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Characterizing and Structuring Urban Data for Housing Stock Energy Modeling


Characterizing and Structuring Urban Data for Housing Stock Energy Modeling

HosseiniHaghighi, SeyedehRabeeh (2021) Characterizing and Structuring Urban Data for Housing Stock Energy Modeling. Masters thesis, Concordia University.

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Setting Canada on a net-zero emission pathway by 2050 encouraged the current research to identify a workflow to focus on housing stock energy modeling by addressing the gap between the accessible urban datasets and the parameters required for building energy performance simulation. Urban building energy modeling (UBEM) supports policymaking for retrofitting buildings toward location-specific, low energy, and low carbon possibilities. The study deals with UBEM data management based on real urban datasets and characterizing archetype profiles to develop a bottom-up model capable of evaluating district energy scenarios. The different origins of datasets from multiple sources and scales required using a range of tools for data processing and analysis to synchronize with the UBEM specification and the CityGML geometry standard. The simulation platform SimStadt was used to integrate the city model with the developed archetypes in eight identified vintages for low-rise housing and estimate the district heating load.
A case study was carried out for the city Kelowna in BC /Canada. The average simulated heating energy use intensity showed almost 10% deviation from the national measured data in British Columbia. Furthermore, the model calculated the equivalent CO2 emission as well as the district's potential for on-site power generation combining photovoltaics with demand-side management. On the urban data side, the key barriers were the low compatibility between different urban datasets, lack of building identifiers shared between documentation conveying building information and deriving urban building geometry from data sources with low accuracies. Concerning the building energy input, the availability of characterized archetypes and local metered data would enhance the developed workflow and outputs toward effective energy policy and bottom-up retrofit planning.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:HosseiniHaghighi, SeyedehRabeeh
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:12 April 2021
Thesis Supervisor(s):Eicker, Ursula
Keywords:Urban datasets, Building data preparation, Energy modeling, Archetype characterization, CityGML
ID Code:988285
Deposited By: Seyedehrabeeh Hosseinihaghighi
Deposited On:29 Jun 2021 21:10
Last Modified:29 Jun 2021 21:10


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