An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and Vietnam
Abstract The advancement of Photovoltaic technology has undergone rapid acceleration in recent years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making it an obvious source of power fluctuation. This study proposes a novel scheme for real‐time or intraday PV pow...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13108 |
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| author | Tuyen Nguyen‐Duc Huu Vu‐Xuan‐Son Hieu Do‐Dinh Nam Nguyen‐Vu‐Nhat Goro Fujita Son Tran‐Thanh |
| author_facet | Tuyen Nguyen‐Duc Huu Vu‐Xuan‐Son Hieu Do‐Dinh Nam Nguyen‐Vu‐Nhat Goro Fujita Son Tran‐Thanh |
| author_sort | Tuyen Nguyen‐Duc |
| collection | DOAJ |
| description | Abstract The advancement of Photovoltaic technology has undergone rapid acceleration in recent years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making it an obvious source of power fluctuation. This study proposes a novel scheme for real‐time or intraday PV power forecasting by adopting two predictive models, namely, White‐box and Combination. The White‐box model is implemented employing mathematical calculations and statistics called Exceedance Probability. Meanwhile, the Combination model is an aggregation of several predictive models' outputs including the White‐box model and benchmark ones by dynamically adjusting the weight coefficient of each model based on their forecasting accuracy. The experimental results, which are verified on two PV systems corresponding to two case studies located at Vietnam and Australia, indicate that the two proposed models outperform other referenced models as nMAPE improves approximately 40% and 38% in terms of the first and second case study, respectively. In particular, the White‐box model shows superiority by updating the forecast every 10 min, which can adapt to the fluctuation of weather conditions whereas the Combination one yields acceptable precision, indicating its flexible application. |
| format | Article |
| id | doaj-art-10f6ddcbe88a4d14be611346dd0cd721 |
| institution | OA Journals |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-10f6ddcbe88a4d14be611346dd0cd7212025-08-20T02:37:20ZengWileyIET Renewable Power Generation1752-14161752-14242024-10-0118142589260410.1049/rpg2.13108An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and VietnamTuyen Nguyen‐Duc0Huu Vu‐Xuan‐Son1Hieu Do‐Dinh2Nam Nguyen‐Vu‐Nhat3Goro Fujita4Son Tran‐Thanh5Department of Electrical Engineering Hanoi University of Science and Technology Hanoi VietnamRenewable Energy Management Department National Load Dispatch Centre Hanoi VietnamDepartment of Electrical Engineering Hanoi University of Science and Technology Hanoi VietnamRenewable Energy Management Department National Load Dispatch Centre Hanoi VietnamDepartment of Electrical Engineering Shibaura Institute of Technology Tokyo JapanDepartment of Electrical Engineering Hanoi University of Science and Technology Hanoi VietnamAbstract The advancement of Photovoltaic technology has undergone rapid acceleration in recent years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making it an obvious source of power fluctuation. This study proposes a novel scheme for real‐time or intraday PV power forecasting by adopting two predictive models, namely, White‐box and Combination. The White‐box model is implemented employing mathematical calculations and statistics called Exceedance Probability. Meanwhile, the Combination model is an aggregation of several predictive models' outputs including the White‐box model and benchmark ones by dynamically adjusting the weight coefficient of each model based on their forecasting accuracy. The experimental results, which are verified on two PV systems corresponding to two case studies located at Vietnam and Australia, indicate that the two proposed models outperform other referenced models as nMAPE improves approximately 40% and 38% in terms of the first and second case study, respectively. In particular, the White‐box model shows superiority by updating the forecast every 10 min, which can adapt to the fluctuation of weather conditions whereas the Combination one yields acceptable precision, indicating its flexible application.https://doi.org/10.1049/rpg2.13108artificial intelligenceforecasting theorysolar photovoltaic systemsstatistical analysis |
| spellingShingle | Tuyen Nguyen‐Duc Huu Vu‐Xuan‐Son Hieu Do‐Dinh Nam Nguyen‐Vu‐Nhat Goro Fujita Son Tran‐Thanh An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and Vietnam IET Renewable Power Generation artificial intelligence forecasting theory solar photovoltaic systems statistical analysis |
| title | An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and Vietnam |
| title_full | An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and Vietnam |
| title_fullStr | An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and Vietnam |
| title_full_unstemmed | An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and Vietnam |
| title_short | An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and Vietnam |
| title_sort | adaptive method for real time photovoltaic power forecasting utilizing mathematics and statistics case studies in australia and vietnam |
| topic | artificial intelligence forecasting theory solar photovoltaic systems statistical analysis |
| url | https://doi.org/10.1049/rpg2.13108 |
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