L2M – Load Forecasting Improvement in Smart Grids

This project focuses on improving short-term electricity demand forecasting, which is essential for ensuring the reliable and cost-effective operation of the power grid. Many Canadian utilities are now using demand-side management strategies such as peak shaving, reducing electricity consumption during peak hours, to lower costs and reduce strain on the grid. However, these strategies can unintentionally distort electricity consumption data, making it more difficult to accurately forecast future demand using traditional models.

The project proposes a novel, software-based forecasting solution that detects and adjusts for the effects of peak shaving to address this challenge. The approach uses advanced machine learning techniques to incorporate key indicators, such as the timing and duration of peak shaving events, into the forecasting process. This results in more accurate and robust predictions of electricity demand, even when smart grid interventions have altered the data.

The improved forecasting model will help utilities like Saint John Energy plan more efficiently, reduce their reliance on fossil-fueled backup systems, and better integrate renewable energy into the grid. These outcomes support Canada’s broader goals for environmental sustainability, grid modernization, and energy affordability.

The project contributes to building smarter and more resilient electricity systems across Canada in the long term by equipping utilities with advanced tools to manage clean energy transitions effectively.

Faculty Supervisor:

Eduardo Castillo Guerra;Ahmad Mezher

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Clean Technology; Energy and Utilities; Green/Alternative Energy

University:

University of New Brunswick

Program: