Abstract
Increasing building efficiency is a key topic in territorial policies at different scales, for which new pathways and actions are progressively introduced. However, the evaluation of building consumptions according to energy features and urban and socio-economic variables is crucial to better assess building efficiency measures. This study presents a place-based statistical model for the evaluation of energy demand at the building scale, starting from disaggregating consumption values at the block level. The case study is the central district of Toronto (Ontario, Canada), part of the 2030 Toronto Platform. The existing interactive tool shows energy data only at the block scale, limiting specific evaluations and benchmarking. Therefore, the analysis presents a set of statistical models for assessing residential building consumption by archetypes. The aim of this study is to extend the application and visualisation of the energy demand of the whole city by GIS software. The statistical models underline more reliable results for electricity use, distinguished by appliances and space cooling. Low-rise apartments are the most challenging category to be assessed for appliance use. The variability of natural gas consumption does not allow to build only one model and values for apartment buildings are more variable for different construction ages.
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Data Availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Highlights
• The evaluation of building energy consumptions according to urban features is crucial.
• A place-based statistical model for the evaluation of energy demand of building is presented.
• A statistical model for assessing residential building demand is presented and visualised through GIS.
• Results distinguish demand by archetypal dwelling types for the main energy uses.
• The photovoltaic potential for rooftops and the electricity demand coverage of the city are assessed.
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Vecchi, F., Berardi, U. & Mutani, G. Data-driven urban building energy models for the platform of Toronto. Energy Efficiency 16, 26 (2023). https://doi.org/10.1007/s12053-023-10106-8
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DOI: https://doi.org/10.1007/s12053-023-10106-8