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Title: A regression-based approach to estimating retrofit savings using the Building Performance Database

Authors:
;
Publication Date:
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1396977
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 179; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-10-17 14:18:47; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Walter, Travis, and Sohn, Michael D. A regression-based approach to estimating retrofit savings using the Building Performance Database. United Kingdom: N. p., 2016. Web. doi:10.1016/j.apenergy.2016.07.087.
Walter, Travis, & Sohn, Michael D. A regression-based approach to estimating retrofit savings using the Building Performance Database. United Kingdom. doi:10.1016/j.apenergy.2016.07.087.
Walter, Travis, and Sohn, Michael D. 2016. "A regression-based approach to estimating retrofit savings using the Building Performance Database". United Kingdom. doi:10.1016/j.apenergy.2016.07.087.
@article{osti_1396977,
title = {A regression-based approach to estimating retrofit savings using the Building Performance Database},
author = {Walter, Travis and Sohn, Michael D.},
abstractNote = {},
doi = {10.1016/j.apenergy.2016.07.087},
journal = {Applied Energy},
number = C,
volume = 179,
place = {United Kingdom},
year = 2016,
month =
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.apenergy.2016.07.087

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  • Retrofitting building systems is known to provide cost-effective energy savings. This article addresses how the Building Performance Database is used to help identify potential savings. Currently, prioritizing retrofits and computing their expected energy savings and cost/benefits can be a complicated, costly, and an uncertain effort. Prioritizing retrofits for a portfolio of buildings can be even more difficult if the owner must determine different investment strategies for each of the buildings. Meanwhile, we are seeing greater availability of data on building energy use, characteristics, and equipment. These data provide opportunities for the development of algorithms that link building characteristics and retrofitsmore » empirically. In this paper we explore the potential of using such data for predicting the expected energy savings from equipment retrofits for a large number of buildings. We show that building data with statistical algorithms can provide savings estimates when detailed energy audits and physics-based simulations are not cost- or time-feasible. We develop a multivariate linear regression model with numerical predictors (e.g., operating hours, occupant density) and categorical indicator variables (e.g., climate zone, heating system type) to predict energy use intensity. The model quantifies the contribution of building characteristics and systems to energy use, and we use it to infer the expected savings when modifying particular equipment. We verify the model using residual analysis and cross-validation. We demonstrate the retrofit analysis by providing a probabilistic estimate of energy savings for several hypothetical building retrofits. We discuss the ways understanding the risk associated with retrofit investments can inform decision making. The contributions of this work are the development of a statistical model for estimating energy savings, its application to a large empirical building dataset, and a discussion of its use in informing building retrofit decisions.« less
  • This paper describes a procedure for estimating weather-adjusted retrofit savings in commercial buildings using ambient-temperature regression models. The selection of ambient temperature as the sole independent regression variable is discussed. An approximate method for determining the uncertainty of savings and a method for identifying the data time scale which minimizes the uncertainty of savings ar developed. The appropriate users of both linear and change-point models for estimating savings based on expected heating and cooling relationships for common HVAC systems are described. A case study example illustrates the procedure.
  • The objective of this paper is to discuss the various sources of uncertainty inherent in the estimation of actual measured energy savings from baseline regression models, and to present pertinent statistical concepts and formulae to determine this uncertainty. Regression models of energy use in commercial buildings are not of the standard type addressed in textbooks because of the changepoint behavior of the models and the effect of patterned and non-constant variance residuals (largely as a result of changes in operating modes of the building and the HVAC system). This paper also addresses such issues as how model prediction is impactedmore » by both improper model residuals and models identified from data periods which do not encompass the entire range of variation of both climatic conditions and the different building operating modes.« less