Build a Cloud based software to Optimize energy efficiency of HVAC systems - QC-159
Preferred Disciplines: Computer science, Artificial Intelligence, Software development (Master, PhD, Post-Doc)
Project length: 2-3 years (Approx. 40 units)
Approx. start date: January 2019
Location: Montreal, QC
No. of Positions: To be determined
The partner wants to put forward Montreal expertise in artificial intelligence and to have a positive environmental impact on our planet and climate change. Indeed, being based in Montreal, the company benefits from the talent and the best artificial intelligence ecosystem in the world to always be on the cutting edge of this innovation. This opportunity led to develop cloud software that optimizes the energy efficiency of buildings and, thus, really improve their environmental footprint. This creativity allows our team to stand out and present solutions that deliver unprecedented results in the energy efficiency of buildings.
Summary of Project:
Our company has been working towards developing a solution to address the longstanding issue of Miscommunication between IT personnel / infrastructure and building connected objects management.
This project aims to enhance the current techniques to develop a standardized cloud software that optimizes the energy efficiency of HVAC with infrastructure already in place in buildings. The project aims to collect and analyze a big number of data from different buildings systems and sensors to allow a real-time and predictive recommissioning to ensure comfort and the best possible energy efficiency.
We are looking to take into account all internal and external factors that have an impact on energy consumption and reduce demand peaks.
Main objective: Optimize building energy effeciency by distance with a cloud-based AI software.
- IT (Software) : Building Connection standardisation (or different/ continent), infrastructures / sensors compatibility, AI software Infrastructure (architecture), cybersecurity (firewall), collect data from buildings to our servers, sending AI decision in BACNET, REST API
- AI: Numerical Data from Commercial Building Management System, Presence detection, Data filtration, optimisation, real time/predictive decisions, Deep Learning, Neural Network, AI Supplychain (Barebones vs supplier)
- Building management: Developping core algorythm for building specific model, heat transmission, energy efficiency key parameters, BAS/BMS, building energy management, energy peak demand, ASHRAE norms, Type of HVAC System
- Comfort : Calculation of impact a good temparature between comfort standard [20-26 °C] have on productivity
- To be determined
Expertise and Skills Needed:
- Knowledge of Python, Java, C/C++, C-sharp
- Machine learning, Mathematical Modeling
- Semi supervised learning and deep reinforcement learning
- SQL Database, MSSQL Database, NoSql
- Knowledge/Previous Studies in cloud computing
- Development and testing under the following environments: Windows, Linux
- Data Processing, Data Flows
- Developing complex software systems with production quality, performance and reliability, availability, scalability
- Strong knowledge in BMS/BAS systems
- Understanding of BACNET Protocol, HTTP Protocole
For more info or to apply to this applied research position, please
- Check your eligibility and find more information about open projects.
- Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage by applying through the webform or directly to Gabriel Garcia-Curiel (ggarciacuriel(at)mitacs.ca).