论文标题
建筑物数据基因组项目2，来自Ashrae的能量表数据，伟大的能量预测器III竞赛
The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition
论文作者
论文摘要
本文介绍了一个每小时频率为1,636座非住宅建筑（2016年和2017年）的开放数据集，该数据集为3,053个能量表，每小时为每小时两年（2016年和2017年）（每米17,544个测量值，可实现约5360万次测量）。这些仪表是从北美和欧洲的19个地点收集的，每个建筑物的一个或多个米，可测量整个建筑物的电气，供暖和冷却水，蒸汽以及太阳能，以及水和灌溉表。这些数据的一部分用于ASHRAE组织在2019年10月至12月举办的伟大能源预测器III（GEPIII）竞赛中。GEPIII是一项机器学习竞赛，进行了长期预测，并应用了测量和验证。本文介绍了数据收集，清洁和时间序列数据的收敛过程，有关建筑物的元数据以及互补的天气数据的过程。该数据集可用于进一步的预测基准测试和原型，以及异常检测，能量分析和建筑类型分类。
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.
