A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Gaussian process modeling for measurement and verification of building energy savings
Highlights ► We present a Gaussian process modeling framework for measurement and verification. ► GP models can reliably determine energy savings and uncertainty levels. ► GP models can predict nonlinear and multi-resolution trends of energy behavior. ► Case studies demonstrated the applicability of the method for M&V practices.
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.
Gaussian process modeling for measurement and verification of building energy savings
Highlights ► We present a Gaussian process modeling framework for measurement and verification. ► GP models can reliably determine energy savings and uncertainty levels. ► GP models can predict nonlinear and multi-resolution trends of energy behavior. ► Case studies demonstrated the applicability of the method for M&V practices.
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.
Gaussian process modeling for measurement and verification of building energy savings
Heo, Yeonsook (author) / Zavala, Victor M. (author)
Energy and Buildings ; 53 ; 7-18
2012-06-30
12 pages
Article (Journal)
Electronic Resource
English
Gaussian process modeling for measurement and verification of building energy savings
| Online Contents | 2012