Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available
Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheles...
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MDPI AG
2025-06-01
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| author | María José Pérez-Molina José A. Carta |
| author_facet | María José Pérez-Molina José A. Carta |
| author_sort | María José Pérez-Molina |
| collection | DOAJ |
| description | Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately capture the local characteristics of wave energy at specific sites. This study proposes a supervised machine-learning (ML) approach to estimate long-term wave energy at locations with only short-term in situ measurements. The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. As a case study, hourly mean significant wave height and energy period data from 2000 to 2023 were analyzed, collected by a deep-water buoy off the coast of Gran Canaria (Canary Islands, Spain). Among the ML techniques evaluated, Multiple Linear Regression (MLR) and Support Vector Regression yielded the most favorable error metrics. MLR was selected due to its lower computational complexity, greater interpretability, and ease of implementation, aligning with the principle of parsimony, particularly in contexts where model transparency is essential. The MLR model achieved a mean absolute error (<i>MAE</i>) of 2.56 kW/m and a root mean square error (<i>RMSE</i>) of 4.49 kW/m, significantly outperforming the direct use of ERA5 data, which resulted in an <i>MAE</i> of 4.38 kW/m and an <i>RMSE</i> of 7.1 kW/m. These findings underscore the effectiveness of the proposed approach in enhancing long-term wave energy estimations using limited in situ data. |
| format | Article |
| id | doaj-art-07b67df73c614d3b881b8e64dfb1d00c |
| institution | Matheson Library |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-07b67df73c614d3b881b8e64dfb1d00c2025-06-25T14:01:50ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01136119410.3390/jmse13061194Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are AvailableMaría José Pérez-Molina0José A. Carta1Oceanic Platform of the Canary Islands (PLOCAN), 35214 Telde, SpainGroup for the Research on Renewable Energy Systems (GRRES), Department of Mechanical Engineering, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainWave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately capture the local characteristics of wave energy at specific sites. This study proposes a supervised machine-learning (ML) approach to estimate long-term wave energy at locations with only short-term in situ measurements. The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. As a case study, hourly mean significant wave height and energy period data from 2000 to 2023 were analyzed, collected by a deep-water buoy off the coast of Gran Canaria (Canary Islands, Spain). Among the ML techniques evaluated, Multiple Linear Regression (MLR) and Support Vector Regression yielded the most favorable error metrics. MLR was selected due to its lower computational complexity, greater interpretability, and ease of implementation, aligning with the principle of parsimony, particularly in contexts where model transparency is essential. The MLR model achieved a mean absolute error (<i>MAE</i>) of 2.56 kW/m and a root mean square error (<i>RMSE</i>) of 4.49 kW/m, significantly outperforming the direct use of ERA5 data, which resulted in an <i>MAE</i> of 4.38 kW/m and an <i>RMSE</i> of 7.1 kW/m. These findings underscore the effectiveness of the proposed approach in enhancing long-term wave energy estimations using limited in situ data.https://www.mdpi.com/2077-1312/13/6/1194Measure–Correlate–Predictwave energymachine learningreanalysis datawave periodsignificant wave height |
| spellingShingle | María José Pérez-Molina José A. Carta Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available Journal of Marine Science and Engineering Measure–Correlate–Predict wave energy machine learning reanalysis data wave period significant wave height |
| title | Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available |
| title_full | Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available |
| title_fullStr | Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available |
| title_full_unstemmed | Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available |
| title_short | Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available |
| title_sort | use of machine learning techniques to estimate long term wave power at a target site where short term data are available |
| topic | Measure–Correlate–Predict wave energy machine learning reanalysis data wave period significant wave height |
| url | https://www.mdpi.com/2077-1312/13/6/1194 |
| work_keys_str_mv | AT mariajoseperezmolina useofmachinelearningtechniquestoestimatelongtermwavepoweratatargetsitewhereshorttermdataareavailable AT joseacarta useofmachinelearningtechniquestoestimatelongtermwavepoweratatargetsitewhereshorttermdataareavailable |