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

BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022