论文标题
KDD杯2022风能预测团队88VIP解决方案
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution
论文作者
论文摘要
KDD杯2022提出了有关空间动态风能数据集的时间序列预测任务，其中要求参与者预测给定历史上下文因素的未来一代。评估指标包含RMSE和MAE。本文介绍了团队88VIP的解决方案，该解决方案主要包括两种模型：梯度增强决策树，以记住基本数据模式和复发性神经网络，以捕获深层和潜在的概率过渡。结合这些模型有助于应对风能的波动，以及训练子模型对预测的异质时间尺度（从几分钟到几天）的杰出特性的目标。此外，还详细介绍了功能工程，插补技术和离线评估的设计。拟议的解决方案在第3阶段的总体在线得分为-45.213。
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.
