发表机构
University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对风电爬坡事件预测难题，提出将风机运行数据转文本生成语义嵌入的混合集成学习方法，在多数据集多时间尺度验证了其有效性，同时开展不确定性感知评估。
AI 中文摘要
风电爬坡事件指短时间内风机输出功率发生的突然大幅波动，这类事件难以估计，标准模型常无法捕捉。本文构建了一种混合预测方法，为爬坡事件预测增添语义上下文。该方法未直接使用大规模语言模型预测风机运行数据，而是搭建了一套流程：将风机运行数据转换为简化文本，再转换为密集嵌入，作为输入与其他特征一同用于集成模型。在SDWPF数据集的多个时间间隔进行测试，包括10分钟、30分钟和60分钟的预测 horizon，其中爬坡事件对应未来功率输出的最大变化。通过Diebold-Mariano检验和自助法置信区间，对比自回归、LSTM、GRU基线模型及多种集成配置，同时调整爬坡阈值、用PCA压缩嵌入，并在Kaggle SCADA和NREL数据上进行外部验证及不确定性感知评分。在多组配对集成运行中，语义上下文特征相比基线模型产生了可忽略但统计显著的提升，在30分钟和60分钟预测 horizon 表现最明显，且这些提升在不同爬坡阈值定义下均成立，PCA压缩在部分长 horizon 案例中有所帮助。最佳的上下文增强集成模型整体排名靠前，不过GRU模型在30分钟和60分钟预测时仍保持最低的爬坡事件RMSE。外部测试证实误差减少可跨数据集推广，但提升幅度取决于模型和数据集。预测区间能良好覆盖多数测试案例，但在爬坡事件期间表现减弱，表明数据分布存在局部偏移。
英文摘要
Wind power ramp events which are sudden, large swings in turbine output over short windows are difficult to estimate, and standard models often miss them. Hybrid forecasting approach is built which augments semantic context to ramp-event forecast. Rather than applying an extensive language model directly to predict turbine operating data, we have implemented a pipeline where turbine operating data is converted to simplified text, which is then converted to dense embeddings to be used as inputs for ensemble models incorporated with other features. Testing runs are performed at multiple intervals within the SDWPF dataset, including 10-minute, 30-minute, and 60- minute horizons, with ramp events constituting the highest change in future power output. We check robustness against autoregressive, LSTM, and GRU baselines plus several ensemble configurations, using Diebold-Mariano tests and bootstrap confidence intervals, and we vary the ramp threshold, compress the embeddings with PCA, and validate externally on Kaggle SCADA and NREL data with uncertainty-aware scoring. The semantic-context features produce negligible yet statistically significant gains over the baselines in multiple paired ensemble runs, most clearly at the 30- and 60-minute horizons where these gains hold across different ramp-threshold definitions, and PCA compression helps in some longer-horizon cases. The best context- augmented ensembles rank near the top overall, though the GRU model still posts the lowest ramp-event RMSE at 30 and 60 minutes. External tests confirm the error reduction generalizes across datasets, but the size of the gain depends on both model and dataset. Prediction intervals cover most test cases well but weaken during ramp events, pointing to a localized shift in the data distribution.
