A NEURAL-NETWORK SHORT-TERM LOAD FORECASTER

This paper presents a neural network based approach to short-term load forecasting, which plays an important role in the day to day operatio n and scheduling of power systems. A four-layer feedforward neural net work, trained by a back-propagation learning algorithm, has been appli ed for forecasting the hourly load of a power system. In this paper, t he performance of the network is compared with some carefully chosen e xperimental methods. This new approach promises to provide results uno btainable with more traditional time series methods. It is shown that, with careful network design, the back-propagation learning procedure is an effective way of training neural networks for electrical load pr ediction. The choice of transfer function is an important design issue in achieving fast convergence and good generalization performance. Th e network is trained on real data from a power system and evaluated fo r short-term forecasting with hourly feedback. The network learns the training set nearly perfectly and shows accurate prediction with 1.07% error on weekdays and 1.80% error on weekends.