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Method for Scalable and Automatised Thermal Building Performance Documentation and Screening

Author

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  • Christoffer Rasmussen

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark)

  • Peder Bacher

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark)

  • Davide Calì

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark)

  • Henrik Aalborg Nielsen

    (ENFOR A/S, Lyngsø Allé 3, 2970 Hørsholm, Denmark)

  • Henrik Madsen

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark)

Abstract

In Europe, more and more data on building energy use will be collected in the future as a result of the energy performance of buildings directive (EPBD), issued by the European Union. Moreover, both at European level and globally it became evident that the real energy performance of new buildings and the existing building stock needs to be documented better. Such documentation can, for example, be done with data-driven methods based on mathematical and statistical approaches. Even though the methods to extract energy performance characteristics of buildings are numerous, they are of varying reliability and often associated with a significant amount of human labour, making them hard to apply on a large scale. A classical approach to identify certain thermal performance parameters is the energy signature method. In this study, an automatised, nonlinear and smooth approach to the well-known energy signature is proposed, to quantify key thermal building performance parameters. The research specifically aims at describing the linear and nonlinear heat usage dependency on outdoor temperature, wind and solar irradiation. To make the model scalable, we realised it so that it only needs the daily average heat use of buildings, the outdoor temperature, the wind speed and the global solar irradiation. The results of applying the proposed method on heat consumption data from 16 different and randomly selected Danish occupied houses are analysed.

Suggested Citation

  • Christoffer Rasmussen & Peder Bacher & Davide Calì & Henrik Aalborg Nielsen & Henrik Madsen, 2020. "Method for Scalable and Automatised Thermal Building Performance Documentation and Screening," Energies, MDPI, vol. 13(15), pages 1-23, July.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3866-:d:391166
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    References listed on IDEAS

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    1. Hammarsten, Stig, 1987. "A critical appraisal of energy-signature models," Applied Energy, Elsevier, vol. 26(2), pages 97-110.
    2. Marieline Senave & Staf Roels & Stijn Verbeke & Evi Lambie & Dirk Saelens, 2019. "Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis," Energies, MDPI, vol. 12(17), pages 1-29, August.
    3. Verbai, Zoltán & Lakatos, Ákos & Kalmár, Ferenc, 2014. "Prediction of energy demand for heating of residential buildings using variable degree day," Energy, Elsevier, vol. 76(C), pages 780-787.
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    Cited by:

    1. Santu Golder & Ramadas Narayanan & Md. Rashed Hossain & Mohammad Rofiqul Islam, 2021. "Experimental and CFD Investigation on the Application for Aerogel Insulation in Buildings," Energies, MDPI, vol. 14(11), pages 1-16, June.
    2. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    3. Christoffer Rasmussen & Niels Lassen & Peder Bacher & Tor Helge Dokka & Henrik Madsen, 2023. "Data-Driven Estimation of Time-Varying Stochastic Effects on Building Heat Consumption Related to Human Interactions," Energies, MDPI, vol. 16(16), pages 1-22, August.
    4. Simon Rouchier, 2022. "Bayesian Workflow and Hidden Markov Energy-Signature Model for Measurement and Verification," Energies, MDPI, vol. 15(10), pages 1-19, May.
    5. Palmer Real, Jaume & Møller, Jan Kloppenborg & Li, Rongling & Madsen, Henrik, 2022. "A data-driven framework for characterising building archetypes: A mixed effects modelling approach," Energy, Elsevier, vol. 254(PB).

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