Wind energy is a widely distributed, recyclable and environmentally friendly energy source that plays an important role in mitigating global warming and energy shortages. Wind energy's uncertainty and fluctuating nature makes grid integration of large-scale wind energy systems challenging. Medium-term wind power forecasts can provide an essential basis for energy dispatch, so accurate wind power forecasts are essential. Much research has yielded excellent results in recent years. However, many of them require additional experimentation and analysis when applied to other data. In this paper, we propose a novel short-term forecasting framework by tree-structured parzen estimator (TPE) and decomposition algorithms. This framework defines the TPE-VMD-TFT method for 24-h and 48-h ahead wind power forecasting based on variational mode decomposition (VMD) and time fusion transformer (TFT). In the Engie wind dataset from the electricity company in France, the results show that the proposed method significantly improves the prediction accuracy. In addition, the proposed framework can be used to other decomposition algorithms and require little manual work in model training.

翻译：风能是一种分布广泛、可再生且环境友好的能源，在缓解全球变暖和能源短缺方面发挥着重要作用。风能的不确定性和波动性使得大规模风电系统的并网面临挑战。中期风电功率预测可为能源调度提供重要依据，因此准确的功率预测至关重要。近年来，许多研究取得了优异成果，但其中多数在应用于其他数据时需要额外的实验与分析。本文提出一种基于树形结构帕尔森估计（TPE）与分解算法的新型短期预测框架。该框架定义了基于变分模态分解（VMD）和时间融合Transformer（TFT）的TPE-VMD-TFT方法，用于未来24小时和48小时的风电功率预测。在法国电力公司Engie风电数据集上的实验结果表明，该方法显著提升了预测精度。此外，所提出的框架可适用于其他分解算法，且在模型训练过程中所需人工干预极少。