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Development of a bottom-up white-box building stock energy model for single-family dwellings

Adam Neale, Michaël Kummert et Michel Bernier

Article de revue (2022)

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Abstract

A new bottom-up white-box building stock energy modelling approach is presented in this paper. The model is a physics simulation-based stock model that can be used to compare the base case building stock with technological variations for comparative assessments. The model accuracy is compared to known stock data for a variety of categories, including end-use, energy source and building type. The model predicts the energy use of a building stock in a Canadian region with good agreement across all categories, with the total energy consumption of the model within 1.5% of the real stock energy use. Our analysis shows that modelling a sufficiently large sample of stock is required to reduce the expected deviation for lesser-represented portions of the stock. A case study illustrates how the model can be applied to compare different heating system distributions, in order to assess their impact on greenhouse gas emissions and peak electricity demand.

Mots clés

Residential, building stock energy model, bottom-up, white-box, greenhouse gas emissions

Sujet(s): 2100 Génie mécanique > 2100 Génie mécanique
2100 Génie mécanique > 2107 Modélisation, simulation et méthodes des éléments finis
Département: Département de génie mécanique
Organismes subventionnaires: Institut de Valorisation des Données
Numéro de subvention: PRF-2017-12
URL de PolyPublie: https://publications.polymtl.ca/10473/
Titre de la revue: Journal of Building Performance Simulation (vol. 15, no 6)
Maison d'édition: Taylor & Francis
DOI: 10.1080/19401493.2022.2082531
URL officielle: https://doi.org/10.1080/19401493.2022.2082531
Date du dépôt: 09 sept. 2022 14:55
Dernière modification: 28 sept. 2024 20:54
Citer en APA 7: Neale, A., Kummert, M., & Bernier, M. (2022). Development of a bottom-up white-box building stock energy model for single-family dwellings. Journal of Building Performance Simulation, 15(6), 735-756. https://doi.org/10.1080/19401493.2022.2082531

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