A real-time short-term peak and average load forecasting system using a self-organising fuzzy neural network

Abstract

This paper presents a self organising fuzzy-neural-network-based short-term electric load forecasting system for real-time implementation. A learning algorithm is devised for updating the connecting weights as well as the structure of the membership function of the network. The number of rules in the inferencing layer is optimised; this in turn optimises the network structure. The proposed algorithm exploits the notion of error back-propagation. The network is initialised with random weights. Experimental results of the system are discussed from a practical standpoint. The system accounts for seasonal and daily characteristics, as well as abnormal conditions, holidays and other conditions. It is capable of forecasting load with a lead time of one day to one week. The adaptive mechanism is used to train the network on-line. The results indicate that the proposed load forecasting system is robust and yields more accurate forecasts. Furthermore, it allows greater adaptability to sudden changes, compared with simple neural-network or statistical approaches. Extensive studies have been performed for all seasons, and some of them are presented in this paper. The new algorithm is tested with a typical load date of the Virginia Utility, and produces a very robust and accurate forecast, with a Mean Absolute of Percentage of Error (MAPE) mostly less than 1.8% for 24-hours-ahead peak load forecast, and 1.6% for a 168-hours-ahead forecast.