Department of Electrical, Computer, and Systems Engineering, Case School of Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America.
SDLE Research Center, Case School of Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America.
PLoS One. 2020 Dec 1;15(12):e0240461. doi: 10.1371/journal.pone.0240461. eCollection 2020.
Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for "virtual" energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings' time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building's daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.
商业建筑在美国的总用电量中占三分之一，其中相当一部分能源被浪费掉了。因此，需要进行“虚拟”能源审计，以使用非侵入性和自动化的方法来识别能源效率低下及其相关的节能机会，从而应用于大量的建筑物。在这里，我们使用一种系统的方法和一个完全自动化的建筑能源分析（BEA）管道，将虚拟能源审计应用于大量建筑物的时间序列智能电表数据，该管道统一、清理、存储和分析非关系型数据仓库中的建筑能源数据集，以实现高效的洞察和结果。这个 BEA 管道是基于一个高性能计算集群的自定义计算作业调度程序，以实现 Slurm 作业的并行处理。在分析管道中，我们引入了一个数据质量工具，通过修复常见错误来提高数据质量，同时使用层次聚类检测建筑物日常运行中的异常。我们分析了 816 座建筑物的暖通空调调度情况，作为一项横断面研究的一部分，使用了这个分析管道。通过我们的方法，这 816 座建筑物的样本数据质量得到了提高，并且在 34 分钟内得到了有效分析，这比顺序处理快 85 倍。这些建筑物的暖通空调运行时间的分析结果表明，在 10 种建筑用途类型中，食品销售建筑的每日暖通空调冷却运行时间为 17.75 小时，是暖通空调节能的不错目标。总体而言，这个分析管道使我们能够从大量建筑能源时间序列数据集的基于人群的研究中识别出具有稳健结果的统计显著结果。这些类型的 BEA 研究可以探索影响建筑能源效率的众多因素和虚拟建筑能源审计。这种方法使大规模的数据驱动建筑能源分析成为可能。
