PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN
This paper aims to further improve the accuracy and reliability of short-term photovoltaic (PV) output power forecasting. Considering the blindness and randomness of weights and thresholds of traditional Elman neural networks and the fluctuation and nonstationarity of PV output power signal, the pap...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2022-05-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202104033 |
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| author | Na ZHANG Qiang REN Guangchen LIU Liping GUO Jingyu LI |
| author_facet | Na ZHANG Qiang REN Guangchen LIU Liping GUO Jingyu LI |
| author_sort | Na ZHANG |
| collection | DOAJ |
| description | This paper aims to further improve the accuracy and reliability of short-term photovoltaic (PV) output power forecasting. Considering the blindness and randomness of weights and thresholds of traditional Elman neural networks and the fluctuation and nonstationarity of PV output power signal, the paper proposes a short-term prediction model of PV output power based on variational mode decomposition (VMD) and an Elman neural network optimized by grey wolf optimization (GWO) algorithm. Firstly, the K-means algorithm is used to cluster the original data according to weather types. Then, VMD is employed to decompose the PV output power data of each weather type, and the decomposition subsequences are input into the Elman neural network optimized by GWO for PV output power forecasting. Finally, the forecasting results are superimposed. An example shows that the model has improved forecasting accuracy. |
| format | Article |
| id | doaj-art-349365e37b9941268b7c8ae8c8befbdf |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2022-05-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-349365e37b9941268b7c8ae8c8befbdf2025-08-20T02:52:38ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492022-05-01555576510.11930/j.issn.1004-9649.202104033zgdl-55-10-zhangnaPV Power Short-Term Forecasting Method Based on VMD-GWO-ELMANNa ZHANG0Qiang REN1Guangchen LIU2Liping GUO3Jingyu LI4College of Electricity, Inner Mongolia University of Technology, Hohhot 010051, ChinaCollege of Electricity, Inner Mongolia University of Technology, Hohhot 010051, ChinaCollege of Electricity, Inner Mongolia University of Technology, Hohhot 010051, ChinaCollege of Electricity, Inner Mongolia University of Technology, Hohhot 010051, ChinaCollege of Electricity, Inner Mongolia University of Technology, Hohhot 010051, ChinaThis paper aims to further improve the accuracy and reliability of short-term photovoltaic (PV) output power forecasting. Considering the blindness and randomness of weights and thresholds of traditional Elman neural networks and the fluctuation and nonstationarity of PV output power signal, the paper proposes a short-term prediction model of PV output power based on variational mode decomposition (VMD) and an Elman neural network optimized by grey wolf optimization (GWO) algorithm. Firstly, the K-means algorithm is used to cluster the original data according to weather types. Then, VMD is employed to decompose the PV output power data of each weather type, and the decomposition subsequences are input into the Elman neural network optimized by GWO for PV output power forecasting. Finally, the forecasting results are superimposed. An example shows that the model has improved forecasting accuracy.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202104033k-means clustervariational mode decompositiongrey wolf optimization algorithmelman neural networkshort-term pv power forecasting |
| spellingShingle | Na ZHANG Qiang REN Guangchen LIU Liping GUO Jingyu LI PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN Zhongguo dianli k-means cluster variational mode decomposition grey wolf optimization algorithm elman neural network short-term pv power forecasting |
| title | PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN |
| title_full | PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN |
| title_fullStr | PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN |
| title_full_unstemmed | PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN |
| title_short | PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN |
| title_sort | pv power short term forecasting method based on vmd gwo elman |
| topic | k-means cluster variational mode decomposition grey wolf optimization algorithm elman neural network short-term pv power forecasting |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202104033 |
| work_keys_str_mv | AT nazhang pvpowershorttermforecastingmethodbasedonvmdgwoelman AT qiangren pvpowershorttermforecastingmethodbasedonvmdgwoelman AT guangchenliu pvpowershorttermforecastingmethodbasedonvmdgwoelman AT lipingguo pvpowershorttermforecastingmethodbasedonvmdgwoelman AT jingyuli pvpowershorttermforecastingmethodbasedonvmdgwoelman |