Article,
Wavelet Transform and Neural Networks for Short-Term Electrical Load Forecasting
Energy Conversion & Management, (2000)
Abstract
Demand forecasting is key to the eÂcient management of electrical
energy systems. A novel approach is proposed in this paper for short
term electrical load forecasting by combining the wavelet transform
and neural networks. The electrical load at any particular time is
usually assumed to be a linear combination of diÂerent components.
From the signal analysis point of view, load can also be considered
as a linear combination of diÂerent frequencies. Every component
of load can be represented by one or several frequencies. The process
of the proposed approach ®rst decomposes the historical load into
an approximate part associated with low frequencies and several detail
parts associated with high frequencies through the wavelet transform.
Then, a radial basis function neural network, trained by low frequencies
and the corresponding temperature records is used to predict the
approximate part of the future load. Finally, the short term load
is forecasted by summing the predicted approximate part and the weighted
detail parts. The approach has been tested by the 1997 data of a
practical system. The results show the application of the wavelet
transform in short term load forecasting is encouraging
