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KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic windpower dataset, in which the participants are required to predict the futuregeneration given the historical context factors. The evaluation metrics containRMSE and MAE. This paper describes the solution of Team 88VIP, which mainlycomprises two types of models: a gradient boosting decision tree to memorizethe basic data patterns and a recurrent neural network to capture the deep andlatent probabilistic transitions. Ensembling these models contributes to tacklethe fluctuation of wind power, and training submodels targets on thedistinguished properties in heterogeneous timescales of forecasting, fromminutes to days. In addition, feature engineering, imputation techniques andthe design of offline evaluation are also described in details. The proposedsolution achieves an overall online score of -45.213 in Phase 3.

KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution