L2M – EnerCast

I intend to commercialize a foundation model for forecasting energy production and consumption time series, addressing challenges like renewable integration, storage optimization, and demand-supply balancing. Resources such as HVACs, EVs, ESSs, and solar roofs introduce uncertainty due to temperature sensitivity, price response, and weather variability, making grid management and the transition to smart grids more complex. Our model incorporates external factors like outages and extreme events to improve forecast accuracy and mitigate peak prices. We also aim to build automated energy management systems that reduce customer costs while helping system operators balance the grid. Additionally, we plan to develop an LLM-based chatbot that offers intuitive, AI-driven insights for market participants and energy traders, making advanced forecasting accessible without requiring technical expertise.

Faculty Supervisor:

Hamidreza Zareipour

Student:

Partner:

Edmonton Unlimited

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Public administration

University:

University of Calgary

Program: