Dr. Navid is an accomplished engineer with a Ph.D. in Civil Engineering from Concordia University, where he led cutting-edge research in renewable resource forecasting and energy system modeling using stochastic programming. He specializes in the design and optimization of renewable energy systems, with a strong focus on enhancing the operational control of microgrids through data-driven and optimization methodologies.

His work is centered on the integration of renewable energy sources, balancing trade-offs between the electric grid and microgrid, and leveraging advanced energy storage systems to foster the development of resilient and sustainable energy infrastructures. Dr. Navid’s research aims to push the boundaries of what’s possible in energy management and system optimization.

In addition to his core work in energy systems, he has significant expertise in power system optimization and conducting techno-economic feasibility studies. He is proficient in developing machine learning, deep learning, and reinforcement learning models using Python for a variety of applications, particularly in the forecasting of renewable energy resources and electrical load demands. His application of AI-based techniques enables innovative and effective solutions across the energy sector.

Dr. Navid has also contributed extensively to education, having developed and delivered content on topics such as Machine Learning with Python, Time Series Analysis Using Deep Learning, and Optimization in Python and GAMS. His goal is to share his deep knowledge and practical expertise to help others understand and advance the frontiers of energy systems, optimization, and intelligent energy forecasting.