Sustainable building operations: AI-driven energy optimization and automated ESG reporting - ON-1176
Project type: ResearchDesired discipline(s): Computer science, Mathematical Sciences, Physics / Astronomy, Natural Sciences
Company: CMAI
Project Length: 6 months to 1 year
Preferred start date: 03/01/2026
Language requirement: English
Location(s): Toronto, ON, Canada
No. of positions: 2
Desired education level: Master'sPhD
Open to applicants registered at an institution outside of Canada: No
About the company:
CMAI is an advanced Artificual Intelligence (AI) technology company focused on transforming property management operations through artificial intelligence driven operation management and workflow automation. The firm not only unifies the entire end-to-end property managment workflows, but also leverages properties’ data, maintaince records and regulations to build a predictive serivce model to significantly drive property management efficiency, energy and reserouce saving, environment and social sustainbility, driving asset value growth.
Describe the project.:
The built environment is a major contributor to global climate change, with commercial and residential properties accounting for approximately 15-20% of total carbon emissions. As regulatory pressure mounts and tenant demand for "green" living spaces increases, property owners face a dual challenge: reducing operational carbon footprints while adhering to increasingly complex Environmental, Social, and Governance (ESG) reporting standards.
The main goal of this research project is to develop a Sustainable Building Operations & Energy Optimization AI Platform. Unlike traditional Building Management Systems (BMS) that rely on static, reactive set-points (e.g., turning on heat only when temperature drops), this project aims to build a Predictive Control System. By integrating real-time variables—including hyper-local weather forecasting, dynamic occupancy prediction, and equipment performance data—the system will proactively adjust HVAC, lighting, and water systems to minimize waste without compromising occupant comfort.
A critical innovation of this project is the automation of ESG Compliance. The platform will feature a dedicated module that translates raw energy data into standardized sustainability metrics, streamlining the certification process for standards such as LEED and BREEAM. Furthermore, the research will explore Tenant Behavior Analysis, utilizing gamification and data visualization to encourage voluntary energy conservation among residents.
Ultimately, this project aims to bridge the "Performance Gap" between building design and actual operation, targeting a 25% reduction in energy costs and positioning the company as a leader in the emerging GreenPropTech sector.
Required expertise/skills:
Building Science & Simulation: Knowledge with energy modeling software Understanding of thermodynamics and HVAC system dynamics.
Control Systems & AI: Experience with Model Predictive Control (MPC) or Reinforcement Learning (RL). Proficiency in Python
IoT & Protocols: Knowledge of building automation protocols (BACnet, Modbus) to interface AI models with physical hardware.
Sustainability Metrics: Familiarity with carbon accounting (GHG Protocol) and green building standards (LEED, BREEAM, Zero Carbon Building Standard).
Research Focus Areas
• Optimization: A strong interest in "Multi-objective optimization" (balancing cost vs. comfort vs. carbon).
• Human-Centric Design: Ability to analyze how tenant behavior impacts energy load.
