Photovoltaic power forecasting: A Transformer based framework
The accurate prediction of photovoltaic (PV) energy production is a crucial task to optimise the integration of solar energy into the power grid and maximise the benefit of renewable source trading in the energy market. This paper systematically and quantitatively analyses the literature by comparin...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001101 |
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| author | Gabriele Piantadosi Sofia Dutto Antonio Galli Saverio De Vito Carlo Sansone Girolamo Di Francia |
| author_facet | Gabriele Piantadosi Sofia Dutto Antonio Galli Saverio De Vito Carlo Sansone Girolamo Di Francia |
| author_sort | Gabriele Piantadosi |
| collection | DOAJ |
| description | The accurate prediction of photovoltaic (PV) energy production is a crucial task to optimise the integration of solar energy into the power grid and maximise the benefit of renewable source trading in the energy market. This paper systematically and quantitatively analyses the literature by comparing different machine learning techniques and the impact of different meteorological forecast providers. The methodology consists of an irradiance model coupled with a meteorological provider; this combination removes the constraint of a local irradiance measurement. The result is a Transformer Neural Network architecture, trained and tested using OpenMeteo data, whose performance is superior to other combinations, providing a MAE of 1.22 kW (0.95%), and a MAPE of 2.21%. The implications of our study suggest that adopting a comprehensive approach, integrating local weather data, modelled irradiance, and PV plant configuration data, can significantly improve the accuracy of PV power forecasting, thus contributing to more effective technological and economic integration. |
| format | Article |
| id | doaj-art-4b4d4a69c0074c1eaa403259211a40cc |
| institution | OA Journals |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-4b4d4a69c0074c1eaa403259211a40cc2025-08-20T02:36:58ZengElsevierEnergy and AI2666-54682024-12-011810044410.1016/j.egyai.2024.100444Photovoltaic power forecasting: A Transformer based frameworkGabriele Piantadosi0Sofia Dutto1Antonio Galli2Saverio De Vito3Carlo Sansone4Girolamo Di Francia5ENEA CR-Portici, Energy Technologies and Renewable Sources Department, Portici, 80055, ItalyUniversity of Naples Federico II, Electrical Engineering and Information Technology Department, Napoli, 80125, ItalyUniversity of Naples Federico II, Electrical Engineering and Information Technology Department, Napoli, 80125, Italy; Corresponding author.ENEA CR-Portici, Energy Technologies and Renewable Sources Department, Portici, 80055, ItalyUniversity of Naples Federico II, Electrical Engineering and Information Technology Department, Napoli, 80125, ItalyENEA CR-Portici, Energy Technologies and Renewable Sources Department, Portici, 80055, ItalyThe accurate prediction of photovoltaic (PV) energy production is a crucial task to optimise the integration of solar energy into the power grid and maximise the benefit of renewable source trading in the energy market. This paper systematically and quantitatively analyses the literature by comparing different machine learning techniques and the impact of different meteorological forecast providers. The methodology consists of an irradiance model coupled with a meteorological provider; this combination removes the constraint of a local irradiance measurement. The result is a Transformer Neural Network architecture, trained and tested using OpenMeteo data, whose performance is superior to other combinations, providing a MAE of 1.22 kW (0.95%), and a MAPE of 2.21%. The implications of our study suggest that adopting a comprehensive approach, integrating local weather data, modelled irradiance, and PV plant configuration data, can significantly improve the accuracy of PV power forecasting, thus contributing to more effective technological and economic integration.http://www.sciencedirect.com/science/article/pii/S2666546824001101PhotovoltaicForecastingDeep learningTransformers |
| spellingShingle | Gabriele Piantadosi Sofia Dutto Antonio Galli Saverio De Vito Carlo Sansone Girolamo Di Francia Photovoltaic power forecasting: A Transformer based framework Energy and AI Photovoltaic Forecasting Deep learning Transformers |
| title | Photovoltaic power forecasting: A Transformer based framework |
| title_full | Photovoltaic power forecasting: A Transformer based framework |
| title_fullStr | Photovoltaic power forecasting: A Transformer based framework |
| title_full_unstemmed | Photovoltaic power forecasting: A Transformer based framework |
| title_short | Photovoltaic power forecasting: A Transformer based framework |
| title_sort | photovoltaic power forecasting a transformer based framework |
| topic | Photovoltaic Forecasting Deep learning Transformers |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001101 |
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