Wind energy is a widely distributed, renewable, and environmentally friendly energy source that plays a crucial role in mitigating global warming and addressing energy shortages. Nevertheless, wind power generation is characterized by volatility, intermittence, and randomness, which hinder its ability to serve as a reliable power source for the grid. Accurate wind power forecasting is crucial for developing a new power system that heavily relies on renewable energy sources. However, traditional wind power forecasting systems primarily focus on ultra-short-term or short-term forecasts, limiting their ability to address the diverse adjustment requirements of the power system simultaneously. To overcome these challenges, We propose an automatic framework capable of forecasting wind power across multi-time scale. The framework based on the tree-structured Parzen estimator (TPE) and temporal fusion transformer (TFT) that can provide ultra-short-term, short-term and medium-term wind power forecasting power.Our approach employs the TFT for wind power forecasting and categorizes features based on their properties. Additionally, we introduce a generic algorithm to simultaneously fine-tune the hyperparameters of the decomposition method and model. We evaluate the performance of our framework by conducting ablation experiments using three commonly used decomposition algorithms and six state-of-the-art models for forecasting multi-time scale. The experimental results demonstrate that our proposed method considerably improves prediction accuracy on the public dataset Engie https://opendata-renewables.engie.com. Compared to the second-best state-of-the-art model, our approach exhibits a reduction of 31.75% and 28.74% in normalized mean absolute error (nMAE) for 24-hour forecasting, and 20.79% and 16.93% in nMAE for 48-hour forecasting, respectively.

翻译：风能是一种分布广泛、可再生且环境友好的能源，在缓解全球变暖和解决能源短缺问题中发挥着关键作用。然而，风电出力具有波动性、间歇性和随机性，这使其难以作为电网的可靠电源。精确的风电功率预测对于构建以可再生能源为主体的新型电力系统至关重要。但传统风电功率预测系统主要聚焦于超短期或短期预测，难以同时满足电力系统多样化的调节需求。为克服这些挑战，本文提出一种能够跨多时间尺度进行风电功率预测的自动框架。该框架基于树结构帕尔森估计器（TPE）和时间融合变换器（TFT），可提供超短期、短期和中期风电功率预测。我们采用TFT进行风电功率预测，并根据特征属性对其进行分类。此外，我们引入一种通用算法来同步微调分解方法和模型的超参数。通过使用三种常用分解算法和六种最先进模型进行多时间尺度预测的消融实验，评估了所提框架的性能。实验结果表明，我们的方法在公开数据集Engie https://opendata-renewables.engie.com 上显著提升了预测精度。与次优的最先进模型相比，我们的方法在24小时预测中的归一化平均绝对误差（nMAE）分别降低了31.75%和28.74%，在48小时预测中分别降低了20.79%和16.93%。