Egyptian Unified Grid hourly load forecasting using artificial neural network

This paper presents an artificial neural network (ANN) based hourly load forecasting application to the Egyptian Unified Grid (EUG). The ANN involved is designed using the multilayer back propagation learning technique. The ANN input layer receives all relevant information that can contribute extensively to the prediction process, excluding weather information. The input layer receives information on: the class of day type, the hour in day time, the load in hour-before, the load in day-before at same hour, the average load in day-before, the peak load in day-before, the minimum load in day-before, and also the latter four measurements but in the week before. Also, the ANN output layer provides the predicted hourly load. The ANN load forecasting model is trained based on a historical domain of knowledge. The required knowledge patterns were obtained for the EUG during the winter of 1993. When testing the trained ANN, it is proved that it can be applied to the prediction of hourly load very efficiently and accurately. Final results indicated average errors of 0.18% (training) and 0.49% (evaluation), and standard deviations of 2.32 and 2.92%, respectively. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.