Machine learning for energy savings in the utility industry - AB-051

Preferred Disciplines: Computer Science, Data Science, Machine Learning (Master's or PhD student)
Project length: 4-6 months (1 unit)
Desired start date: As soon as possible
Location: Calgary, AB
No. of Positions: 1
Preferences: The candidate must be based in Alberta
Company: Anonymous

About Company:

We are bringing the latest IoT solutions to utilities and through doing this create GHG savings. Our mission is to save the world 162M tonnes of CO2 annually. We have a team of extremely well experienced and connected people. Our team members have developed innovative products for utilities over the last 20 years. We aim to save utilities up to 20% of their opex costs through applying machine leaning software products.

Project Description:

We require a temperature prediction algorithm that can predict ‘tomorrow’s temperature’ of the feedwater onsite at utilities. Through comparing hourly feedwater temperature with ambient weather data available for the site from weather data records, this goal will help us make predictions on when the feedwater will be warmer and cooler. We aim to develop an algorithm that lets our customers produce more water when the feedwater temperature is warm, thus using less energy and saving operating costs. 

Research Objectives/Sub-Objectives:

  1. Find the best set of algorithms to predict temperature in water
  2. Test algorithms using real data in multiple locations


  1. Review and understand the business opportunity and thus the goals for the project
  2. Undertake domain knowledge training
  3. Review the available data to gain an understanding of the situation 
  4. Undertake a literature search to confirm the best available technique in collaboration with AMII
  5. Undertake machine learning algorithm development
  6. Test and iterate on the best solution
  7. Present the findings of the project to customer

Expertise and Skills Needed:

  • Machine learning
  • Data analysis
  • Python and or Go (lang) skills prefferable

For more info or to apply to this applied research position, please

  1. Check your eligibility and find more information about open projects.
  2. 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.