@article{heo2013cost,
  title={Cost-effective measurement and verification method for determining energy savings under uncertainty},
  author={Heo, Yeonsook and Graziano, Diane J and Zavala, Victor M and Dickinson, Peter and Kamrath, Mark and Kirshenbaum, Marvin},
  journal={ASHRAE Transactions},
  volume={119},
  pages={EE1},
  year={2013},
  publisher={American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc.}
,

abstract = { In this paper, for measurement and verification (M&V) of energy savings in buildings, the authors propose an approach based on Gaussian Process (GP) modeling that can represent nonlinear energy behavior, multivariable interactions and time correlations while quantifying uncertainty associated with predictions. They applied the GP modeling to determine the energy savings from BuildingIQ's energy management system deployed at the Advanced Photon Source Office building at Argonne. The case study demonstrates the potential strengths of GP models for M&V and explores the importance of dataset characteristics and explanatory variables for the reliability of analysis results. The case study illustrates the capability of GP modeling to predict hourly dynamic behavior, exploiting the possibility to reduce uncertainty in energy-use predictions using measured data with finer time resolutions. The proposed M&V approach is amendable to automation in energy management systems and continuous monitoring of energy performance.}
}

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