Journal article
Prediction Uncertainty of Linear Building Energy Use Models With Autocorrelated Residuals
Journal of solar energy engineering, v 121(1), pp 63-68
01 Feb 1999
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Prediction Uncertainty of Linear Building Energy Use Models With Autocorrelated Residuals
- Creators
- D. K Ruch - Sam Houston State UniversityJ. K Kissock - University of DaytonT. A Reddy - Drexel UniversityAgami T Reddy - [Retired Faculty]
- Publication Details
- Journal of solar energy engineering, v 121(1), pp 63-68
- Publisher
- ASME
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:000079015600010
- Scopus ID
- 2-s2.0-0033077029
- Other Identifier
- 991019184820304721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Energy & Fuels
- Engineering, Mechanical