Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism
Solar photovoltaic power generation has the characteristics of intermittence and randomness, which makes it a challenge to accurately predict solar power generation power, and it is difficult to achieve the desired effect. Therefore, by fully considering the relationship between power generation dat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1123558/full |
| _version_ | 1797869945365725184 |
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| author | Wengang Chen Hongying He Jianguo Liu Jinbiao Yang Ke Zhang Diansheng Luo |
| author_facet | Wengang Chen Hongying He Jianguo Liu Jinbiao Yang Ke Zhang Diansheng Luo |
| author_sort | Wengang Chen |
| collection | DOAJ |
| description | Solar photovoltaic power generation has the characteristics of intermittence and randomness, which makes it a challenge to accurately predict solar power generation power, and it is difficult to achieve the desired effect. Therefore, by fully considering the relationship between power generation data and climate factors, a new prediction method is proposed based on sliced bidirectional long short term memory and the attention mechanism. The prediction results show that the presented model has higher accuracy than the common prediction models multi-layer perceptron, convolution neural network, long short term memory and bidirectional long short term memory. The presented sliced bidirectional cyclic network has high prediction accuracy by low root mean square error and mean absolute error of 1.999 and 1.159 respectively. The time cost is only 24.32% of that of long short term memory network and 13.76% of that of bidirectional long short term memory network. |
| first_indexed | 2024-04-10T00:19:26Z |
| format | Article |
| id | doaj.art-01391856cd544d1b8b067307ecc85101 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T00:19:26Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-01391856cd544d1b8b067307ecc851012023-03-16T04:36:20ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-03-011110.3389/fenrg.2023.11235581123558Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanismWengang Chen0Hongying He1Jianguo Liu2Jinbiao Yang3Ke Zhang4Diansheng Luo5State Grid Jincheng Power Supply Company, Jincheng, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha, ChinaState Grid Jincheng Power Supply Company, Jincheng, ChinaState Grid Jincheng Power Supply Company, Jincheng, ChinaState Grid Jincheng Power Supply Company, Jincheng, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha, ChinaSolar photovoltaic power generation has the characteristics of intermittence and randomness, which makes it a challenge to accurately predict solar power generation power, and it is difficult to achieve the desired effect. Therefore, by fully considering the relationship between power generation data and climate factors, a new prediction method is proposed based on sliced bidirectional long short term memory and the attention mechanism. The prediction results show that the presented model has higher accuracy than the common prediction models multi-layer perceptron, convolution neural network, long short term memory and bidirectional long short term memory. The presented sliced bidirectional cyclic network has high prediction accuracy by low root mean square error and mean absolute error of 1.999 and 1.159 respectively. The time cost is only 24.32% of that of long short term memory network and 13.76% of that of bidirectional long short term memory network.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1123558/fullphotovoltaic power generation systempower predictionsliced recurrent networkbidirectional long short term memoryattention mechanism |
| spellingShingle | Wengang Chen Hongying He Jianguo Liu Jinbiao Yang Ke Zhang Diansheng Luo Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism Frontiers in Energy Research photovoltaic power generation system power prediction sliced recurrent network bidirectional long short term memory attention mechanism |
| title | Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism |
| title_full | Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism |
| title_fullStr | Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism |
| title_full_unstemmed | Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism |
| title_short | Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism |
| title_sort | photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism |
| topic | photovoltaic power generation system power prediction sliced recurrent network bidirectional long short term memory attention mechanism |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1123558/full |
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