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 the Caribbean's future workforce.
Each one is set out here as a use case: what it is, and how it helps a Caribbean student learn.
Why this matters for young people in the Caribbean.
Weather Normalization Research
Telling the difference between a hot year and a wasteful building.
If a building used more electricity this year than last, was that the building or was it the weather? Answering that properly is called weather normalization, and 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 the United States and Canada 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 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: US 10,770,898 B2 and CA 2,996,731 C, 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
- Built with Ryerson University's Faculty of Engineering and Architectural Science
- Already in use on real commercial and residential buildings
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 university research and graduate study in engineering
Background: US Patent 10,770,898 B2 · Canadian Patent 2,996,731 C · 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 classroom, this means an AI companion that is accurate, safe and grounded in real energy and climate knowledge rather than rumour or noise.
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. It was the foundation that student teams built on during the hackathon, turning ideas into working AI solutions in a supported setting. 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.
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
- A safer starting point for classroom projects than the open web
- The build platform student teams used to create AI solutions at the hackathon
- Support for teachers who want reliable material to plan lessons and activities
- An accessible way to introduce AI concepts to learners with no prior background
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 · ITU, digital development in small island developing states
AI Foundry
Customer Information System (CIS)
See how an energy utility really works, end to end.
A Customer Information System is the software a utility uses to run the business side of supplying energy. It manages customer accounts, meters, meter readings, usage data, billing and payments, the full journey often called meter to cash. It is the operational backbone that connects the energy a household uses to the bill it receives and the service it gets in return.
How it helps a student learn
Most young people have never seen inside the system that produces an electricity bill, yet it is one of the clearest windows into how the energy sector functions. Learning on a real CIS shows students how accounts, meters and usage data fit together, how billing is calculated, and how a utility serves thousands of customers reliably. It builds practical data literacy and opens up career pathways, from customer service and metering to data analysis and energy management, that many learners never knew existed.
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
- Data literacy: reading, checking and making sense of usage and billing data
- Understanding of how an energy utility operates end to end
- Awareness of real career pathways in the utility and energy sector
- Practical experience with operational software young people rarely see
Background: ILO, 2023 Labour Overview of Latin America and the Caribbean (youth employment) · World Bank, clean energy in the Caribbean (electricity costs and the sector)
Customer Information System (CIS)
Energy Management System (EMS)
Give a school its own energy data to learn from.
An Energy Management System collects and analyses building energy data from meters, weather stations and electricity-consumption devices, then helps track, benchmark and reduce how much energy a building uses. It turns raw readings into clear pictures of when and where energy is being used, and where it is being wasted, so a building can run more efficiently.
How it helps a student learn
When an EMS is connected to a school, that school gets its own real energy data, and that changes what learning can look like. Students can analyse how their building uses electricity across the day and the seasons, spot waste, and design efficiency projects that they can actually measure. It is a hands-on STEM tool that brings mathematics, science and sustainability together around something learners can see and change. Teachers get living data for lessons, and students practise the green skills the region needs while cutting their own school's energy use.
What it offers
- A school's own live energy data from meters and connected devices
- Clear charts that make electricity use easy to read and compare
- Benchmarking so learners can measure progress over time
- Real projects: find waste, act on it, and see the result
- A cross-subject STEM resource linking maths, science and sustainability
- Practical material for teachers built on the school's own building
What learners gain
- Data analysis skills applied to real, local energy information
- Green skills in energy efficiency and sustainability by doing, not just reading
- Project experience running a measurable efficiency initiative
- A stronger sense of agency over energy use and climate action
Background: IRENA and IISD, renewable energy profiles for SIDS and the Caribbean (regional 2027 target) · World Bank, clean energy in the Caribbean (high electricity costs)
Energy Management System (EMS)
AI Energy & Climate Hackathon
Where the learning turns into something students build themselves.
The CZITT AI Energy and Climate Hackathon puts secondary school students in teams and gives them real Caribbean energy and weather data, AI tools and mentors, then asks them to build something practical. The first one ran on 12 June 2026 in Trinidad and Tobago, after a month of educational training sessions delivered with CZITT so that students arrived ready to build rather than starting from scratch on the day. Forty-eight students from eight schools formed eight teams, supported by twenty mentors.
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. That combination of 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. The month of training beforehand means the event rewards preparation, not just confidence.
What it offers
- A month of educational training sessions with CZITT before the event
- Real Caribbean 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: See the first hackathon
AI Energy & Climate 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.