KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution
This is an incremental solution for a specific competition task in wind power forecasting, aimed at participants in the KDD CUP 2022 challenge.
The paper tackles wind power forecasting by combining gradient boosting decision trees and recurrent neural networks to handle fluctuations and heterogeneous timescales, achieving an overall online score of -45.213 in Phase 3 of the KDD CUP 2022 competition.
KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors. The evaluation metrics contain RMSE and MAE. This paper describes the solution of Team 88VIP, which mainly comprises two types of models: a gradient boosting decision tree to memorize the basic data patterns and a recurrent neural network to capture the deep and latent probabilistic transitions. Ensembling these models contributes to tackle the fluctuation of wind power, and training submodels targets on the distinguished properties in heterogeneous timescales of forecasting, from minutes to days. In addition, feature engineering, imputation techniques and the design of offline evaluation are also described in details. The proposed solution achieves an overall online score of -45.213 in Phase 3.