RECURRENT NEURAL NETWORKS FOR SHORT-TERM LOAD FORECASTING

Forecasting the short-term load entails the construction of a model, a nd, 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 o utput of some dynamic system, influenced by a number of weather, time and other environmental variables. Recurrent neural networks, being me mbers of a class of connectionist models exhibiting inherent dynamic b ehavior, can thus be used to construct empirical models for this dynam ic system. Because of the nonlinear dynamic nature of these models, th e behavior of the load prediction system can be captured in a compact and robust representation. This is illustrated by the performance of r ecurrent models on the short-term forecasting of the nation-wide load for the South African utility, ES-KOM. A comparison with feedforward n eural networks is also given.