Gaussian process modeling for measurement and verification of building energy savings

Yeonsook Heo, Victor M. Zavala

Research output: Contribution to journalArticlepeer-review

145 Citations (Scopus)


We present a Gaussian process (GP) modeling framework to determine energy savings and uncertainty levels in measurement and verification (M&V) practices. Existing M&V guidelines provide savings calculation procedures based on linear regression techniques that are limited in their predictive and uncertainty estimation capabilities. We demonstrate that, unlike linear regression, GP models can capture complex nonlinear and multivariable interactions as well as multiresolution trends of energy behavior. In addition, because GP models are developed under a Bayesian setting, they can capture different sources of uncertainty in a more systematic way. We demonstrate that these capabilities can ultimately lead to significantly less expensive M&V practices. We illustrate the developments using simulated and real data settings.

Original languageEnglish
Pages (from-to)7-18
Number of pages12
JournalEnergy and Buildings
Publication statusPublished - 2012 Oct
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the U.S. Department of Energy , under Contract No. DE-AC02-06CH11357 .


  • Gaussian process modeling
  • Measurement and verification
  • Performance-based contracts
  • Retrofit analysis
  • Uncertainty

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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