A functional-link-neural network for short-term electric load forecasting

Abstract

This paper presents a functional-link network based short-term electric load forecasting system for real-time implementation. The load and weather parameters are modelled as a nonlinear autoregressive moving average (ARMA) process and parameters of this model are obtained using the functional approximation capabilities of an auto-enhanced Functional Link net. Numerous and significant advantages accrue from using a flat net, including rapid quadratic optimisation in the learning of weights, simplification in the hardware as well as in computational procedures. The functional link net based load forecasting system accounts for seasonal and daily load characteristics as well as abnormal conditions, holidays and other conditions. It is capable of forecasting load with a lead time of one hour to seven days. The adaptive mechanism with a nonlinear learning rule is used to train the network on-line. The results indicate that the functional link net based load forecasting system produces robust and more accurate load forecasts in comparison to simple adaptive neural network or statistical based approaches. Testing the algorithm with load and weather data for a period of two years reveals satisfactory performance with mean absolute percentage error (MAPE) mostly less than 2% for a 24-hour ahead forecast and less than 2.5% for a 168-hour ahead forecast.