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Digital Twins and Energy Systems for Decarbonizing Low-Carbon Social Housing in Québec

Concordia University Building, Civil, and Environmental Engineering
✓ Fully Funded 🎓 Environmental Engineering digital twins renewable energy smart grids energy modeling low-carbon housing retrofit strategies electrification urban energy systems

Explore the development of digital twin and energy simulation models to decarbonize low-income social housing in Québec. Develop and validate retrofit and electrification strategies that integrate renewable energy and smart grid technologies. Contribute to scalable decision-support tools empowering community and city stakeholders.

AI-generated overview

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Why This Research Matters

This research supports equitable access to energy transitions by focusing on social housing often excluded from retrofit markets. It advances decarbonization through innovative digital twin models and sustainable urban energy solutions, crucial for reducing carbon footprints and supporting climate goals in cities.

Next generation cities Urban energy systems renewable energies

Project Description

Project Overview

This project advances decarbonization and electrification in social, affordable, and low-income housing across Québec using innovative digital twin and urban energy simulation tools. It involves four living labs collaborating with housing cooperatives and city partners to create scalable retrofit solutions focused on reducing carbon emissions.

What You Will Do

  • Develop digital twin models for low-income housing and urban districts
  • Build and apply building energy simulations to evaluate retrofit performance
  • Design and test retrofit strategies including heat pumps, thermal storage, PV, and battery integration
  • Model electrification impacts on grids and demand response
  • Contribute to integration of 3D city models and urban data on the Tools4Cities platform
  • Simulate urban sustainability indicators like thermal comfort and heat island effect
  • Integrate real-time monitoring data for model validation and calibration
  • Develop decision-support tools for housing authorities and city stakeholders
  • Analyze cost, emissions, and policy constraints of retrofit scenarios
  • Support ongoing monitoring and commissioning of retrofits

Expected Outcomes

Deliver validated digital twin and energy models that inform cost-effective, low-carbon retrofit strategies in social housing. Provide tools that facilitate decision-making for municipalities and community partners and demonstrate replicable approaches for sustainable urban energy transitions.

Why This Matters

This research addresses a critical gap by focusing on low-income housing sectors often excluded from market-driven retrofits. By combining technical innovation with community engagement, it facilitates equitable access to clean energy transitions and contributes to climate goals by reducing carbon footprints in urban residential environments.

Entry Requirements

Master's degree in Building Engineering, Mechanical Engineering, Energy Systems, Civil Engineering, or related field. Strong background in building physics and energy systems modeling. Experience with simulation tools (EnergyPlus, TRNSYS, Modelica, or similar). Programming skills in Python, MATLAB, or similar. Familiarity with digital twins, 3D city modeling, or GIS tools is a plus. Knowledge of renewable energy systems and grid interaction. Ability to work in interdisciplinary teams and communicate with non-technical stakeholders.

How to Apply

Send a single PDF file to volt-age.recruitment@concordia.ca including a letter of intent aligned with the professor's research, CV, unofficial transcripts, names and emails of 3 referees, publication links if any, and other supporting documents. Email subject: Digital twins_Your name. Contact Alisa Makusheva at alisa.makusheva@concordia.ca for questions. Applications considered on a rolling basis.

Eligibility

UK/Home
EU
International

Supervisor Profile

PU
Prof. Ursula Eicker
Concordia University, Building, Civil, and Environmental Engineering
7000 Citations
45 h-index
Google Scholar

Prof. Ursula Eicker is a leading researcher at Concordia University specializing in urban energy systems and renewable energies for next-generation cities. Her research focuses on integrating solar technologies, building energy efficiency, and modeling smart urban energy grids. She has a strong publication record and is recognized internationally for work on solar-powered building technologies and sustainable urban energy solutions.

Key Publications

2006 538 citations
Solar technologies for buildings
2013 381 citations
Controlled natural ventilation for energy efficient buildings
2021 354 citations
On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach
2011 314 citations
Photovoltaic–thermal collectors for night radiative cooling of buildings
2009 277 citations
Design and performance of solar powered absorption cooling systems in office buildings

Research Contributions

Research on solar technologies for buildings advancing sustainable energy solutions in urban environments.
Supports development of energy-efficient building designs reducing environmental footprint.
Development of controlled natural ventilation methods to improve building energy efficiency.
Enables reduction in energy consumption for indoor climate control.
Application of machine learning, specifically deep LSTM-CNN, to short-term load forecasting in energy systems.
Improves accuracy of energy load predictions facilitating better grid management.
Innovative integration of photovoltaic–thermal systems for night radiative cooling of buildings.
Enhances cooling efficiency using renewable energy sources.

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