One-step-ahead hourly Load Forecasting using artificial Neural Network

Abstract

An accurate and efficient Short-Term Load Forecasting (STLF) plays a vital role for economic operational planning of both regulated power systems and electricity markets. Therefore, many techniques and approaches for STLF problem have been presented in the literature. However, there is still an essential need to develop more accurate load forecast method. This paper presents the application of artificial Neural Network (NN) for hour-ahead load forecasting which is useful for real-time/balancing electricity market. The hourly load data set of California electricity market has been used to train and test the NN model. The reasonably accurate hour-ahead load forecast results have been obtained using NN.