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Linking design and operation phase energy performance analysis through regression-based approaches

Linking design and operation phase energy performance analysis through regression-based approaches
Linking design and operation phase energy performance analysis through regression-based approaches

The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

Building energy performance, Data-driven methods, Multivariate regression, Open energy data, Open software
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Nastasi, Benedetto
0d19eabe-134e-4cbe-9912-ff4c095410cd
Tronchin, Lamberto
8527a327-51fb-4865-b99d-eab721dadec9
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Nastasi, Benedetto
0d19eabe-134e-4cbe-9912-ff4c095410cd
Tronchin, Lamberto
8527a327-51fb-4865-b99d-eab721dadec9

Manfren, Massimiliano, Nastasi, Benedetto and Tronchin, Lamberto (2020) Linking design and operation phase energy performance analysis through regression-based approaches. Frontiers in Energy Research, 8, [557649]. (doi:10.3389/fenrg.2020.557649).

Record type: Article

Abstract

The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

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2020_24_11_Nastasi_Manuscript - Accepted Manuscript
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e-pub ahead of print date: 24 November 2020
Additional Information: Funding Information: This work was part of the activities carried out within the project n.201594LT3F, funded by PRIN (Programmi di Ricerca Scientifica di Rilevante Interesse Nazionale) of the Italian Ministry of Education, University and Research and the project “SIPARIO - Il Suono: arte Intangibile delle Performing Arts – Ricerca su teatri italiani per l'Opera POR–FESR 2014–20” , n. PG/2018/632038, funded by the Regione Emilia Romagna under EU Commission. Publisher Copyright: © 2020 Manfren, Nastasi and Tronchin. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
Keywords: Building energy performance, Data-driven methods, Multivariate regression, Open energy data, Open software

Identifiers

Local EPrints ID: 446840
URI: http://eprints.soton.ac.uk/id/eprint/446840
PURE UUID: e4069a38-822d-4579-bcf8-5a23cfdc9b03
ORCID for Massimiliano Manfren: ORCID iD orcid.org/0000-0003-1438-970X

Catalogue record

Date deposited: 24 Feb 2021 17:30
Last modified: 18 Mar 2024 03:40

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Contributors

Author: Benedetto Nastasi
Author: Lamberto Tronchin

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