Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration
Abstract Accurate prediction of photovoltaic power generation is a critical technical problem for utilizing solar energy. Aiming at the problem that the model parameters are difficult to obtain in applying photovoltaic power prediction methods, this paper has used long‐term monitoring data of output...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
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| Series: | Energy Science & Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ese3.1598 |
| _version_ | 1797391446319300608 |
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| author | Hang Zhao Delan Zhu Yalin Yang Qianlin Li Enze Zhang |
| author_facet | Hang Zhao Delan Zhu Yalin Yang Qianlin Li Enze Zhang |
| author_sort | Hang Zhao |
| collection | DOAJ |
| description | Abstract Accurate prediction of photovoltaic power generation is a critical technical problem for utilizing solar energy. Aiming at the problem that the model parameters are difficult to obtain in applying photovoltaic power prediction methods, this paper has used long‐term monitoring data of output power, various meteorological data, and solar irradiation intensity of photovoltaic modules. This paper establishes the functional relationship between the output power of photovoltaic modules and the irradiation intensity through Pearson correlation analysis. By deducing the distribution relationship of irradiation intensity, the prediction model of irradiation intensity based on peak sunshine hours and sunshine duration is constructed and based on 340 sites across the country 64 years peak sunshine hours and sunshine duration query database. In this work, the theoretical value of the prediction model on sunny days is close to the measured value (R2 = 0.918–0.985). The solar radiation intensity on rainy days is weak, and the prediction accuracy is low (R2 = 0.838–0.930). The relative errors between the sunshine duration and the peak sunshine hours in the database are less than 4.55% and 4.79%, respectively, under sunny conditions in each quarter, indicating that the accuracy of the database meets the actual needs. |
| first_indexed | 2024-03-08T23:32:44Z |
| format | Article |
| id | doaj.art-6b6945c286d84b3a94eec032e304af5e |
| institution | Directory Open Access Journal |
| issn | 2050-0505 |
| language | English |
| last_indexed | 2024-03-08T23:32:44Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj.art-6b6945c286d84b3a94eec032e304af5e2023-12-14T10:54:26ZengWileyEnergy Science & Engineering2050-05052023-12-0111124570458010.1002/ese3.1598Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine durationHang Zhao0Delan Zhu1Yalin Yang2Qianlin Li3Enze Zhang4Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education Northwest A&F University Yangling Shaanxi ChinaKey Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education Northwest A&F University Yangling Shaanxi ChinaHebei Luanhe River Affairs Center Tangshan ChinaCollege of Water Resources and Architectural Engineering Northwest A&F University Yangling ChinaCollege of Water Resources and Architectural Engineering Northwest A&F University Yangling ChinaAbstract Accurate prediction of photovoltaic power generation is a critical technical problem for utilizing solar energy. Aiming at the problem that the model parameters are difficult to obtain in applying photovoltaic power prediction methods, this paper has used long‐term monitoring data of output power, various meteorological data, and solar irradiation intensity of photovoltaic modules. This paper establishes the functional relationship between the output power of photovoltaic modules and the irradiation intensity through Pearson correlation analysis. By deducing the distribution relationship of irradiation intensity, the prediction model of irradiation intensity based on peak sunshine hours and sunshine duration is constructed and based on 340 sites across the country 64 years peak sunshine hours and sunshine duration query database. In this work, the theoretical value of the prediction model on sunny days is close to the measured value (R2 = 0.918–0.985). The solar radiation intensity on rainy days is weak, and the prediction accuracy is low (R2 = 0.838–0.930). The relative errors between the sunshine duration and the peak sunshine hours in the database are less than 4.55% and 4.79%, respectively, under sunny conditions in each quarter, indicating that the accuracy of the database meets the actual needs.https://doi.org/10.1002/ese3.1598forecasting modeloutput powerphotovoltaic power generationsunshine durationsunshine hours |
| spellingShingle | Hang Zhao Delan Zhu Yalin Yang Qianlin Li Enze Zhang Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration Energy Science & Engineering forecasting model output power photovoltaic power generation sunshine duration sunshine hours |
| title | Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration |
| title_full | Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration |
| title_fullStr | Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration |
| title_full_unstemmed | Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration |
| title_short | Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration |
| title_sort | study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration |
| topic | forecasting model output power photovoltaic power generation sunshine duration sunshine hours |
| url | https://doi.org/10.1002/ese3.1598 |
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