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Big Data and Residential Energy Efficiency Evaluation

  • End-Use Efficiency (Y Wang, Section Editor)
  • Published:
Current Sustainable/Renewable Energy Reports Aims and scope Submit manuscript

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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Funding

Funding for this research was provided by the National Science Foundation under Grant No. 1757329.

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Authors and Affiliations

  1. University of Maryland College Park, School of Public Policy. 3135 Van Munching Hall, College Park, MD, 20742, USA

    Yueming Qiu

  2. University of Maryland College Park, School of Public Policy. 3111C Van Munching Hall, College Park, MD, 20742, USA

    Anand Patwardhan

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  1. Yueming Qiu
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Correspondence to Yueming Qiu.

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Yueming Qiu and Anand Patwardhan declare no conflicts of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on End-Use Efficiency

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Qiu, Y., Patwardhan, A. Big Data and Residential Energy Efficiency Evaluation. Curr Sustainable Renewable Energy Rep 5, 67–75 (2018). https://doi.org/10.1007/s40518-018-0098-4

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