Short-Term Load Forecasting Based on Ant Colony Clustering and Improved BP Neural Networks

Author(s):  
Ming Meng ◽  
Jian-chang Lu ◽  
Wei Sun
2013 ◽  
Vol 380-384 ◽  
pp. 3018-3021
Author(s):  
Kun Zhang ◽  
Yan Hui Wang

In order to ensure the dynamic balance of power load and improve the accuracy of short-term load forecasting, this paper presents a method of short-term load forecasting for electric power based on DB wavelet and regression BP neural networks. In this method, we get the wavelet coefficients at different scales through series decomposing of wavelet decomposition to load sample, and each scale wavelet coefficients for threshold selection, and then trained adjusted wavelet coefficients by regression BP neural networks, reconstructed load sequence predicted date through inverse wavelet transform. Finally, the accuracy of this method is significantly higher than BP neural network by examples verification.


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