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Modelling platform for schools (MPS): The development of an automated One-By-One framework for the generation of dynamic thermal simulation models of schools

  • Yair Schwartz*
  • , Ivan Korolija
  • , Daniel Godoy-Shimizu
  • , Sung Min Hong
  • , Jie Dong
  • , Duncan Grassie
  • , Anna Mavrogianni
  • , Dejan Mumovic
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

The UK Government has recently committed to achieve net zero carbon status by year 2050. Schools are responsible for around 2% of the UK's total energy consumption, and around 15% of the UK public sector's carbon emissions. A detailed analysis of the English school building stock's performance can help policymakers improve its energy efficiency and indoor environmental quality. Building stock modelling is a technique commonly used to quantify current and future energy demand or indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. ‘Building-by-building’ stock modelling is a modelling technique whereby individual buildings within the stock are modelled and simulated, and performance results are aggregated and analysed at stock level. This paper presents the development of the Modelling Platform for Schools (MPS) – an automated generation of one-by-one thermal models of schools in England through the analysis and integration of a range of data (geometry, size, number of buildings within a school premises etc.) from multiple databases and tools (Edubase/Get Information About Schools, Property Data Survey Programme, Ordanance Survey and others). The study then presents an initial assessment and evaluation of the modelling procedure of the proposed platform. The model evaluation has shown that out of 15,245 schools for which sufficient data were available, nearly 50% can be modelled in an automated manner having a high level of confidence of similarity with the actual buildings. Visual comparison between automatically-generated models and actual buildings has shown that around 70% of the models were, indeed, geometrically accurate.

Original languageEnglish
Article number111566
JournalEnergy and Buildings
Volume254
DOIs
Publication statusPublished - 1 Jan 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Generative design
  • Schools stock
  • Stock modelling
  • Thermal simulation

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