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

by   Jiawei Jiang, et al.

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 achieves -45.36026 on the test set. We release our codes on Github (https://github.com/BUAABIGSCity/KDDCUP2022) for reproduction.


SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022

The variability of wind power supply can present substantial challenges ...

KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution

KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic ...

Application of BERT in Wind Power Forecasting-Teletraan's Solution in Baidu KDD Cup 2022

Nowadays, wind energy has drawn increasing attention as its important ro...

First Place Solution of KDD Cup 2021 OGB Large-Scale Challenge Graph-Level Track

In this technical report, we present our solution of KDD Cup 2021 OGB La...

A Collection and Categorization of Open-Source Wind and Wind Power Datasets

Wind power and other forms of renewable energy sources play an ever more...

End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning

Wind turbine wake modelling is of crucial importance to accurate resourc...

Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network

Wind energy resource quantification, air pollution monitoring, and weath...

Please sign up or login with your details

Forgot password? Click here to reset