论文标题
XAI用于透明风力涡轮电源曲线模型
XAI for transparent wind turbine power curve models
论文作者
论文摘要
将环境条件转化为涡轮功率输出的准确风力涡轮机功率曲线模型对于风能扩展并履行其在全球能量过渡中提出的作用至关重要。尽管机器学习（ML）方法比参数，物理知识的方法具有显着优势，但它们通常因不透明的“黑匣子”而受到批评，这阻碍了他们在实践中的应用。我们应用Shapley值，一种流行的可解释的人工智能（XAI）方法，以及XAI的最新发现用于回归模型，以发现ML模型从操作风力涡轮机数据中学到的策略。我们的发现表明，在对测试设置性能的关注驱动的驱动到越来越大的模型体系结构的趋势可能会导致物理上难以置信的模型策略。因此，我们呼吁XAI方法在模型选择中更为重要。此外，我们提出了一种实用方法来利用风力涡轮绩效监测的背景下的解释来进行根本原因分析。这可以帮助减少停机时间并增加涡轮机中涡轮机的利用。
Accurate wind turbine power curve models, which translate ambient conditions into turbine power output, are crucial for wind energy to scale and fulfill its proposed role in the global energy transition. While machine learning (ML) methods have shown significant advantages over parametric, physics-informed approaches, they are often criticised for being opaque 'black boxes', which hinders their application in practice. We apply Shapley values, a popular explainable artificial intelligence (XAI) method, and the latest findings from XAI for regression models, to uncover the strategies ML models have learned from operational wind turbine data. Our findings reveal that the trend towards ever larger model architectures, driven by a focus on test set performance, can result in physically implausible model strategies. Therefore, we call for a more prominent role of XAI methods in model selection. Moreover, we propose a practical approach to utilize explanations for root cause analysis in the context of wind turbine performance monitoring. This can help to reduce downtime and increase the utilization of turbines in the field.
