A Photovoltaic Power Prediction Method Based on AFSA-BP Neural Network
In order to improve the prediction accuracy of photovoltaic output power， this paper proposes a prediction method using AFSA （artificial fish swarm algorithm） to optimize BP （back-propagation） neural network. Based on the cleaned data， the paper takes highly correlative meteorological data as input，...
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| Format: | Article |
| Language: | zho |
| Published: |
zhejiang electric power
2022-04-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=4d1ab761-adb3-421b-ba0b-6e8da98c78e3 |
| _version_ | 1817998057687482368 |
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| author | CHEN Wenjin ZHU Feng ZHANG Tongyan ZHANG Jun ZHANG Fengming XIE Dong RU Wei SONG Meiya FAN Qiang |
| author_facet | CHEN Wenjin ZHU Feng ZHANG Tongyan ZHANG Jun ZHANG Fengming XIE Dong RU Wei SONG Meiya FAN Qiang |
| author_sort | CHEN Wenjin |
| collection | DOAJ |
| description | In order to improve the prediction accuracy of photovoltaic output power， this paper proposes a prediction method using AFSA （artificial fish swarm algorithm） to optimize BP （back-propagation） neural network. Based on the cleaned data， the paper takes highly correlative meteorological data as input， and photovoltaic output power data as output. It uses the global optimization capabilities and inherent parallel computing capabilities of AFSA to optimize the weights and thresholds of the BP neural network. The photovoltaic output power prediction model based on the AFSA-BP neural network is obtained after training. The simulation analysis of a photovoltaic power station shows that compared with using BP neural networks, genetic algorithm optimized BP neural network， and PSO-BP network， the prediction results of this method are more accurate, the degree of fitting to the original data curve is better， the corresponding error evaluation index is lower, and the training is less time-consuming; the method can rapidly and accurately predict the photovoltaic output power. |
| first_indexed | 2024-04-14T02:47:31Z |
| format | Article |
| id | doaj.art-f66306c6a20641d5bb0ee2bd9abe4a18 |
| institution | Directory Open Access Journal |
| issn | 1007-1881 |
| language | zho |
| last_indexed | 2024-04-14T02:47:31Z |
| publishDate | 2022-04-01 |
| publisher | zhejiang electric power |
| record_format | Article |
| series | Zhejiang dianli |
| spelling | doaj.art-f66306c6a20641d5bb0ee2bd9abe4a182022-12-22T02:16:30Zzhozhejiang electric powerZhejiang dianli1007-18812022-04-0141471310.19585/j.zjdl.2022040021007-1881（2022）04-0007-07A Photovoltaic Power Prediction Method Based on AFSA-BP Neural NetworkCHEN Wenjin0ZHU Feng1ZHANG Tongyan2ZHANG Jun3ZHANG Fengming4XIE Dong5RU Wei6SONG Meiya7FAN Qiang8State Grid Zhejiang Electric Power Co.,Ltd., Hangzhou 310007, ChinaState Grid Shaoxing Power Supply Company, Shaoxing Zhejiang 312362, ChinaSchool of Electrical and Automation, Wuhan University, Wuhan 430072, ChinaState Grid Zhejiang Electric Power Co.,Ltd., Hangzhou 310007, ChinaState Grid Shaoxing Power Supply Company, Shaoxing Zhejiang 312362, ChinaState Grid Shaoxing Power Supply Company, Shaoxing Zhejiang 312362, ChinaState Grid Shaoxing Power Supply Company, Shaoxing Zhejiang 312362, ChinaState Grid Shaoxing Power Supply Company, Shaoxing Zhejiang 312362, ChinaState Grid Shaoxing Power Supply Company, Shaoxing Zhejiang 312362, ChinaIn order to improve the prediction accuracy of photovoltaic output power， this paper proposes a prediction method using AFSA （artificial fish swarm algorithm） to optimize BP （back-propagation） neural network. Based on the cleaned data， the paper takes highly correlative meteorological data as input， and photovoltaic output power data as output. It uses the global optimization capabilities and inherent parallel computing capabilities of AFSA to optimize the weights and thresholds of the BP neural network. The photovoltaic output power prediction model based on the AFSA-BP neural network is obtained after training. The simulation analysis of a photovoltaic power station shows that compared with using BP neural networks, genetic algorithm optimized BP neural network， and PSO-BP network， the prediction results of this method are more accurate, the degree of fitting to the original data curve is better， the corresponding error evaluation index is lower, and the training is less time-consuming; the method can rapidly and accurately predict the photovoltaic output power.https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=4d1ab761-adb3-421b-ba0b-6e8da98c78e3afsabp neural networkphotovoltaic power generationpower prediction |
| spellingShingle | CHEN Wenjin ZHU Feng ZHANG Tongyan ZHANG Jun ZHANG Fengming XIE Dong RU Wei SONG Meiya FAN Qiang A Photovoltaic Power Prediction Method Based on AFSA-BP Neural Network Zhejiang dianli afsa bp neural network photovoltaic power generation power prediction |
| title | A Photovoltaic Power Prediction Method Based on AFSA-BP Neural Network |
| title_full | A Photovoltaic Power Prediction Method Based on AFSA-BP Neural Network |
| title_fullStr | A Photovoltaic Power Prediction Method Based on AFSA-BP Neural Network |
| title_full_unstemmed | A Photovoltaic Power Prediction Method Based on AFSA-BP Neural Network |
| title_short | A Photovoltaic Power Prediction Method Based on AFSA-BP Neural Network |
| title_sort | photovoltaic power prediction method based on afsa bp neural network |
| topic | afsa bp neural network photovoltaic power generation power prediction |
| url | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=4d1ab761-adb3-421b-ba0b-6e8da98c78e3 |
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