AI4DA in Energy

Powering the grid of the future is a critical societal challenge. Electricity systems must deliver reliable, affordable, and sustainable power while integrating a growing mix of resources: traditional and nuclear plants, renewable energy like wind and solar, and distributed assets such as batteries, electric vehicles, and smart appliances.

This shift creates new challenges. Renewable resources are variable and hard to predict, and distributed energy resources add uncertainty both at the utility and household level. Energy supply and demand also don’t always align, increasing the need for storage solutions and demand response programs. At the same time, electrification of transportation, heating, and industry — plus the rapid growth of energy-hungry data centers — puts even more pressure on the grid.

At a Glance

Health Expenditure

Canada spends over $300 billion annually on healthcare, accounting for about 12% of its GDP. Health expenditure has more than doubled since 2005 and is projected to continue rising.

Health Workforce

Canada’s healthcare workforce includes doctors, nurses, and other health professionals, but shortages remain a major challenge, especially in rural areas.

Hospital Capacity

Canada has about 1,300 hospitals with 2.5 beds per 1,000 people. Many hospitals operate at or near full capacity as demand for services continues to rise.

Wait Times

Canada continues to face long wait times for healthcare services, especially elective procedures and specialist consultations.

How AI Can Help

Resource Allocation

Data analysis can help optimize resource allocation, such as determining the most efficient way to schedule medical staff, allocate hospital beds, and manage operating rooms. Machine learning can assist in predicting patient demand and staffing needs based on historical data.

Patient Scheduling

Data analysis can optimize patient scheduling, reducing wait times and ensuring that healthcare providers are efficiently utilized. It can also analyze patient data to predict appointment no-shows and enable better scheduling strategies.

Supply Chain Management

Healthcare facilities rely on a steady supply of medications, medical equipment, and other resources. Data analysis can optimize inventory management, helping prevent shortages or excessive waste.

Optimizing Clinical Trials

Data analysis and machine learning can optimize the design of clinical trials, helping researchers make better use of resources and conduct trials more efficiently.

Bed Management

Data analysis can help hospitals manage bed allocation more efficiently. Machine learning can predict patient admissions and discharges, allowing for proactive bed assignment and reduced wait times.

Current Projects

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 Lifting Solutions

AI4DA is collaborating with Lifting Solutions to apply advanced analytics to inventory management and operational planning. By analyzing historical operational data, customer demand, and product usage patterns, the project helps improve forecasting accuracy, optimize inventory decisions, and provide data-driven insights into future product needs. This collaboration demonstrates how advanced analytics can address real-world business challenges by transforming operational data into practical decision-support tools.

HIGH-SPEED INTERNET AND ENERGY FOR ALL

Billions of people still lack reliable internet and affordable energy — two essentials for modern life.

Vulnerable groups, including women, ethnic minorities, rural communities, low-income households, and internal migrants, are the most affected. Limited access doesn’t just mean fewer opportunities online; it also deepens energy poverty, restricts education and jobs, and widens inequality.

Our project explores three big questions:

  • Who are the vulnerable groups most in need of support?
  • Where should infrastructure investment be prioritized?
  • How can reliable internet and energy truly reach everyone?