LAPSE:2018.0752
Published Article
LAPSE:2018.0752
A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks
Honglu Zhu, Xu Li, Qiao Sun, Ling Nie, Jianxi Yao, Gang Zhao
October 22, 2018
The power prediction for photovoltaic (PV) power plants has significant importance for their grid connection. Due to PV power’s periodicity and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable. This paper presents a method combining the advantages of the wavelet decomposition (WD) and artificial neural network (ANN) to solve this problem. With the ability of ANN to address nonlinear relationships, theoretical solar irradiance and meteorological variables are chosen as the input of the hybrid model based on WD and ANN. The output power of the PV plant is decomposed using WD to separated useful information from disturbances. The ANNs are used to build the models of the decomposed PV output power. Finally, the outputs of the ANN models are reconstructed into the forecasted PV plant power. The presented method is compared with the traditional forecasting method based on ANN. The results shows that the method described in this paper needs less calculation time and has better forecasting precision.
Keywords
artificial neural network, photovoltaic power prediction, signal reconstruction, theoretical solar irradiance, wavelet decomposition
Suggested Citation
Zhu H, Li X, Sun Q, Nie L, Yao J, Zhao G. A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks. (2018). LAPSE:2018.0752
Author Affiliations
Zhu H: School of Renewable Energy, North China Electric Power University, Beijing 102206, China [ORCID]
Li X: School of Renewable Energy, North China Electric Power University, Beijing 102206, China
Sun Q: Beijing Guodiantong Network Technology Co., Ltd., Beijing 100070, China
Nie L: Beijing Guodiantong Network Technology Co., Ltd., Beijing 100070, China
Yao J: School of Renewable Energy, North China Electric Power University, Beijing 102206, China
Zhao G: School of Electronic Engineering, Xidian University, Xian 710071, China
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Journal Name
Energies
Volume
9
Issue
1
Article Number
E11
Year
2015
Publication Date
2015-12-24
Published Version
ISSN
1996-1073
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PII: en9010011, Publication Type: Journal Article
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LAPSE:2018.0752
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doi:10.3390/en9010011
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Oct 22, 2018
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Oct 22, 2018
 
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Calvin Tsay
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