In this technical report, we present our solution for the Baidu KDD Cup 2022 Spatial Dynamic Wind Power Forecasting Challenge. Wind power is a rapidly growing source of clean energy. Accurate wind power forecasting is essential for grid stability and the security of supply. Therefore, organizers provide a wind power dataset containing historical data from 134 wind turbines and launch the Baidu KDD Cup 2022 to examine the limitations of current methods for wind power forecasting. The average of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) is used as the evaluation score. We adopt two spatial-temporal graph neural network models, i.e., AGCRN and MTGNN, as our basic models. We train AGCRN by 5-fold cross-validation and additionally train MTGNN directly on the training and validation sets. Finally, we ensemble the two models based on the loss values of the validation set as our final submission. Using our method, our team \team achieves -45.36026 on the test set. We release our codes on Github (https://github.com/BUAABIGSCity/KDDCUP2022) for reproduction.

翻译：在本技术报告中，我们介绍了针对百度KDD Cup 2022空间动态风电功率预测挑战赛的解决方案。风电是一种快速发展的清洁能源，准确的风电功率预测对于电网稳定性和供电安全性至关重要。为此，赛事组织者提供了一个包含134台风机历史数据的风电数据集，并举办百度KDD Cup 2022以评估现有风电功率预测方法的局限性。评分指标采用RMSE（均方根误差）与MAE（平均绝对误差）的平均值。我们采用两种时空图神经网络模型——AGCRN和MTGNN作为基础模型。通过五折交叉验证训练AGCRN，并额外在训练集和验证集上直接训练MTGNN。最终，我们基于验证集的损失值对两个模型进行集成作为最终提交方案。采用该方法，我们的团队在测试集上取得了-45.36026的成绩。我们已在GitHub（https://github.com/BUAABIGSCity/KDDCUP2022）上开源代码以供复现。