scholarly journals A Short-Term Prediction Model Based on Support Vector Regression Optimized by Artificial Fish-Swarm Algorithm

2015 ◽  
Vol 8 (7) ◽  
pp. 237-250
Author(s):  
GuiPing Wang ◽  
ShuYu Chen ◽  
Jun Liu ◽  
TianShu Wu
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Short Term ◽  
Artificial Fish Swarm Algorithm ◽  
Model Based ◽  
Term Prediction ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Short Term Prediction

Short-Term Prediction Model for a Grid-Connected Photovoltaic System Using EMD and GABPNN

2013 ◽  
Vol 291-294 ◽  
pp. 74-82
Author(s):  
Zeng Wei Zheng ◽  
Yuan Yi Chen ◽  
Xiao Wei Zhou ◽  
Mei Mei Huo ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Prediction Method ◽  
Photovoltaic System ◽  
Photovoltaic Systems ◽  
Short Term ◽  
Model Based ◽  
Term Prediction ◽  
Short Term Prediction

The integration between photovoltaic systems and tradition grid have a lot of challenges. To accurately predict is a key to solve these challenges. Due to complex, non-linear and non-stationary characteristics, it is difficult to accurately predict the power of photovoltaic systems. In this paper, a short-term prediction model based on empirical mode decomposition (EMD)and back propagation neural network(BPNN) was constructed, and use genetic algorithm as the learn algorithm of BPNN. The power data after pre-processing is decomposed into several components, then using prediction model based on BPNN and genetic algorithm to predict each component, and all the component prediction values were aggregated to obtain the ultimate predicted result. The simulation shows the purposed prediction model has higher prediction precision compare with traditional neural network prediction method and it is an effective prediction method of photovoltaic systems.

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