Dynamic neural network based genetic algorithm optimizing for short term load forecasting

2010 ◽  
Author(s):  
Yan Wang ◽  
Yuanwei Jing ◽  
Weilun Zhao ◽  
Yan-e Mao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Load Forecasting ◽  
Short Term ◽  
Dynamic Neural Network ◽  
Short Term Load Forecasting

An Improved RBF Neural Network for Short Term Load Forecasting

2014 ◽  
Vol 1008-1009 ◽  
pp. 709-713 ◽  
Author(s):  
Chuang Li ◽  
Zhi Qiang Liang ◽  
Min You Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Discriminant Function ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Static Model ◽  
Short Term ◽  
Dynamic Neural Network ◽  
Short Term Load Forecasting

Neural network is widely used in the load forecasting area,but the traditional methods of load forecasting usually base on static model,which cannot change as time goes on. And the accuracy is worse and worse. To solve the problem, a dynamic neural network model for load forecasting is proposed .By way of introduce Error discriminant function, to control the error of load forecasting and dynamically modify the predicting model. Through the contrast of the short-term load forecasting result based on static neural network model and dynamic neural network model proposed, the error of load forecasting is decrease effectively.

scholarly journals A novel genetic-algorithm-based neural network for short-term load forecasting

2003 ◽  
Vol 50 (4) ◽  
pp. 793-799 ◽  
Author(s):  
S.H. Ling ◽  
F.H.F. Leung ◽  
H.K. Lam ◽  
Yim-Shu Lee ◽  
P.K.S. Tam
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

Short-Term Load Forecasting Based on GA-Elman Model

2014 ◽  
Vol 986-987 ◽  
pp. 520-523
Author(s):  
Wen Xia You ◽  
Jun Xiao Chang ◽  
Zi Heng Zhou ◽  
Ji Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Relative Error ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Elman Neural Network ◽  
Learning Efficiency ◽  
Multiple Layer ◽  
Short Term Load Forecasting

Elman Neural Network is a typical neural-network which shares the characteristics of multiple-layer and dynamic recurrent, and it’s more suitable than BP Neural Network when it’s applied to forecast the short-term load with periodicity and similarity. To solve the problem that Elman Neural Network lacks learning efficiency, GA-Elman model is established by optimizing the weights and thresholds using Genetic Algorithm. An example is then given to prove the effectiveness of GA-Elman model, using the load data of a certain region. Relative error and MSE have been considered as criterions to analyze the results of load forecasting. By comparing the results calculated by BP, Elman and GA-Elman model, the effectiveness of GA-Elman model is verified, which will improve the accuracy of short-term load forecasting.

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