Short term load forecasting for distribution substations using artificial neural networks - Nieva, Ronaldo E. (2006)
The primary objective of this study is to use artificial neural network for forecasting the hourly load of a distribution substation for each day of the week regardless of the season or the month of the year. Forecasting load demand at the substation level is generally more difficult and less accurate compared to forecasting total system load demand. The study used the multi-layered feed-forward neural network with back-propagation training algorithm to develop the models. The study only uses hourly MW and MVAR load data of a substation as input data for simplicity and ease of model preparation. Forecast model for each day of the week were developed for two MERALCO substations. The resulting models obtained ma MAPE less than 5%, which was set as the upper limit of model accuracy, with the lowest MAPE being 3.53%. Forecast models were also developed using the multiple regression (MR) method and the accuracy of the developed models were compared to the ANN models. The resulting MAPE of the ANN models were lower than that of the MR models.