Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerica...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2023-09-01
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| Series: | Archives of Electrical Engineering |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/128358/PDF/art04_int.pdf |
| _version_ | 1797682231846633472 |
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| author | Ping He Jie Dong Xiaopeng Wu Lei Yun Hua Yang |
| author_facet | Ping He Jie Dong Xiaopeng Wu Lei Yun Hua Yang |
| author_sort | Ping He |
| collection | DOAJ |
| description | At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP. |
| first_indexed | 2024-03-11T23:57:41Z |
| format | Article |
| id | doaj.art-f52f00b98a804bc5adc1e3277f0d80a3 |
| institution | Directory Open Access Journal |
| issn | 2300-2506 |
| language | English |
| last_indexed | 2024-03-11T23:57:41Z |
| publishDate | 2023-09-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Electrical Engineering |
| spelling | doaj.art-f52f00b98a804bc5adc1e3277f0d80a32023-09-18T11:04:24ZengPolish Academy of SciencesArchives of Electrical Engineering2300-25062023-09-01vol. 72No 3613628https://doi.org/10.24425/aee.2023.146040Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagationPing He0https://orcid.org/0000-0002-1692-5804Jie Dong1https://orcid.org/0000-0003-2307-4240Xiaopeng Wu2https://orcid.org/0000-0003-2691-4487Lei Yun3https://orcid.org/0000-0002-4277-2178Hua Yang4https://orcid.org/0000-0003-1129-019XZhengzhou University of Light Industry, College of Electrical and Information Engineering, ChinaZhengzhou University of Light Industry, College of Electrical and Information Engineering, ChinaZhengzhou University of Light Industry, College of Electrical and Information Engineering, ChinaZhengzhou University of Light Industry, College of Electrical and Information Engineering, ChinaZhengzhou University of Light Industry, College of Electrical and Information Engineering, ChinaAt present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.https://journals.pan.pl/Content/128358/PDF/art04_int.pdfbp neural networkphotovoltaic power generationpso–gwo modelpso–gwo–bp prediction modelstandard grey wolf algorithm |
| spellingShingle | Ping He Jie Dong Xiaopeng Wu Lei Yun Hua Yang Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation Archives of Electrical Engineering bp neural network photovoltaic power generation pso–gwo model pso–gwo–bp prediction model standard grey wolf algorithm |
| title | Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation |
| title_full | Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation |
| title_fullStr | Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation |
| title_full_unstemmed | Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation |
| title_short | Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation |
| title_sort | photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation |
| topic | bp neural network photovoltaic power generation pso–gwo model pso–gwo–bp prediction model standard grey wolf algorithm |
| url | https://journals.pan.pl/Content/128358/PDF/art04_int.pdf |
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