Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and correlation between steps is omitted in most multistep wind power forecasts. This paper is the first time that data augmentation is applied to wind power forecasting by systematically summarizing and proposing both physics-oriented and data-oriented time-series wind data augmentation approaches to considerably enlarge primary datasets, and develops deep encoder-decoder long short-term memory networks that enable sequential input and sequential output for wind power forecasting. The proposed augmentation techniques and forecasting algorithm are deployed on five turbines with diverse topographies in an Arctic wind park, and the outcomes are evaluated against benchmark models and different augmentations. The main findings reveal that on one side, the average improvement in RMSE of the proposed forecasting model over the benchmarks is 33.89%, 10.60%, 7.12%, and 4.27% before data augmentations, and increases to 40.63%, 17.67%, 11.74%, and 7.06%, respectively, after augmentations. The other side unveils that the effect of data augmentations on prediction is intricately varying, but for the proposed model with and without augmentations, all augmentation approaches boost the model outperformance from 7.87% to 13.36% in RMSE, 5.24% to 8.97% in MAE, and similarly over 12% in QR90. Finally, data-oriented augmentations, in general, are slightly better than physics-driven ones.

准确的风电功率预测对于风电场的运行和风能并网的调度起着至关重要的作用。凭借出色的大数据自动模式识别和非线性映射能力，深度学习越来越多地应用于风电预测。然而，突出的现实是现场测量的风数据相对昂贵且难以访问，并且在大多数多步风电预测中省略了步骤之间的相关性。本文首次将数据增强应用于风电功率预测，系统地总结并提出了面向物理和面向数据的时间序列风数据增强方法，显着扩大了原始数据集，并开发了深度编码器-解码器长短期记忆网络，可实现风电预测的顺序输入和顺序输出。所提出的增强技术和预测算法部署在北极风电场中具有不同地形的五台涡轮机上，并根据基准模型和不同的增强来评估结果。主要发现表明，一方面，在数据增强之前，所提出的预测模型的 RMSE 相对于基准的平均改进为 33.89%、10.60%、7.12% 和 4.27%，并增加到 40.63%、17.67%、11.74%和 7.06%，分别在增强后。另一边揭示了数据增强对预测的影响错综复杂，但对于有和没有增强的模型，所有的增强方法都将模型在 RMSE 中的表现从 7.87% 提高到 13.36%，在 MAE 中从 5.24% 提高到 8.97%，在 QR90 中同样超过 12%。最后，面向数据的增强通常比物理驱动的增强略好。