建筑数据基因组计划2，来自ASHRAE Great Energy Predictor III竞赛的能源表数据
The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition
摘要：
本文描述了来自1，636栋非住宅建筑的3，053个电表的开放数据集，范围为两年（2016年和2017年），每小时频率（每米17，544次测量，约5360万次测量）。这些仪表收集自北美和欧洲的19个地点，每栋建筑有一个或多个仪表测量整个建筑的电气，加热和冷却水，蒸汽和太阳能以及水和灌溉仪表。这些数据的一部分用于2019年12月由ASHRAE组织主办的Great Energy Predictor III（GEPIII）竞赛。GEPIII是一个长期预测的机器学习竞赛，用于测量和验证。本文描述了时间序列仪表数据、建筑物元数据和补充气象数据的数据采集、清洗和收敛过程。该数据集可用于进一步的预测基准测试和原型设计，以及异常检测，能源分析和建筑类型分类。
英文摘要：
This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data were used in the Great Energy Predictor III (GEPIII) competition hosted by the ASHRAE organization in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
