Machine Learning-Based PV and Load Forecasting for Power System Optimization: Using real data in Cambodia with IEEE 9-Bus Validation

The growing number of renewable energy sources in Cambodia, such as solar farms, makes it more difficult to schedule power system operations to satisfy demand and save operating costs. Forecasting is one way to manage energy. An excessively high prediction accuracy indicates that managing the spatial energy in Economic Dispatch (ED) and Optimal Power Flow (OPF) is simple. One graduate intern will travel to Concordia University to conduct advanced research on machine-learning-based photovoltaic (PV) and load forecasting models using data from Electricité Du Cambodge (EDC) in Cambodia and the weather variable chosen from public data from NASA POWER in collaboration with the IEEE 9-Bus. The expected outcomes are improved forecasting accuracy, reduced grid power losses, reduced energy costs, and increased use of renewable energy. The findings will support Cambodia’s transition to clean energy and aid in the planning of a more reliable and sustainable power system. The project will also help Canada become a leader in renewable energy innovation, improve cooperation between Cambodia and Canada, and exchange new research findings.

Faculty Supervisor:

Manar Amayri

Student:

Partner:

Institute Of Technology Of Cambodia

Discipline:

Engineering

Sector:

Artificial Intelligence; Energy and Utilities; Natural Resources

University:

Concordia University

Program: