Top

Energy Efficiency

Published in:

01-04-2023 | Original Article

Data-driven urban building energy models for the platform of Toronto

Authors: Francesca Vecchi, Umberto Berardi, Guglielmina Mutani

Published in: Energy Efficiency | Issue 4/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article Do housing rental and sales markets incentivise energy-efficient retrofitting of western Germany’s post-war apartments? Challenges for property owners, tenants, and policymakers

next article Correction to: A systemic framework of energy efficiency in schools: experiences from six European countries

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Aguilar, C., White, D. J., & Ryan, D. L. (2005). Domestic water heating and water heater energy consumption in Canada. Canadian Building Energy End-Use Data and Analysis Centre - CBEEDAC.

Alsaadani, S., Roque, M., Trinh, K., Fung, A., & Straka, V. (2016). An overview of research projects investigating energy consumption in multi-unit residential buildings in Toronto. The Asian Conference on Sustainability, Energy and the Environment 2016: OfficialConference Proceedings (pp. 419–441). Kobe.

Chaturvedi, V., Eom, J., Clarke, L., & Shukla, P. (2014). Long term building energy demand for India: Disaggregating end use energy services in an integrated assessment modeling framework. Energy Policy, 64(1), 226–242. https://doi.org/10.1016/j.enpol.2012.11.021CrossRef

City of Toronto, Planning & Development, Zoning By-law 569–2013. https://www.toronto.ca/city-government/planning-development/zoning-by-law-preliminary-zoning-reviews/zoning-by-law-569-2013-2/, accessed on Nov 24^th, 2022.

City of Wien, Energy Map. https://www.wien.gv.at/ english/environment/energy/maps.html, accessed on Nov 24^th, 2022.

City Planning, 3D Massing. https://open.toronto.ca/dataset/3d-massing/, accessed on Nov 24^th, 2022.

Canadian Commission on Building and Fire Codes, National Energy Code of Canada for Buildings 2017. https://nrc.canada.ca/en/certifications-evaluations-standards/codes-canada/codes-canada-publications/national-energy-code-canada-buildings-2017, accessed on Nov 24^th, 2022.

CUI (2016). TOcore Neighbourhood Population Profiles. https://www.toronto.ca/city-government/data-research-maps/neighbourhoods-communities/neighbourhood-profiles/, accessed on Nov 24^th, 2022.

2030 District (2020). Toronto 2030 Platform. https://www.toronto2030platform.ca/, accessed on Nov 24^th, 2022.

Energy Efficiency at NRCan, Comprehensive Energy Use Database 2020. https://oee.nrcan.gc.ca/corporate/statistics/ neud/dpa/home.cfm, accessed on May 28^th, 2022.

Environment and Climate Change Canada - ECCC (2021). Temperature change. https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/temperature-change, accessed on Nov 24^th, 2022.

Environment and Climate Change Canada - ECCC (2022). 2030 emissions reduction plan. https://www.canada.ca/en/environment-climate-change/news/2022/03/2030-emissions-reduction-plan--canadas-next-steps-for-clean-air-and-a-strong-economy.html, accessed on Nov 24^th, 2022.

Fan, J., Zhang, Y., & Wang, B. (2017). The impact of urbanization on residential energy consumption in China: An aggregated and disaggregated analysis. Renewable and Sustainable Energy Reviews, 75(8), 220–233. https://doi.org/10.1016/j.rser.2016.10.066CrossRef

Farahbakhsh, H., Ugursal, V., & Fung, A. S. (1998). A residential end-use energy consumption model for Canada. International Journal of Energy Research, 22(13), 1133–1143. https://doi.org/10.1002/(SICI)1099-114X(19981025)22:13%3c1133::AID-ER434%3e3.0.CO;2-ECrossRef

Ferrando, M., Causone, F., Hong, T., Chen, Y. (2020). Urban building energy modelling (UBEM) tools: A state-of-the art review of bottom-up physic-based approaches. Sustainable Cities and Society, vol. 62, n. 32. https://doi.org/10.48550/arXiv.2103.01761

