Prediction Uncertainty of Linear Building Energy Use Models With Autocorrelated Residuals

[+] Author and Article Information
D. K. Ruch

Department of Mathematics, Sam Houston State University, Huntsville, TX

J. K. Kissock

Department of Mechanical Engineering, University of Dayton, Dayton, OH

T. A. Reddy

Department of Civil and Architectural Engineering, Drexel University, Philadelphia, PA

J. Sol. Energy Eng 121(1), 63-68 (Feb 01, 1999) (6 pages) doi:10.1115/1.2888144 History: Received June 01, 1998; Revised November 01, 1998; Online February 14, 2008


Autocorrelated residuals from regression models of building energy use present problems when attempting to estimate retrofit energy savings and the uncertainty of the savings. This paper discusses the causes of autocorrelation in energy use models and proposes a method to deal with autocorrelation. A hybrid of ordinary least squares (OLS) and autoregressive (AR) models is developed to accurately predict energy use and give reasonable uncertainty estimates. Only linear models are considered because both the data and the physical theory for many commercial buildings support this choice (Kissock, 1993). A procedure for model selection is presented and tested on data from three commercial buildings participating in the Texas LoanSTAR program. In every case examined, the hybrid OLS-AR model provided the best estimate of energy use and the most robust estimate of uncertainty.

Copyright © 1999 by The American Society of Mechanical Engineers
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