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

ABSTRACT
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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. 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.Two network models are proposed in this paper: Elman network and Neural wavelet
network.