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 and Gang Zhao
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Honglu Zhu: School of Renewable Energy, North China Electric Power University, Beijing 102206, China
Xu Li: School of Renewable Energy, North China Electric Power University, Beijing 102206, China
Qiao Sun: Beijing Guodiantong Network Technology Co., Ltd., Beijing 100070, China
Ling Nie: Beijing Guodiantong Network Technology Co., Ltd., Beijing 100070, China
Jianxi Yao: School of Renewable Energy, North China Electric Power University, Beijing 102206, China
Gang Zhao: School of Electronic Engineering, Xidian University, Xian 710071, China

Energies, 2015, vol. 9, issue 1, 1-15

Abstract: 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and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable.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: photovoltaic power prediction; wavelet decomposition; artificial neural network; theoretical solar irradiance; signal reconstruction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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