Real use cases young people can learn on
These projects are more than technology initiatives. They serve as real-world use cases that help inspire and develop the next generation of innovators, providing valuable learning opportunities for students from elementary school through university. By connecting education, energy, data, and technology, these projects help build the skills, experience, and confidence needed to support Canada's future workforce.
Each one is set out here as a use case: what it is, and how it helps a Canadian student learn.
Why this matters for young people in Canada.
Weather Normalization Research
Telling the difference between a cold winter and a wasteful building.
If a school used more energy this winter than last, was that the building or was it the weather? Answering that properly is called weather normalization, and it matters more in Canada than almost anywhere, because a single cold snap can swamp a year of efficiency work. The usual industry approach, the Degree-Day method, only looks at temperature. Screaming Power and Ryerson University, now Toronto Metropolitan University, developed a better one: Structure Dependent Weather Normalization, which takes in temperature, humidity, solar radiation and wind, and uses regression and neural networks to model how a specific building actually responds to them. It is patented in both Canada and the United States and published in a peer-reviewed international journal.
How it helps a student learn
This is what real research looks like from end to end, and students can see every stage of it: a question, a Canadian university lab, a peer-reviewed paper, two granted patents, and finally a product that utilities use. Very few school projects can point at that whole chain. It is also directly useful, because any student comparing their school's energy use across months or years runs straight into the same problem the research solves. Normalizing for weather first is the difference between a real finding and a coincidence.
What it offers
- A patented method: CA 2,996,731 C and US 10,770,898 B2, both granted and active
- Four weather inputs rather than one: temperature, humidity, solar radiation and wind
- Machine learning and deep learning models that handle extreme weather better than Degree-Day
- Peer-reviewed and published in Energy Science & Engineering, April 2019
- Canadian research, built with Ryerson University's Faculty of Engineering and Architectural Science
- Supported by the Ontario Centre of Innovation, NSERC and NRC IRAP
What learners gain
- Understanding why raw energy comparisons mislead, and how to correct for it
- A first look at regression and neural networks applied to a real problem
- Research literacy: how a question becomes a paper, a patent and a product
- The habit of asking what else could explain a result
- A route into Canadian university research and graduate study in engineering
Background: Canadian Patent 2,996,731 C · US Patent 10,770,898 B2 · Beheshti, Sahebalam and Nidoy, Energy Science & Engineering (2019) · Screaming Power patents
Weather Normalization Research
AI Foundry
A trusted AI learning companion for energy, climate and STEM.
AI Foundry is a sovereign, curated artificial intelligence capability built for the energy sector. Instead of guessing from the open web, it draws on a trusted domain of expert-reviewed knowledge, organised through a knowledge trust framework so that answers can be traced back to sources people can rely on. For a Canadian classroom, this means an AI companion that is accurate, safe and grounded in real energy and climate knowledge, and one whose data stays in Canada rather than leaving it.
How it helps a student learn
Young people are already using AI, so they need to learn to use it well. AI Foundry gives students and teachers a safer place to ask questions, explore energy and climate topics, and understand how AI systems reach an answer. Because its knowledge is curated and reviewed, learners can see the difference between a trustworthy source and an unchecked one, which is the heart of AI literacy and increasingly what schools are being asked to teach.
What it offers
- A curated, expert-reviewed knowledge base focused on energy, climate and sustainability
- A knowledge trust framework so students can see where an answer comes from
- Data that stays in Canada, which matters for school boards and privacy officers
- A safer starting point for classroom projects than the open web
- The build platform student teams use to create AI solutions at the hackathon
- Support for teachers who want reliable material to plan lessons and activities
What learners gain
- AI literacy: knowing how to question, check and use AI responsibly
- Digital skills that transfer to further study and green-economy work
- Confidence to build and test a real AI project from an idea
- A habit of tracing claims back to trustworthy sources
Background: UNESCO guidance on AI in education · Canada's Clean Electricity Strategy
AI Foundry
Customer Information System (CIS)
See how a Canadian utility really works, end to end.
A Customer Information System is the software a utility uses to run the business side of supplying energy: accounts, meter readings, rate classes, billing, payments and the support that goes with them. Canadian students rarely see any of this, even though every household deals with the output of it every month. Working inside a real system shows how a tariff, a meter reading and a bill are connected.
