Mayer, Martin János and Yang, Dazhi and Markovics, Dávid (2025) The complexity and dimensionality of making deterministic photovoltaic power forecasts from ensemble numerical weather prediction. ENERGY CONVERSION AND MANAGEMENT, 344. No.-120303. ISSN 0196-8904 (In Press)
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Thecomplexityanddimensionalityofmakingdeterministicphotovoltaicpowerforecastsfromensemblenumericalweatherprediction.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
Abstract
Ensemble numerical weather prediction (NWP) constitutes a fundamental and reliable way of creating weather forecasts and quantifying their uncertainty. However, converting ensemble solar irradiance forecasts to deterministic photovoltaic (PV) power forecasts is associated with two challenging characteristics, that is, complexity and dimensionality. Complexity is introduced because of the necessary involvement of physical model chains and post-processing tools, both of which require in-depth knowledge of energy meteorology. Dimensionality, on the other hand, arises because one can freely cascade model chains and post-processing tools, each having many alternatives, into 16 distinct conversion workflows, in that, the possibilities multiply. When machine learning is involved, in one way or another, the situation becomes more convoluted. This work provides empirical evidence on the optimal workflow of making deterministic PV power forecasts from ensemble NWP, using four-year data from five utility-scale PV plants in Hungary alongside ensemble NWP forecasts from the European Centre of Medium-Range Weather Forecasts. It is found that (1) using ensemble NWP results in a 5% error reduction over just using deterministic NWP, and (2) bias-correcting the final PV power forecasts is the only indispensable stage of the workflow, which suggests that post-processing irradiance forecasts is not really needed, insofar as the final goal is to forecast PV power. © 2025 The Authors
| Item Type: | Article |
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| Additional Information: | Acknowledgment: The authors thank Norbert Péter from MVM Green Generation Ltd. for the PV plant design and production data and the HungaroMet Hungarian Meteorological Service for providing access to the ECMWF’s Meteorological Archival and Retrieval System. This work was supported by the National Research, Development and Innovation Fund, project no. OTKA-FK 142702 and project no. KDP-IKT-2023-900-I1-00000957/0000003, and by the Hungarian Academy of Sciences through the Sustainable Development and Technologies National Programme (FFT NP FTA) and the János Bolyai Research Scholarship. |
| Uncontrolled Keywords: | Learning systems; Forecasting; uncertainty analysis; machine learning; CHAINS; Machine-learning; ENSEMBLE; ENSEMBLE; Weather forecasting; SOLAR IRRADIANCE; Dimensionality reduction; Solar power generation; numerical weather prediction; numerical weather prediction; Photovoltaics; Photovoltaics; Work-flows; Photovoltaic; post-processing; post-processing; Deterministics; photovoltaic power; Processing tools; POWER FORECAST; |
| Subjects: | T Technology / alkalmazott, műszaki tudományok > T2 Technology (General) / műszaki tudományok általában |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 16 Sep 2025 15:33 |
| Last Modified: | 16 Sep 2025 15:33 |
| URI: | https://real.mtak.hu/id/eprint/224374 |
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