Fremouw, M., Bagaini, A., & DePascali, P. (2020). Energy potential mapping: Open data in support of urban transition planning. Energies, 13(5), 1264. https://doi.org/10.3390/en13051264CrossRef

Gonzales, P., Landajo, M., & Presno, M. (2014). Multilevel LMDI decomposition of changes in aggregate energy consumption A cross country analysis in the EU-27. Energy Policy, 68(5), 576–584. https://doi.org/10.1016/j.enpol.2013.12.065CrossRef

Government of Canada (2016). Pan-Canadian framework on clean growth and climate change. https://www.canada.ca/en/services/ environment/weather/climatechange/pan-canadian-framework.html, accessed on Nov 24^th, 2022.

Government of Canada, Energy efficient product information - Water heaters. https://www.nrcan.gc.ca/energy-efficiency/products/product-information/water-heaters/13735, accessed on Nov 24^th, 2022.

Hong, T., Chen, Y., Luo, X., Luo, N., Hoon Lee, S. (2020). Ten questions on urban building energy modelling. Urban Integrated System, vol. 168, n. 5. https://doi.org/10.1016/j.buildenv.2019.106508

Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A., & Djurovic-Petrovic, M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment, 45(7), 1683–1697. https://doi.org/10.1016/j.buildenv.2010.01.021CrossRef

Lorimer, S. (2012). A housing stock model of non-heating end-use energy in England verified by aggregated energy use data. Energy Policy, 50, 419–427. https://doi.org/10.1016/j.enpol.2012.07.037CrossRef

Città Metropolitana di Torino, Portale Solare. http://www.cittametropolitana.torino.it/cms/ambiente/risorse-energetiche/osservatorio-energia/portale-solare, accessed on Nov 24^th, 2022.

Mutani, G., & Todeschi, V. (2020). Building energy modelling at neighbourhood scale. Energy Efficiency, 13(2), 1353–1386. https://doi.org/10.1007/s12053-020-09882-4CrossRef

Mutani, G., & Todeschi, V. (2021). GIS-based urban energy modelling and energy efficiency scenarios using the energy performance certificate database. Energy Efficiency, 14(47), 1–28. https://doi.org/10.1007/s12053-021-09962-zCrossRef

Mutani, G., Fontanive, M., & Arboit, E. (2018). Energy-use modelling for residential buildings in the metropolitan area of Gran Mendoza (AR). Italian Journal of Engineering Science, 61(2), 74–82. https://doi.org/10.18280/ti-ijes.620204CrossRef

Mutani, G. and Todeschi, V. 2019 An urban energy atlas and engineering model for resilient cities. International Journal of Heat and Technology 32 (4) 936–947. https://doi.org/10.18280/ijht.370402

Mutani, G., Fabiano, E., Garcia, D.A., Mancini, F. (2021). Spatial energy modelling for the metropolitan city of Rome. 2021 IEEE 4^th IEEE CANDO Conference, Budapest, pp. 43–48. https://doi.org/10.1109/CANDO-EPE54223.2021.9667932

NRCan, 2017 Survey of Household Energy Use. https://www150.statcan.gc.ca/n1/daily-quotidien/171201/dq171201f-cansim-eng.htm, accessed on Nov 24^th, 2022.

NYU, NYC Energy & Water Performance Map. https://energy.cusp.nyu.edu/#/, accessed on Nov 24^th, 2022.

OECD (2020). Managing environmental and energy transitions: A place-based approach. https://www.oecd-ilibrary.org/sites/f3f8e305en/index.html?itemId=/content/component/f3f8e305-en, accessed on Nov 24^th, 2022.