How it helps a student learn
A bill stops being a mystery once you have seen the system that produces it. Students follow a reading from the meter through a rate structure to a charge on an account, which makes time-of-use pricing, demand charges and delivery costs concrete rather than abstract. It is also a direct look at the kind of work Canadian utilities, regulators and energy retailers actually hire people to do.
What it offers
- A real view of the meter-to-cash journey from usage to bill
- Hands-on understanding of accounts, meters and customer data
- A clear example of how billing and tariffs are worked out
- Insight into the day-to-day operations behind a reliable energy service
- A grounding in the data skills utilities actually use
- Exposure to a range of energy-sector roles and career pathways
What learners gain
- An understanding of how energy is metered, priced and billed in Canada
- Data literacy built on records that behave like the real thing
- Insight into time-of-use pricing and why when you use power matters
- A realistic view of careers at utilities, regulators and energy retailers
- Confidence reading and questioning a real energy bill
Background: Ontario Energy Board, how your bill works · Statistics Canada, Labour Force Survey
Customer Information System (CIS)
Energy Management System (EMS)
Give a school its own energy data to learn from.
An Energy Management System collects a building's real energy use and turns it into something a person can act on. Pointed at a school, it shows when the building actually draws power and gas, where the waste is, and what changes would cut it. In most of Canada that story is dominated by heating and by cold-weather peaks, which makes it a very different problem from a warm climate and a far more interesting one to teach.
How it helps a student learn
Students stop reading about energy efficiency and start measuring it in the building they sit in every day. They can see the morning warm-up, the holiday baseline that never drops to zero, the effect of a cold snap. Then they can propose a change and check whether the data agrees with them, which is the whole scientific method wrapped around something they care about.
What it offers
- Live electricity and gas data from the school's own building
- Heating-season analysis, which is where most Canadian building energy goes
- Baseline and peak demand views that make waste visible
- Green Button data access, so the school can get its own consumption in a usable form
- Weather data alongside consumption, so students can separate cold from behaviour
- A place to test whether a proposed change actually worked
What learners gain
- Practical understanding of how a Canadian building uses energy through the year
- Skills in reading interval data and spotting what is out of place
- Evidence-based argument: propose a change, then prove it
- Awareness of emissions and cost as two sides of the same decision
- Experience with the data standards Canadian energy work actually uses
Background: Green Button data access in Ontario · Natural Resources Canada, energy efficiency in buildings
Energy Management System (EMS)
Canadian AI Energy Hackathon
Where the learning turns into something students build themselves.
The Canadian AI Energy Hackathon puts secondary school students in teams and gives them real Canadian energy and weather data, AI tools and mentors, then asks them to build something practical. It follows a model already proven in the Caribbean, where the Carbon Zero Institute of Trinidad and Tobago ran the first AI Energy Hackathon in June 2026 after a month of training sessions. The Canadian programme is being built on the same principle: teach first, then build.
How it helps a student learn
A hackathon is where the other use cases come together. Students use AI Foundry to research and build, draw on the kind of data a Customer Information System and an Energy Management System hold, and then have to explain their thinking to a room. Building under time pressure, working in a team and presenting the result is difficult to teach from a textbook, and it is exactly what employers and universities look for.
What it offers
- Training sessions before the event, so students arrive ready to build
- Real Canadian energy and weather data rather than textbook examples
- Mentors from industry working alongside every team
- AI Foundry as the build platform, so teams start from a trusted knowledge base
- Teams present their work to an audience of peers, teachers and partners
- Open to secondary school students, with no prior AI background required
What learners gain
- Practical experience turning an idea into something that works
- Teamwork and communication under a real deadline
- Confidence presenting technical work to an unfamiliar audience
- A portfolio piece for university applications and future employers
- A first look at climate and energy careers in the region
Background: How the first hackathon ran in the Caribbean
Canadian AI Energy Hackathon
Bring these tools to your school or community.
Tell us which use case fits your learners, and we will help you take the next step. You can also explore how to sponsor a school and put real data and tools in young people's hands.