1. Short-Term Prediction Model for a Grid-Connected Photovoltaic System Using EMD and GABPNN
- Author
-
Minyi Guo, Zeng Wei Zheng, Yuan Yi Chen, Bo Zhao, Xiao Wei Zhou, and Mei Mei Huo
- Subjects
Engineering ,Artificial neural network ,business.industry ,Photovoltaic system ,General Medicine ,Grid ,computer.software_genre ,Hilbert–Huang transform ,Power (physics) ,Component (UML) ,Genetic algorithm ,Grid-connected photovoltaic power system ,Data mining ,business ,computer - Abstract
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.
- Published
- 2013