@article{Heo20127,
title = "Gaussian process modeling for measurement and verification of building energy savings ",
journal = "Energy and Buildings ",
volume = "53",
number = "",
pages = "7 - 18",
year = "2012",
note = "",
issn = "0378-7788",
doi = "http://dx.doi.org/10.1016/j.enbuild.2012.06.024",
url = "http://www.sciencedirect.com/science/article/pii/S037877881200312X",
author = "Yeonsook Heo and Victor M. Zavala",
keywords = "Gaussian process modeling",
keywords = "Measurement and verification",
keywords = "Performance-based contracts",
keywords = "Retrofit analysis",
keywords = "Uncertainty ",
abstract = "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. "
}

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