Combining a neural network and a rule-based expert system for short-term load forecasting

Abstract

A backpropagation neural network that used the output provided by a rule-based expert system was designed for short-term load forecasting. Extensive studies were performed on the effect of various factors such as learning rate and the number of hidden nodes. Load forecasting was performed on a Taiwan power system to demonstrate that the inclusion of the prediction from a rule-based expert system developed for a power system would improve the predictive capability of the neural network. The hourly power load for two typical days was evaluated, and for both days the inclusion of the rule-based expert system prediction as a network input significantly improved the neural network's prediction of power load. The predictive capability of the network was compared to the expert system as well as to a previously developed neural network. The proposed neural network provided improved predictive capability. In addition, the proposed combined approach converges much faster than both the conventional neural network and the rule-based expert system method.