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Is disaggregation the holy grail of energy efficiency? The case of electricity

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  • Carrie Armel, K.
  • Gupta, Abhay
  • Shrimali, Gireesh
  • Albert, Adrian
Abstract
This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure – without careful consideration of the human element – will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or whole-building, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

Suggested Citation

  • Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    • Handle: RePEc:eee:enepol:v:52:y:2013:i:c:p:213-234
    DOI: 10.1016/j.enpol.2012.08.062
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    References listed on IDEAS

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    1. Mario E. Berges & Ethan Goldman & H. Scott Matthews & Lucio Soibelman, 2010. "Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring," Journal of Industrial Ecology, Yale University, vol. 14(5), pages 844-858, October.
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    3. McCalley, L. T. & Midden, Cees J. H., 2002. "Energy conservation through product-integrated feedback: The roles of goal-setting and social orientation," Journal of Economic Psychology, Elsevier, vol. 23(5), pages 589-603, October.
    4. Yen-Yi Ho & Giovanni Parmigiani & Thomas A. Louis & Leslie M. Cope, 2011. "Modeling Liquid Association," Biometrics, The International Biometric Society, vol. 67(1), pages 133-141, March.
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