The temporal dependencies of wind power are significant to be involved in the modeling of short-term wind power forecasts. However, different time series inputs will contribute differently to the forecasting performance and bring in challenges to the selection of the relevant driving information. In this paper, a Multi-Source and Temporal Attention Network (MSTAN) is proposed for short-term wind power probabilistic prediction. The MSTAN model introduces multi-source NWP and makes three specific designs to improve prediction performance. Firstly, a novel multi-source variable attention module is proposed to select the driving variables of NWP. Secondly, a temporal attention module is used to capture the implicit temporal dependency hidden in the historical measurements and multi-source NWP sequence. Thirdly, the residual module is wrapped in MSTAN to skip some unnecessary nonlinear transformations and provide adaptive complexity to the entire model. After training, multi-horizon density forecasts for the next 48 hours are yielded by MSTAN. The MSTAN is compared with state-of-the-art machine learning schemes in the wind power forecasting system using the operation data from 3 wind farms. We demonstrate that MSTAN outperforms other counterparts on both deterministic and probabilistic prediction. The structure design scheme of MSTAN has been proven effective.

中文翻译：

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用于概率风电预测的多源和时间注意网络

风电的时间依赖性对于短期风电预测建模非常重要。然而，不同的时间序列输入对预测性能的贡献不同，给相关驾驶信息的选择带来挑战。在本文中，提出了一种用于短期风电概率预测的多源时间注意网络（MSTAN）。MSTAN 模型引入了多源 NWP 并进行了三个具体设计以提高预测性能。首先，提出了一种新颖的多源变量注意力模块来选择 NWP 的驱动变量。其次，时间注意力模块用于捕获隐藏在历史测量和多源 NWP 序列中的隐式时间依赖性。第三，残差模块被包裹在 MSTAN 中以跳过一些不必要的非线性变换并为整个模型提供自适应复杂性。训练后，MSTAN 会生成接下来 48 小时的多层面密度预测。使用来自 3 个风电场的运行数据，将 MSTAN 与风电预测系统中最先进的机器学习方案进行了比较。我们证明 MSTAN 在确定性和概率预测方面都优于其他同行。MSTAN 的结构设计方案已被证明是有效的。使用来自 3 个风电场的运行数据，将 MSTAN 与风电预测系统中最先进的机器学习方案进行了比较。我们证明 MSTAN 在确定性和概率预测方面都优于其他同行。MSTAN 的结构设计方案已被证明是有效的。使用来自 3 个风电场的运行数据，将 MSTAN 与风电预测系统中最先进的机器学习方案进行了比较。我们证明 MSTAN 在确定性和概率预测方面都优于其他同行。MSTAN 的结构设计方案已被证明是有效的。