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.
Original language | English |
---|---|
Pages (from-to) | 7-18 |
Number of pages | 12 |
Journal | Energy and Buildings |
Volume | 53 |
DOIs | |
Publication status | Published - 2012 Oct |
Externally published | Yes |
Keywords
- 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