Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation
Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of po...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719883134 |
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| _version_ | 1832559134866669568 |
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| author | Chi Hua Erxi Zhu Liang Kuang Dechang Pi |
| author_facet | Chi Hua Erxi Zhu Liang Kuang Dechang Pi |
| author_sort | Chi Hua |
| collection | DOAJ |
| description | Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid. |
| format | Article |
| id | doaj-art-4f460a671d2749dd8e47a71fd6eb81f7 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-4f460a671d2749dd8e47a71fd6eb81f72025-02-03T01:30:43ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-10-011510.1177/1550147719883134Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagationChi Hua0Erxi Zhu1Liang Kuang2Dechang Pi3College of Internet of Things Engineering, Jiangsu Vocational College of Information Technology, Wuxi, ChinaCollege of Internet of Things Engineering, Jiangsu Vocational College of Information Technology, Wuxi, ChinaSchool of Physics and Optoelectronic Engineering, Nanjing University of Information Science & Technology, Nanjing, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics & Astronautics, Nanjing, ChinaAccurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.https://doi.org/10.1177/1550147719883134 |
| spellingShingle | Chi Hua Erxi Zhu Liang Kuang Dechang Pi Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation International Journal of Distributed Sensor Networks |
| title | Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation |
| title_full | Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation |
| title_fullStr | Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation |
| title_full_unstemmed | Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation |
| title_short | Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation |
| title_sort | short term power prediction of photovoltaic power station based on long short term memory back propagation |
| url | https://doi.org/10.1177/1550147719883134 |
| work_keys_str_mv | AT chihua shorttermpowerpredictionofphotovoltaicpowerstationbasedonlongshorttermmemorybackpropagation AT erxizhu shorttermpowerpredictionofphotovoltaicpowerstationbasedonlongshorttermmemorybackpropagation AT liangkuang shorttermpowerpredictionofphotovoltaicpowerstationbasedonlongshorttermmemorybackpropagation AT dechangpi shorttermpowerpredictionofphotovoltaicpowerstationbasedonlongshorttermmemorybackpropagation |