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Current Sustainable/Renewable Energy Reports

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13-01-2018 | End-Use Efficiency (Y Wang, Section Editor)

Big Data and Residential Energy Efficiency Evaluation

Authors: Yueming Qiu, Anand Patwardhan

Published in: Current Sustainable/Renewable Energy Reports | Issue 1/2018

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Abstract

Purpose of Review

Recent 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 Findings

Results 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.

Summary

Researchers 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.

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Title: Big Data and Residential Energy Efficiency Evaluation
Authors: Yueming Qiu
Anand Patwardhan
Publication date: 13-01-2018
Publisher: Springer International Publishing
Published in: Current Sustainable/Renewable Energy Reports / Issue 1/2018
Electronic ISSN: 2196-3010
DOI: https://doi.org/10.1007/s40518-018-0098-4

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Abstract

Purpose of Review

Recent Findings

Summary

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