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

• URL: http://arxiv.org/abs/2302.11159v1
• Date: Wed, 22 Feb 2023 05:47:45 GMT
• Title: BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022
• Authors: Jiawei Jiang, Chengkai Han, Jingyuan Wang
• Abstract summary: We present our solution for the Baidu KDD Cup 2022 Spatial Dynamic Wind Power Forecasting Challenge. We adopt two spatial-temporal graph neural network models, i.e., AGCRN and MTGNN, as our basic models. Using our method, our team team achieves -45.36026 on the test set.
• Score: 22.15262401549984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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