Wavelet neural network embedded expert system used in short-term load forecasting

2011 ◽  
Author(s):  
Niu Dongxiao ◽  
Ji Ling ◽  
Tian Jie
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Load Forecasting ◽  
Wavelet Neural Network ◽  
Short Term ◽  
Short Term Load Forecasting

A Method Based on Wavelet Neural Network for Power System Short Term Load Forecasting

2014 ◽  
Vol 521 ◽  
pp. 303-306 ◽  
Author(s):  
Hong Mei Zhong ◽  
Jie Liu ◽  
Qi Fang Chen ◽  
Nian Liu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Wavelet Neural Network ◽  
Signal Spectrum ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Load Sequence ◽  
Daily Load

The short-term load of Power System is uncertain and the daily-load signal spectrum is continuous. The approach of Wavelet Neural Network (WNN) is proposed by combing the wavelet transform (WT) and neural network. By the WT, the time-based short-term load sequence can be decomposed into different scales sequences, which is used to training the BP neural network. The short-term load is forecasted by the trained BP neural network. Select the load of a random day in Lianyungang to study, according to the numerical simulation results, the method proves to achieve good performances.

Combining a neural network and a rule-based expert system for short-term load forecasting

1997 ◽  
Vol 32 (4) ◽  
pp. 787-797 ◽  
Author(s):  
Chih-Chou Chiu ◽  
Deborah F. Cook ◽  
Jen-Lung Kao ◽  
Yu-Chao Chou
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Load Forecasting ◽  
Short Term ◽  
Rule Based ◽  
Short Term Load Forecasting

Short-Term Power Load Forecasting Based on Improved Wavelet Neural Network

2013 ◽  
Vol 816-817 ◽  
pp. 766-769
Author(s):  
Dong Xiao Niu ◽  
Lei Lei Fan ◽  
Chun Xiang Liu
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Load Forecasting ◽  
Weighted Average ◽  
Wavelet Neural Network ◽  
Generalization Capability ◽  
Short Term ◽  
Nonlinear Fitting ◽  
Power Load ◽  
Short Term Load Forecasting

Accurate short-term load forecasting contributes to safe and economic operation of power systems. Due to the shortcomings of traditional wavelet neural network (WNN), which usually has low convergence rate and easily falls into local minimum, an improved wavelet neural network (IWNN) is proposed to modify the algorithm by introducing momentum. Together with the weighted average method (WA) and WNN, these three methods are applied to an example of short-term load forecasting. The results show that compared with the WA method, WNN has obvious advantages of nonlinear fitting and forecasting, and the IWNN method is superior to the others in terms of prediction accuracy and generalization capability, which is helpful to further improve the accuracy of short-term load forecasting.

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