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 (this https URL) for reproduction.

本文介绍了一种使用空间 - 时间图神经网络建立的风力发电预测模型。本方法在 2022 年度百度 KDD 大赛中取得了较好的成绩，并通过将 AGCRN 和 MTGNN 两种模型进行集成得到更好的结果，测试集的 RMSE 分数为 - 45.36026。