发表机构
University of Nottingham; University of Granada; DaSCI Andalusian Institute in Data Science and Computational Intelligence; Intelligent Plant; University of Leeds（诺丁汉大学; 格拉纳达大学; 安达卢西亚数据科学与计算智能研究所（DaSCI）; 智能工厂公司; 利兹大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用天气数据训练人工神经网络模型，准确预测风力涡轮机功率（R2=0.98），优于基线，并用于识别低功率维护窗口，每次可节省约2000千瓦。
AI 中文摘要
海上风力涡轮机广泛用于产生可再生能源，但其维护可能因强制停机而导致效率下降。准确的风力涡轮机功率预测可以识别低功率时段，这些时段是安排维护的理想选择。然而，数据量、特征选择和数据预处理对此类功率预测模型性能的影响尚未得到深入研究。此外，当前模型在不同风力涡轮机之间的可迁移性有限。因此，本研究开发了一个基线线性回归模型，用于与更复杂的人工神经网络模型进行性能比较，以预测风力涡轮机的功率输出，仅使用天气条件以增强适用性。研究了一系列数据预处理技术，并分别使用一个月和一年的数据训练模型，以确定数据预处理和数据量对模型性能的影响。使用随机森林回归器探索特征选择。不同模型的最佳结果表明，人工神经网络模型提供了最高的准确性，R2分数为0.98，平均绝对误差低至194，而基线模型的R2分数为0.94，平均绝对误差为441。模型性能与以往研究的范围相当，其优势在于所提出的方法利用了附近气象站的独立天气数据集，使得未来能够应用于不同位置的类似风力涡轮机。然后，使用人工神经网络模型识别了2个月内4小时的低功率预测时段（模拟未来时段的应用），为每次维护事件提供了约2000千瓦的功率输出节省。
英文摘要
Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns. Accurate wind turbine power predictions can identify periods of low power that would be ideal for scheduling maintenance. However, the effects of data volume, feature selection, and data preprocessing on the performance of such power prediction models have not been thoroughly studied. Besides, current models have limited transferability between different wind turbines. Therefore, this study developed a baseline Linear Regression for performance comparison with a more complex Artificial Neural Network model to predict the power output of a wind turbine, using weather conditions only to enhance applicability. A range of data preprocessing techniques were studied, and models were trained on one month and one year of data to determine the effects of data preprocessing and volume on model performance. Feature selection was explored using a Random Forest Regressor. The best results from the different models showed that the Artificial Neural Network models provided the highest accuracy, with an R2 score of 0.98 and a low Mean Absolute Error of 194, when compared with the baseline model (R2 score of 0.94 and Mean Absolute Error of 441). The model performance is comparable to the range of results in past studies, with the advantage that the proposed method leverages a separate weather dataset from a nearby weather station, enabling future applications for similar wind turbines in different locations. The Artificial Neural Network model was then used to identify 4-h periods of low power predictions over 2 months (simulating application for future periods), providing power output savings of approximately 2000 kW for each maintenance event.
