Short-Term Electrical Load Forecasting Based on Neural Networks

Abstract

The article sets out the use of an artificial neural network for electrical load consumption forecasting. Electrical load prediction is one of the most demanded areas of research in the electric power industry. The two-layer artificial neural network of direct distribution with the number of neurons in the hidden layer equal to 6 was proposed in the article. The following data was taken as an input for forecasting short-term electrical load: electrical load, time, day of the week, temperature, day, weekend and working day code. According to the studies, the value of the mean absolute percentage error was 2.35, using the Bayesian Regularization learning algorithm.