Short-term ANN load forecasting from limited data using generalization learning strategies - Chan, Zeke S.H.; Ngan, H.W.; Rad, A.B.; David, A.K.; Kasabov, N. (Neurocomputing (Amsterdam), 2006)
The emergence of the new competitive electricity market environment has made short-term load forecasting a more complex task, owing to the effect of marketers’ behavior on the load pattern and the reduction of available information due to commercial reasons. In recent years, many ANN-based forecasters are proposed for learning the highly nonlinear load pattern, yet their effectiveness are limited by the reduction of training data, which causes these ANN models to be susceptible to “over-fitting”. “Over-fitting” is a common ANN problem that describes the situation that the model memorizes the training data but fails to generalize well to new data. This paper discusses the problem of “over-fitting” and some common generalization learning techniques in the ANN literature, as well as introducing a new Genetic Algorithm-based regularization method called “GARNET” for short-term load forecasting. As an illustration, four generalization learning techniques, including Early-Stopping, Bayesian Regularization, Adaptive-Regularization and GARNET are applied to train Multi-Layer Perceptrons networks (MLP) for day-ahead load forecasting on limited amount of hourly data from a US utility. Results show that forecasters trained by these four methods consistently produce lower prediction error than those trained by the standard error minimization method.