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Long-Term Techno-Economic Performance Monitoring to Promote Built Environment Decarbonisation and Digital Transformation—A Case Study

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  • Massimiliano Manfren

    (Faculty of Engineering and Physical Sciences, University of Southampton, Boldrewood Innovation Campus, Burgess Road, Southampton SO16 7QF, UK)

  • Lavinia Chiara Tagliabue

    (Department of Computer Science, University of Turin, Corso Svizzera 185, 10149 Torino, Italy)

  • Fulvio Re Cecconi

    (Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, Via G. Ponzio 31, 20133 Milano, Italy)

  • Marco Ricci

    (Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, Via G. Ponzio 31, 20133 Milano, Italy)

Abstract

Buildings’ long-term techno-economic performance monitoring is critical for benchmarking in order to reduce costs and environmental impact while providing adequate services. Reliable building stock performance data provide a fundamental knowledge foundation for evidence-based energy efficiency interventions and decarbonisation strategies. Simply put, an adequate understanding of building performance is required to reduce energy consumption, as well as associated costs and emissions. In this framework, Variable-base degree-days-based methods have been widely used for weather normalisation of energy statistics and energy monitoring for Measurement and Verification (M & V) purposes. The base temperature used to calculate degree-days is determined by building thermal characteristics, operation strategies, and occupant behaviour, and thus varies from building to building. In this paper, we develop a variable-base degrees days regression model, typically used for energy monitoring and M & V, using a “proxy” variable, the cost of energy services. The study’s goal is to assess the applicability of this type of model as a screening tool to analyse the impact of efficiency measures, as well as to understand the evolution of performance over time, and we test it on nine public schools in the Northern Italian city of Seregno. While not as accurate as M & V techniques, this regression-based approach can be a low-cost tool for tracking performance over time using cost data typically available in digital format and can work reasonably well with limited resolution, such as monthly data. The modelling methodology is simple, scalable and can be automated further, contributing to long-term techno-economic performance monitoring of building stock in the context of incremental built environment digitalization.

Suggested Citation

  • Massimiliano Manfren & Lavinia Chiara Tagliabue & Fulvio Re Cecconi & Marco Ricci, 2022. "Long-Term Techno-Economic Performance Monitoring to Promote Built Environment Decarbonisation and Digital Transformation—A Case Study," Sustainability, MDPI, vol. 14(2), pages 1-17, January.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:644-:d:719618
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    References listed on IDEAS

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    1. Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    2. Xing, Yangang & Hewitt, Neil & Griffiths, Philip, 2011. "Zero carbon buildings refurbishment--A Hierarchical pathway," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3229-3236, August.
    3. Pia Andres & Giorgia Cecchinato & Penny Mealy & Charlotte Taylor & Sam Unsworth & Anna Valero, 2020. "Jobs for a strong and sustainable recovery from Covid-19," CEP Covid-19 Analyses cepcovid-19-010, Centre for Economic Performance, LSE.
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