In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. This ensemble approach outperforms individual models, ensuring stable and accurate power output predictions. Additionally, machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies and health assessment. Crucially, our analysis reveals the uniqueness of each wind turbine, necessitating tailored models for optimal predictions. These insight underscores the importance of providing automatized customization for different turbines to keep human modeling effort low. Importantly, the methodologies developed in this analysis are not limited to wind turbines; they can be extended to predict and optimize performance in various machinery, highlighting the versatility and applicability of our research across diverse industrial contexts.

翻译：本研究利用多台风力发电机组的SCADA数据，通过先进的时间序列分析方法——功能神经网络（FNN）与长短期记忆网络（LSTM）——实现功率预测。核心创新在于构建FNN与LSTM模型的集成框架，充分发挥其协同学习能力。该集成方法优于单一模型，可提供稳定且精准的功率输出预测。同时，采用机器学习技术检测风力发电机组的性能退化现象，为主动维护策略及健康评估提供支撑。关键发现表明，每台风力发电机组均具有独特性，需为其定制专属预测模型。这一发现凸显了通过自动化定制方案降低人工建模成本的必要性。值得强调的是，本研究开发的预测方法不仅适用于风力发电领域，还可推广至各类机械设备的性能预测与优化，充分展现其在多元工业场景中的普适性与应用价值。