Automated pipeline framework for processing of large-scale building energy time series data.
Automated pipeline framework for processing of large-scale building energy time series data.
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DOI：
10.1371/journal.pone.0240461
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Abramson AR
中科院分区：
文献类型：
--
作者：
Khalilnejad A;Karimi AM;Kamath S;Haddadian R;French RH;Abramson AR
商业建筑占美国总电力消耗的三分之一，其中大量能源被浪费。因此，有必要进行“虚拟”能源审计，以确定能源效率低下及其相关的节省机会，使用的方法可以是非侵入性的和自动化的，适用于大量的建筑物。在这里，我们展示了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，使用系统化的方法和全自动的建筑能源分析（Building Energy Analytics，简称EAI）管道，在非关系数据仓库中统一，清理，存储和分析建筑能源数据集，以获得有效的见解和结果。该Slurm流水线基于用于高性能计算集群的自定义计算作业调度器，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鉴定工具，通过修复常见错误来提高数据质量，同时还使用分层聚类检测建筑物日常运营中的异常情况。我们使用此分析管道分析了816栋建筑物的HVAC调度，作为横截面研究的一部分。通过我们的方法，这个816栋建筑物的样本在数据质量上得到了提高，并在34分钟内进行了有效的分析，这比顺序处理所需的时间快了85倍。这些建筑物的HVAC运行时间的分析结果表明，在10种建筑使用类型中，每天HVAC冷却运行17.75小时的食品销售建筑是HVAC节省的理想目标。总的来说，这种分析管道能够从大量建筑物能源时间序列数据集的基于人群的研究中识别出具有统计学意义的结果，并具有稳健的结果。这些类型的可持续发展研究可以探索影响建筑能源效率和虚拟建筑能源审计的众多因素。这种方法可以实现新一代大规模数据驱动的建筑物能源分析。
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.
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影响因子：
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