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Comparing energy and comfort metrics for building benchmarking.

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Title: Comparing energy and comfort metrics for building benchmarking.
Authors: Estrella Guillén, Esteban1 (AUTHOR) estrellaguillen@gsd.harvard.edu, Samuelson, Holly W.1 (AUTHOR), Cedeño Laurent, Jose G.2 (AUTHOR)
Source: Energy & Buildings. Dec2019, Vol. 205, pN.PAG-N.PAG. 1p.
Subjects: Energy Star (Program), Benchmarking (Management), Regression analysis
Abstract: • Describes drawbacks of building energy & comfort benchmarking metrics/methods. • Demonstrates how different metrics lead to vastly different rankings with 29 cases. • Demonstrates value of custom regression model compared to popular regression tools. • Proposes new metrics: ''Overheating and overcooling degree Days". • New metrics help highlight cases of discomfort resulting in energy waste. Benchmarking energy use is increasingly mandated and tied to consequences such as fines for underperforming buildings. Yet, standard benchmarking methods and metrics may not adequately align with policymakers' or building owners' goals. We demonstrate how benchmarking metrics are non-interchangeable and how they can lead to substantially different building rankings. We analyze the performance of 29 case study buildings using different methods and metrics, divided into three categories: simple energy benchmarking, regression, and comfort. We find that Energy Use Intensity (EUI) serves as a poor proxy for harder-to-measure but more meaningful metrics. For example, factoring in the number of occupants ("EUI per person" rather than EUI) changes a building's ranking in our group by 24%. We demonstrate how a custom regression analysis and the "Observed-to-modeled" ratio can be useful for large-portfolio building owners, and how this differs from available benchmarking tools like Energy Star. We benchmark a subset of buildings via reported and monitored comfort factors and, importantly, propose the metrics "Overheating/cooling Degree Days". These metrics measure discomfort relative to a building's operation mode and highlight cases of energy waste. The Overheating Degree Days metric highlighted operational problems in one case study building. [ABSTRACT FROM AUTHOR]
Copyright of Energy & Buildings is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 139651240
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Label: Title
  Group: Ti
  Data: Comparing energy and comfort metrics for building benchmarking.
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  Data: <searchLink fieldCode="AR" term="%22Estrella+Guillén%2C+Esteban%22">Estrella Guillén, Esteban</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> estrellaguillen@gsd.harvard.edu</i><br /><searchLink fieldCode="AR" term="%22Samuelson%2C+Holly+W%2E%22">Samuelson, Holly W.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cedeño+Laurent%2C+Jose+G%2E%22">Cedeño Laurent, Jose G.</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energy+%26+Buildings%22">Energy & Buildings</searchLink>. Dec2019, Vol. 205, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Energy+Star+%28Program%29%22">Energy Star (Program)</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmarking+%28Management%29%22">Benchmarking (Management)</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • Describes drawbacks of building energy & comfort benchmarking metrics/methods. • Demonstrates how different metrics lead to vastly different rankings with 29 cases. • Demonstrates value of custom regression model compared to popular regression tools. • Proposes new metrics: ''Overheating and overcooling degree Days". • New metrics help highlight cases of discomfort resulting in energy waste. Benchmarking energy use is increasingly mandated and tied to consequences such as fines for underperforming buildings. Yet, standard benchmarking methods and metrics may not adequately align with policymakers' or building owners' goals. We demonstrate how benchmarking metrics are non-interchangeable and how they can lead to substantially different building rankings. We analyze the performance of 29 case study buildings using different methods and metrics, divided into three categories: simple energy benchmarking, regression, and comfort. We find that Energy Use Intensity (EUI) serves as a poor proxy for harder-to-measure but more meaningful metrics. For example, factoring in the number of occupants ("EUI per person" rather than EUI) changes a building's ranking in our group by 24%. We demonstrate how a custom regression analysis and the "Observed-to-modeled" ratio can be useful for large-portfolio building owners, and how this differs from available benchmarking tools like Energy Star. We benchmark a subset of buildings via reported and monitored comfort factors and, importantly, propose the metrics "Overheating/cooling Degree Days". These metrics measure discomfort relative to a building's operation mode and highlight cases of energy waste. The Overheating Degree Days metric highlighted operational problems in one case study building. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Energy & Buildings is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=139651240
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.enbuild.2019.109539
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      – Code: eng
        Text: English
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        StartPage: N.PAG
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        Type: general
      – SubjectFull: Benchmarking (Management)
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      – SubjectFull: Regression analysis
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      – TitleFull: Comparing energy and comfort metrics for building benchmarking.
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              Text: Dec2019
              Type: published
              Y: 2019
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