Prediction uncertainty of linear building energy use models with autocorrelated residuals

D. K. Ruch, J. K. Kissock, T. A. Reddy

Research output: Contribution to journalArticle

13 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 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.

Original languageEnglish (US)
Pages (from-to)63-68
Number of pages6
JournalJournal of Solar Energy Engineering, Transactions of the ASME
Volume121
Issue number1
DOIs
StatePublished - Feb 1 1999
Externally publishedYes

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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