NDLI: The load forecasting using the PSO-BP neural network and wavelet transform

Content Provider	IEEE Xplore Digital Library
Author	Liu Mengliang Gao Rong Wang Xiuhong
Copyright Year	2008
Description	Author affiliation: Shandong Agric. Univ., Taian (Liu Mengliang)
Abstract	An integrated BP neural network and particle swarm optimization (PSO) for load forecasting method is presented in this paper. From the signal analysis point of view, load can also be considered as a linear combination of different frequencies. The proposed approach decomposes the historical load into an approximate part and several detail parts through the wavelet transform. Then based on the maximum and minimum loads of the approximate part, the similar coefficients are given. The PSO-BP neural network, trained by low frequencies and the corresponding temperature records, is used to forecast the maximum and minimum load of the forecasting day. The short term load forecasting is forecasted by summing the predicted approximate part and the weighted detail parts. We knew that the PSO has a good capability for searching for optimal value. There are have few parameters to adjust than genetic algorithm (GA). The result demonstrated the accuracy of the proposed load forecasting scheme.
Starting Page	34
Ending Page	37
File Size	112577
Page Count	4
File Format	PDF
ISBN	9787900719706
DOI	10.1109/CHICC.2008.4605163
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute Of Intelligence M&C Technical Of KMUST
Subject Keyword	Wavelet Transform Gallium BP Neural Network Load forecasting Load Forecasting Artificial neural networks Predictive models Approximation methods Forecasting Particle Swarm Optimization Load modeling
Content Type	Text
Resource Type	Article

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