论文标题
梯度提升机和仔细的预处理工作最好：Ashrae伟大的能量预测器III课程
Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned
论文作者
论文摘要
Ashrae Great Energy Preditionor III（GEPIII）竞赛于2019年底举行，是有史以来最大的机器学习竞赛之一，专注于建筑性能。它是在Kaggle平台上托管的，并产生了39,402次预测提交，前五名团队分配了25,000美元的奖金。本文概述了从参与者那里学到的教训，主要是从在比赛中排名前5％的球队中学到的。通过在线调查，对公开共享的意见书和笔记本的分析以及获胜团队的文档，从他们的经验中获得了各种见解。最佳的解决方案主要使用梯度提升机（GBM）树的模型，其中LightGBM软件包是最受欢迎的。调查参与者指出，预处理和特征提取阶段是创建最佳建模方法的最重要方面。所有调查受访者都使用Python作为其主要建模工具，通常使用Jupyter风格的笔记本作为开发环境。这些结论对于帮助未来建立能量计预测的研究和实际实施至关重要。
The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late 2019 as one of the largest machine learning competitions ever held focused on building performance. It was hosted on the Kaggle platform and resulted in 39,402 prediction submissions, with the top five teams splitting $25,000 in prize money. This paper outlines lessons learned from participants, mainly from teams who scored in the top 5% of the competition. Various insights were gained from their experience through an online survey, analysis of publicly shared submissions and notebooks, and the documentation of the winning teams. The top-performing solutions mostly used ensembles of Gradient Boosting Machine (GBM) tree-based models, with the LightGBM package being the most popular. The survey participants indicated that the preprocessing and feature extraction phases were the most important aspects of creating the best modeling approach. All the survey respondents used Python as their primary modeling tool, and it was common to use Jupyter-style Notebooks as development environments. These conclusions are essential to help steer the research and practical implementation of building energy meter prediction in the future.
