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.
翻译:KDD CUP 2022 提出了空间动态风能数据集的时间序列预测任务,要求参与者根据历史背景因素预测未来一代。评价指标包含RMSE和MAE。本文介绍了88VIP小组的解决方案,主要包括两类模型:一个梯度增强决策树,用于记忆基本数据模式,一个经常性神经网络,以捕捉深层和潜在概率变化。这些模型的组合有助于应对风力波动,并培训关于不同预测时间尺度中不同特性的子模型,从几分钟到几天。此外,还详细介绍了地貌工程、估算技术和离线评价的设计。拟议解决方案在第三阶段实现总线分-45.213。