Electricity Price and Load Short-Term Forecasting Using Artificial Neural Networks

Abstract

This paper presents an approach for short-term electricity price and load forecasting using the artificial neural network (ANN) computing technique. The described approach uses the three-layered ANN paradigm with back-propagation. The publicly available data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The ANN approach based on similarity technique has been proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of similar days. Two different ANN models, one for load forecasting and another for price forecasting, have been proposed. Test results show that average price and load MAPEs for the year 2003 by using the ANN approach are obtained as 14.29% and 0.95%, respectively. MAPE values obtained from the price and load forecasting results confirm considerable value of the ANN based approach in forecasting short-term electricity prices and loads.