A comparative analysis of real and theoretical data in offshore wind energy generation
Wind energy plays a key role in the global shift towards renewable energy, requiring accurate prediction models for integration with power grids and effective energy distribution. This study validates the accuracy of wind speed forecasts from three widely used sources – European Centre for Medium-Ra...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671125000087 |
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| author | Fernando M. Camilo Paulo J. Santos Armando J. Pires |
| author_facet | Fernando M. Camilo Paulo J. Santos Armando J. Pires |
| author_sort | Fernando M. Camilo |
| collection | DOAJ |
| description | Wind energy plays a key role in the global shift towards renewable energy, requiring accurate prediction models for integration with power grids and effective energy distribution. This study validates the accuracy of wind speed forecasts from three widely used sources – European Centre for Medium-Range Weather Forecasts (ERA5), Modern-Era Retrospective Analysis for Research and Applications, MERRA-2 (NASA), and the Wind Atlas – against actual power generation data from the WindFloat Atlantic offshore wind farm near Viana do Castelo, Portugal, over the years 2022 and 2023. The results show that NASA’s forecasts were the most precise, with annual relative errors of 5 % for 2022 and 1.6% for 2023, outperforming the other models. This analysis underscores the importance of validated forecasting models to enhance renewable energy management through multi-year data for precise local calibration. The findings also emphasize the necessity of consistent short-term load forecasting models for reliable daily energy production. Overall, this research demonstrates that combining global wind datasets with local validation improves offshore wind prediction accuracy. In this context, NASA’s dataset emerges as the most reliable for operational and planning purposes in offshore renewable energy systems. |
| format | Article |
| id | doaj-art-d140518ebe2a4f3282b7e60ee1a3d2bf |
| institution | Kabale University |
| issn | 2772-6711 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-d140518ebe2a4f3282b7e60ee1a3d2bf2025-01-24T04:46:03ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112025-03-0111100901A comparative analysis of real and theoretical data in offshore wind energy generationFernando M. Camilo0Paulo J. Santos1Armando J. Pires2Instituto Politécnico de Setúbal, Escola Superior de Tecnologia de Setúbal, Campus do IPS, Estefanilha, 2910-761, Setúbal, Portugal; MARE - Marine and Environmental Sciences Centre, Escola Superior de Tecnologia de Setúbal, Instituto Politécnico de Setúbal, Campus do IPS, Estefanilha, 2910-761, Setúbal, Portugal; INESC-ID/IST, University of Lisbon, 1000-029, Lisbon, Portugal; Corresponding author.Instituto Politécnico de Setúbal, Escola Superior de Tecnologia de Setúbal, Campus do IPS, Estefanilha, 2910-761, Setúbal, Portugal; MARE - Marine and Environmental Sciences Centre, Escola Superior de Tecnologia de Setúbal, Instituto Politécnico de Setúbal, Campus do IPS, Estefanilha, 2910-761, Setúbal, PortugalInstituto Politécnico de Setúbal, Escola Superior de Tecnologia de Setúbal, Campus do IPS, Estefanilha, 2910-761, Setúbal, Portugal; CTS-UNINOVA, LASI, FCT/UNL, 2829-517, Caparica, PortugalWind energy plays a key role in the global shift towards renewable energy, requiring accurate prediction models for integration with power grids and effective energy distribution. This study validates the accuracy of wind speed forecasts from three widely used sources – European Centre for Medium-Range Weather Forecasts (ERA5), Modern-Era Retrospective Analysis for Research and Applications, MERRA-2 (NASA), and the Wind Atlas – against actual power generation data from the WindFloat Atlantic offshore wind farm near Viana do Castelo, Portugal, over the years 2022 and 2023. The results show that NASA’s forecasts were the most precise, with annual relative errors of 5 % for 2022 and 1.6% for 2023, outperforming the other models. This analysis underscores the importance of validated forecasting models to enhance renewable energy management through multi-year data for precise local calibration. The findings also emphasize the necessity of consistent short-term load forecasting models for reliable daily energy production. Overall, this research demonstrates that combining global wind datasets with local validation improves offshore wind prediction accuracy. In this context, NASA’s dataset emerges as the most reliable for operational and planning purposes in offshore renewable energy systems.http://www.sciencedirect.com/science/article/pii/S2772671125000087Wind offshoreRenewable energyWind energy forecastingWind power predictive models |
| spellingShingle | Fernando M. Camilo Paulo J. Santos Armando J. Pires A comparative analysis of real and theoretical data in offshore wind energy generation e-Prime: Advances in Electrical Engineering, Electronics and Energy Wind offshore Renewable energy Wind energy forecasting Wind power predictive models |
| title | A comparative analysis of real and theoretical data in offshore wind energy generation |
| title_full | A comparative analysis of real and theoretical data in offshore wind energy generation |
| title_fullStr | A comparative analysis of real and theoretical data in offshore wind energy generation |
| title_full_unstemmed | A comparative analysis of real and theoretical data in offshore wind energy generation |
| title_short | A comparative analysis of real and theoretical data in offshore wind energy generation |
| title_sort | comparative analysis of real and theoretical data in offshore wind energy generation |
| topic | Wind offshore Renewable energy Wind energy forecasting Wind power predictive models |
| url | http://www.sciencedirect.com/science/article/pii/S2772671125000087 |
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