对比不同特征组合，发现输出风速能提升10%预测准确率
Analysis of Learning-based Offshore Wind Power Prediction Models with Various Feature Combinations
- 用风速作输出比用风功率预测更准
- 多特征输入相比单特征提升不明显
- 适合风电场选址与模型优化参考
精确的风速预测对海上风电场选址设计至关重要。本文通过分析墨西哥湾附近海域的气象数据，研究多种机器学习模型在预测海上风电功率方面的表现。收集并预处理气象数据后，设计了九种不同的输入特征组合，评估其对多高度风功率预测的影响。结果表明，以风速作为输出特征可使预测准确率提升约10%，而多特征输入相比单特征输入改进不显著，主要由于关键特征间相关性较差及模型泛化能力有限。研究强调了输出特征选择的重要性，并为未来风电预测模型与转换技术提供了重要参考。
原文摘要 · Abstract (English)
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.
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