论文标题
通过误差纠正转移学习的知识蒸馏进行风能预测
Knowledge distillation with error-correcting transfer learning for wind power prediction
论文作者
论文摘要
风能预测，尤其是对于涡轮机，对于电力公司的运营，可控性和经济至关重要。将高级数据科学与天气预报结合的混合方法已逐渐应用于预测。然而，单独对大型涡轮机进行建模，从头开始对涡轮机尺寸的天气预报进行缩小范围既不容易，也不是经济的。针对它，本文提出了一个新颖的框架，该框架具有用于涡轮力量预测的数学基础。该框架是第一次将知识蒸馏纳入能量预测中，从而通过从建立的公园模型中学习知识来实现涡轮模型的准确和经济结构。此外，公园规模的天气预报非明显地通过转移预测功率错误的学习来映射到涡轮机，从而实现模型校正以提高性能。所提出的框架部署在北极风园中各种地形的五个涡轮机上，对消融调查的竞争对手进行了评估。主要发现表明，根据有利的知识蒸馏和转移学习参数调整而开发的拟议框架可使绩效从3.3％提高到23.9％。在风能物理和计算效率方面，此优势也存在，这些优势通过预测质量速率和计算时间来验证。
Wind power prediction, especially for turbines, is vital for the operation, controllability, and economy of electricity companies. Hybrid methodologies combining advanced data science with weather forecasting have been incrementally applied to the predictions. Nevertheless, individually modeling massive turbines from scratch and downscaling weather forecasts to turbine size are neither easy nor economical. Aiming at it, this paper proposes a novel framework with mathematical underpinnings for turbine power prediction. This framework is the first time to incorporate knowledge distillation into energy forecasting, enabling accurate and economical constructions of turbine models by learning knowledge from the well-established park model. Besides, park-scale weather forecasts non-explicitly are mapped to turbines by transfer learning of predicted power errors, achieving model correction for better performance. The proposed framework is deployed on five turbines featuring various terrains in an Arctic wind park, the results are evaluated against the competitors of ablation investigation. The major findings reveal that the proposed framework, developed on favorable knowledge distillation and transfer learning parameters tuning, yields performance boosts from 3.3 % to 23.9 % over its competitors. This advantage also exists in terms of wind energy physics and computing efficiency, which are verified by the prediction quality rate and calculation time.
