Recurrent neural networks for short-term load forecasting

Forecasting the short-term load entails the construction of a model, and, using the information available, estimating the parameters of the model to optimize the prediction performance. It follows that the more closely the chosen model approximates the actual physical generating process, the higher the expected performance of the forecasting system. In this paper it is postulated that the load can be modeled as the output of some dynamic system, influenced by a number of weather, time and other environmental variables. Recurrent neural networks, being members of a class of connectionist models exhibiting inherent dynamic behavior, can thus be used to construct empirical models for this dynamic system. Because of the nonlinear dynamic nature of these models, the behavior of the load prediction system can be captured in a compact and robust representation. This is illustrated by the performance of recurrent models on the short-term forecasting of the nation-wide load for the South African utility, ES-KOM. A comparison with feedforward neural networks is also given.