Application of wavelet and neural network to long-term load forecasting

Abstract

Long term load forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. Artificial intelligent applications have been introduced for load forecasting. The forecasting procedure considered the correlation variables that have an influence over the demand for electricity, for example: gross state product (GSP), consumers price index (CPI) and electricity tariff (ET). These variables are chosen to enter the model as the inputs of network. The output is consumed energy. This paper proposed to apply the universal approximation properties of neural and wavelet networks to determine the function that denote relationship between input variables and output energy. The basic back-propagation algorithm is used as a supervisory learning. Three network models are proposed in this paper: functional link net, multi-layer perceptron neural network and wavelet network.