A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks

Honglu Zhu,Ling Nie,Jianxi Yao,Gang Zhao,Xu Li,Qiao Sun

doi:10.3390/en9010011

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and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable.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.

Highlights

As energy supply and the environmental situation becomes increasingly tight and critical around the world, the contradiction between electricity power supply and demand stands out.The development and utilization of conventional energy sources suffer from increasing limitations.Solar energy is recognized as an ideal renewable energy power generation sourcef
Forecasting the output power of PV plant is a significant problem for electric power departments to adjust dispatch planning in time, boost the reliability of electric system operation and the connection level of PV power plants and reduce spinning the reserve capacity of generation systems [1,2]
In order to deal with the periodic and non-stationary problems of PV output power, a hybrid modeling method based on Wavelet Decomposition (WD) and artificial neural network (ANN) is proposed in the paper, to achieve both good algorithm convergence and prediction results

Summary

Introduction

As energy supply and the environmental situation becomes increasingly tight and critical around the world, the contradiction between electricity power supply and demand stands out. In order to deal with the periodic and non-stationary problems of PV output power, a hybrid modeling method based on WD and ANN is proposed in the paper, to achieve both good algorithm convergence and prediction results. Influenced by the surrounding meteorological factors, the output power of the PV power plant shows different characteristics every day. 1-year can the be seen that output of the PV power plant has obvious differences in the total amount during different seasons in power of output power of the PV methods power plant showson nonlinear characteristics from day to night; the conventional power prediction based time series are not applicable to thewhile output aoutput. WD is conducted for output power signals collected for 5 days from a PV power plant, by a WDwavelet is conducted forwith output signals collected for 5indays from a PV power plant,results by a Dmeyer function five power decomposition layers, shown

The decomposition

Artificial

Example

10. Comparison

Conclusions