Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach

Michael C. Burkhart, Yeonsook Heo, Victor M. Zavala

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

Uncertainty in sensor data (e.g., weather, occupancy) complicates the construction of baseline models for measurement and verification (M&V). We present a Monte Carlo expectation maximization (MCEM) framework for constructing baseline Gaussian process (GP) models under uncertain input data. We demonstrate that the GP-MCEM framework yields more robust predictions and confidence levels compared with standard GP training approaches that neglect uncertainty. We argue that the approach can also reduce data needs because it implicitly expands the data range used for training and can thus be used as a mechanism to reduce data collection and sensor installation costs in M&V processes. We analyze the numerical behavior of the framework and conclude that robust predictions can be obtained with relatively few samples.

Original languageEnglish
Pages (from-to)189-198
Number of pages10
JournalEnergy and Buildings
Volume75
DOIs
Publication statusPublished - 2014 Jun
Externally publishedYes

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Keywords

  • Data uncertainty
  • Expectation maximization
  • Gaussian process modeling
  • Measurement and verification

ASJC Scopus subject areas

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

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