Analysis of Learning-based Offshore Wind Power Prediction Models with Various Feature Combinations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913742180057088 |
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| author | Fang, Linhan Jiang, Fan Toms, Ann Mary Li, Xingpeng |
| author_facet | Fang, Linhan Jiang, Fan Toms, Ann Mary Li, Xingpeng |
| contents | Accurate wind speed prediction is crucial for designing and selecting sites for offshore wind farms. This paper investigates the effectiveness of various machine learning models in predicting offshore wind power for a site near the Gulf of Mexico by analyzing meteorological data. After collecting and preprocessing meteorological data, nine different input feature combinations were designed to assess their impact on wind power predictions at multiple heights. The results show that using wind speed as the output feature improves prediction accuracy by approximately 10% compared to using wind power as the output. In addition, the improvement of multi-feature input compared with single-feature input is not obvious mainly due to the poor correlation among key features and limited generalization ability of models. These findings underscore the importance of selecting appropriate output features and highlight considerations for using machine learning in wind power forecasting, offering insights that could guide future wind power prediction models and conversion techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analysis of Learning-based Offshore Wind Power Prediction Models with Various Feature Combinations Fang, Linhan Jiang, Fan Toms, Ann Mary Li, Xingpeng Signal Processing Machine Learning Applications Accurate wind speed prediction is crucial for designing and selecting sites for offshore wind farms. This paper investigates the effectiveness of various machine learning models in predicting offshore wind power for a site near the Gulf of Mexico by analyzing meteorological data. After collecting and preprocessing meteorological data, nine different input feature combinations were designed to assess their impact on wind power predictions at multiple heights. The results show that using wind speed as the output feature improves prediction accuracy by approximately 10% compared to using wind power as the output. In addition, the improvement of multi-feature input compared with single-feature input is not obvious mainly due to the poor correlation among key features and limited generalization ability of models. These findings underscore the importance of selecting appropriate output features and highlight considerations for using machine learning in wind power forecasting, offering insights that could guide future wind power prediction models and conversion techniques. |
| title | Analysis of Learning-based Offshore Wind Power Prediction Models with Various Feature Combinations |
| topic | Signal Processing Machine Learning Applications |
| url | https://arxiv.org/abs/2503.13493 |