Measurement and Verification of Building Systems Under Uncertain Data: A Gaussian Process Modeling Approach

TitleMeasurement and Verification of Building Systems Under Uncertain Data: A Gaussian Process Modeling Approach
Publication TypeJournal Article
Year of Publication2014
AuthorsBurkhart, MC, Heo, Y, Zavala, VM
JournalEnergy and Buildings
Volume75
Pagination189-198
Date Published06/2014
Other NumbersANL/MCS-P5101-0214
KeywordsGaussian process modeling; Data uncertainty; Expectationmaximization; Measurement and verification
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.

 

URLhttp://www.sciencedirect.com/science/article/pii/S0378778814001091
DOI10.1016/j.enbuild.2014.01.048
PDFhttp://www.mcs.anl.gov/papers/P5101-0214.pdf