Big Data and Residential Energy Efficiency Evaluation
Big Data and Residential Energy Efficiency Evaluation
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DOI：
10.1007/s40518-018-0098-4
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发表时间：
2018-01
期刊：
影响因子：
--
通讯作者：
Y. Qiu;A. Patwardhan
中科院分区：
文献类型：
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作者：
Y. Qiu;A. Patwardhan
能源大数据的最新发展可能会改变 现有的能源效率评估研究，以更准确，概括， and scalable可扩展ones.这篇评论文章涵盖了现有的住宅能源 效率评估研究和住宅建筑能源研究。最近的调查结果显示，大多数现有的能源效率， 评价框架和传统的统计分析是不够的 足以确定能源效率的因果影响。在现实中， 家庭大多自行选择安装节能装置， 安装后观察到的能源消耗变化可能是由于， 至少部分地取决于通常随时间变化的某些因素， 研究人员可以利用新兴的大规模建筑能源 数据集与高频能源需求数据相结合， 计算能源效率评估框架。这种框架应 融合了包括机械在内的各个学科的知识和进步 学习，统计和计量经济学，以提供更准确， 能源效率措施的信息丰富的因果影响评估。
Purpose of ReviewRecent development of energy big data could potentially transform existing energy efficiency evaluation studies into more accurate, generalizable, and scalable ones. This review article covers existing residential energy efficiency evaluation studies and residential building energy studies.Recent FindingsResults reveal that the majority of existing energy efficiency evaluation frameworks and traditional statistical analysis are not sufficient enough to identify the causal impact of energy efficiency. In reality, households mostly self-select into energy efficiency installations and the observed changes in energy consumption after the installations may be due, at least in part, to certain factors that are generally time-variant and unobservable to the statistician.SummaryResearchers can utilize emerging large-scale building energy datasets combined with high-frequency energy demand data to develop innovative computational energy efficiency evaluation frameworks. Such frameworks should incorporate knowledge and advances from various disciplines including machine learning, statistics, and econometrics in order to provide more accurate and information-rich causal impact evaluations of energy efficiency measures.