A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks
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and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable.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 m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-12-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/1/11 |
| _version_ | 1817990181238603776 |
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| author | Honglu Zhu Xu Li Qiao Sun Ling Nie Jianxi Yao Gang Zhao |
| author_facet | Honglu Zhu Xu Li Qiao Sun Ling Nie Jianxi Yao Gang Zhao |
| author_sort | Honglu Zhu |
| collection | DOAJ |
| description | 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and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable.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. |
| first_indexed | 2024-04-14T00:55:44Z |
| format | Article |
| id | doaj.art-c0a4183b0d5240ca9a721f45e5e054b6 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T00:55:44Z |
| publishDate | 2015-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c0a4183b0d5240ca9a721f45e5e054b62022-12-22T02:21:37ZengMDPI AGEnergies1996-10732015-12-01911110.3390/en9010011en9010011A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural NetworksHonglu Zhu0Xu Li1Qiao Sun2Ling Nie3Jianxi Yao4Gang Zhao5School of Renewable Energy, North China Electric Power University, Beijing 102206, ChinaSchool of Renewable Energy, North China Electric Power University, Beijing 102206, ChinaBeijing Guodiantong Network Technology Co., Ltd., Beijing 100070, ChinaBeijing Guodiantong Network Technology Co., Ltd., Beijing 100070, ChinaSchool of Renewable Energy, North China Electric Power University, Beijing 102206, ChinaSchool of Electronic Engineering, Xidian University, Xian 710071, ChinaThe 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.http://www.mdpi.com/1996-1073/9/1/11photovoltaic power predictionwavelet decompositionartificial neural networktheoretical solar irradiancesignal reconstruction |
| spellingShingle | Honglu Zhu Xu Li Qiao Sun Ling Nie Jianxi Yao Gang Zhao A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks Energies photovoltaic power prediction wavelet decomposition artificial neural network theoretical solar irradiance signal reconstruction |
| title | A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks |
| title_full | A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks |
| title_fullStr | A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks |
| title_full_unstemmed | A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks |
| title_short | A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks |
| title_sort | power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks |
| topic | photovoltaic power prediction wavelet decomposition artificial neural network theoretical solar irradiance signal reconstruction |
| url | http://www.mdpi.com/1996-1073/9/1/11 |
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