发表机构
H. Milton Stewart School of Industrial and Systems Engineering; Georgia Institute of Technology（H. Milton Stewart工业与系统工程学院; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何将基于运营风电场数据训练的功率曲线模型转移到新农场，提出基于域适应的迁移学习方法，实验表明该方法在跨农场功率预测上显著优于其他方法。
AI 中文摘要
风能行业依靠精确的功率曲线模型进行功率预测、评估涡轮机性能、量化升级或支持选址规划决策。本文聚焦于选址规划功率曲线，研究如何将基于运营风电场涡轮机数据训练的功率曲线模型转移到新的未开发农场。风能文献中的传统方法依赖距离、布局或地形特征进行跨农场功率曲线转移。通过域适应的视角，我们提出一种更可靠的跨农场功率曲线建模的迁移学习方法。在跨农场应用中，一个域由时间环境变量和空间地形变量指定。域适应是找到一个合适的相似性度量，以使新农场的域适应现有农场的域。实证结果表明，我们的域适应功率曲线在选址规划功率预测方面始终显著优于竞争方法。
英文摘要
The wind energy industry relies on accurate power curve models to make power forecast, evaluate turbine performance, quantify upgrade, or support site-planning decisions. In this paper, we focus on site-planning power curves, i.e., we investigate how power curve models trained using turbine data on an operating wind farm can be transferred to a new, undeveloped farm. The traditional wisdom in the wind energy literature relies on distance, layout, or terrain characteristics for making cross-farm power curve transfer. Through the lens of domain adaptation, we propose a more reliable transfer learning approach for cross-farm power curve modeling. In the cross-farm applications, a domain is specified by the temporal environmental variates and spatial terrain variables. Domain adaptation is to find a capable similarity metric to adapt the domain on the new farm to that on the existing farm. Empirical results show that our domain adapted power curve consistently outperforms competing approaches by an appreciable margin for site-planning power predictions.
CommentsSubmitted to Renewable Energy
