Neural network models to predict short-term electricity prices and loads

Forecasting hourly electricity prices and loads in daily power markets is the most essential task and basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper presents an approach for short-term electricity price and load forecasting using neural network model, which uses publicly available data from NEMMCO Web site to forecast electricity price and load for the Victorian electricity market. An approach of selection of similar days is 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. The forecasted price and load from the neural networks are the corrected output of selected similar price and load days, respectively. MAPE results show that short-term electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy