Model identification and prediction uncertainty of linear building energy use models with autocorrelated residuals

David K. Ruch, J. Kelly Kissock, T. Agami Reddy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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 methods to deal with autocorrelation. A hybrid of ordinary least squares (OLS) and autoregressive (AR) models is developed to accurately predict energy use and give realistic uncertainty estimates. 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 an uncertainty estimate for energy use far more accurate than the OLS estimate.

Original languageEnglish (US)
Title of host publicationSolar Engineering
PublisherPubl by ASME
Pages465-473
Number of pages9
ISBN (Print)0791809536
StatePublished - Jan 1 1993
Externally publishedYes
EventASME International Solar Energy Conference - Washington, DC, USA
Duration: Apr 4 1993Apr 9 1993

Publication series

NameSolar Engineering

Other

OtherASME International Solar Energy Conference
CityWashington, DC, USA
Period4/4/934/9/93

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Model identification and prediction uncertainty of linear building energy use models with autocorrelated residuals'. Together they form a unique fingerprint.

  • Cite this

    Ruch, D. K., Kissock, J. K., & Reddy, T. A. (1993). Model identification and prediction uncertainty of linear building energy use models with autocorrelated residuals. In Solar Engineering (pp. 465-473). (Solar Engineering). Publ by ASME.