A three-year building operational performance dataset for informing energy efficiency

This dataset was curated from an office building constructed in 2015 in Berkeley, California, which includes whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, and occupant counts. The data was collected in three years from more than 300 sensors and meters for two office floors (each 2,325 m2) of the building. A three-step data curation strategy is applied to transform the raw data into the research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; (3) describing the metadata of the dataset using a semantic JSON schema. This dataset can be used for various types of applications, including building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls to improve understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions.

本数据集源自于位于加利福尼亚州伯克利的一座建于2015年的办公楼，涵盖了整个建筑及末端能源消耗、暖通空调系统运行状态、室内外环境参数以及占用人数。数据采集自超过300个传感器和仪表，持续三年时间，针对该建筑两层的办公区域（每层面积约为23,250平方米）。对原始数据进行三次数据整理策略，将其转换为研究级数据：（1）清洗原始数据，以检测并调整异常值以及填补数据空缺；（2）利用Brick架构创建建筑系统和数据点的元数据模型；（3）使用语义JSON架构描述数据集的元数据。该数据集适用于多种应用类型，包括建筑能源基准测试、负荷形状分析、能源预测、占用预测与分析，以及暖通空调控制，以增进对建筑运营的理解与效率，从而降低能源消耗、能源成本及碳排放。