A recurrent neural network for short-term load forecasting

Abstract

This paper proposes a recurrent neural network based approach to short-term load forecasting in power systems. Recurrent neural networks in multilayer perceptrons have an advantage that the context layer is able to cope with historical data. As a result, it is expected that recurrent neural networks give better solutions than the conventional feedforward multilayer perceptrons in term of accuracy. Also, the differential equation form of the time series is utilized to deal with the nonstationarity of the daily load time series. Furthermore, this paper proposes the diffusion learning method for determining weights between units in a recurrent network. The method is capable of escaping from local minima with stochastic noise. A comparison is made between conventional multilayer perceptrons and the proposed method for actual data. >