Pereira, I., & Sad de Assis, E. (2013). Urban energy consumption mapping for energy management. Energy Policy, 59(8), 257–269. https://doi.org/10.3390/en13051264CrossRef

Reinhart, C., & Cerezo Davila, C. (2016). Urban building energy modeling – A review of a nascent field. Building and Environment, 97(15), 196–202. https://doi.org/10.1016/j.buildenv.2015.12.001CrossRef

Roth, J., Martin, A., Miller, C., & Jain, R. (2020). SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods. Applied Energy, 280(12), 115981.CrossRef

Rottondi, C., Derboni, M., Piga, D., & Rizzoli, A. E. (2019). An optimisation-based energy disaggregation algorithm for low frequency smart meter data. Energy Informatics, 13(2), 1–11. https://doi.org/10.1186/s42162-019-0089-8CrossRef

Sari, R., & Soytas, U. (2004). Disaggregate energy consumption, employment and income in Turkey. Energy Economics, 26(3), 335–344. https://doi.org/10.1016/j.eneco.2004.04.014CrossRef

Social Development, Finance & Administration, Neighbourhood Profiles. https://open.toronto.ca/dataset/neighbourhood-profiles/, accessed on Nov 24^th, 2022.

Steingrube, A., Bao, K., Wieland, S., Lalama, A., Kabiro, P., Coors, V., & Schröter, B. (2021). A method for optimizing and spatially distributing heating systems by coupling an urban energy simulation platform and a energy system model. Resources, 10(5), 52. https://doi.org/10.3390/resources10050052CrossRef

Swan, L., & Ugursal, V. (2009). Modelling of end-use energy consumption in the residential sector: A review of modelling techniques. Renewable and Sustainable Energy Reviews, 13(8), 1819–1835. https://doi.org/10.1016/j.rser.2008.09.033CrossRef

Todeschi, V., Boghetti, R., Kampf, J., & Mutani, G. (2021). Evaluation of urban-scale building energy-use models and tools - Application for the city of Fribourg. Switzerland. Sustainability, 13(4), 1595–1617. https://doi.org/10.3390/su13041595CrossRef

Torabi, S. M., Delmastro, C., Corgnati, S. P., & Lombardi, P. (2017). Urban energy planning procedure for sustainable development in the built environment: A review of available spatial approaches. Journal of Cleaner Production, 165(1), 811–827. https://doi.org/10.1016/j.jclepro.2017.07.142CrossRef

Torabi, S.M., Mutani, G. and Lombardi, P.L. (2016). GIS-based energy consumption model at the urban scale for the building stock. 9^th IEECB&SC’16, Frankfurt, Germany, pp. 1–8. https://doi.org/10.2790/290244

Toronto 2030 District (2021). Building energy supply decarbonization – Pathways project progress update. https://www.2030districts.org/toronto/about, accessed on Nov 24^th, 2022.

Touchie, M. F., Binkley, C., & Pressnail, K. D. (2013). Correlating energy consumption with multi-unit residential building characteristics in the city of Toronto. Energy and Buildings, 66(11), 648–656. https://doi.org/10.1016/j.enbuild.2013.07.068CrossRef

UCLA, California Centre for Sustainable Communities Energy Atlas. https://energyatlas.ucla.edu/, accessed on Nov 24^th, 2022.

United Way Toronto (2011). Vertical poverty. http://www.unitedwaytoronto.com/verticalpoverty/, accessed on Nov 24^th, 2022.

Zhuravchak, R., Pedrero, R., Crespo del Granado, P., Nord, N., & Brattebo, H. (2021). Top-down spatially-explicit probabilistic estimation of building energy performance at a scale. Energy and Buildings, 238(5), 110786. https://doi.org/10.1016/j.enbuild.2021.110786CrossRef

Title: Data-driven urban building energy models for the platform of Toronto
Authors: Francesca Vecchi
Umberto Berardi
Guglielmina Mutani
Publication date: 01-04-2023
Publisher: Springer Netherlands
Published in: Energy Efficiency / Issue 4/2023
Print ISSN: 1570-646X
Electronic ISSN: 1570-6478
DOI: https://doi.org/10.1007/s12053-023-10106-8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 4/2023

How does financial inclusion promote low-carbon energy transition? The global case for natural gas

A systemic framework of energy efficiency in schools: experiences from six European countries

Purchase intention for energy-efficient equipment appliances: extending TPB with eco-labels, green trust, and environmental concern

A needs-based approach to construct an industrial energy efficiency network: a case study of South Korea

Does economic complexity increase energy intensity?

Definition and evaluation of good manufacturing practices for plastic injection molding