municipal toad sectors. Each sector is governed and influenced by phenomena inherent to that sector. Thus the load consumption characteristic for a sector is unique due to different socio-economic functions that take place in that sector. We use a multilayer neural network with a back propagation algorithm to forecast the commercial sector load portion resulting from decomposing the system load of the Nova Scotia Power Inc. system. To minimize the effect of weather on the forecast of the commercial load, it is further decomposed into four autonomous sections of six hour durations. The optimal input for a training set is determined based on the sum of the squared residuals of the predicted loads. The input patterns are made up of the immediate past four or five hours load and the output is the fifth or the sixth hour load. The results obtained using the proposed approach provide evidence that in the absence of some influential variables such as temperature, a careful selection of training patterns will enhance the performance of the artificial neural network in predicting the power system toad. Key Words: Power System Load Forecasting, Estimation and Prediction.