Dr. Meng Zhang is a PhD candidate in Energy and Power Engineering, specializing in the impacts of heating electrification and cooling on future electricity demand. With expertise in building simulation and ANN predictive models, Meng is responsible for forecasting building heating demand, a basic element for digital twin project. Her research work includes developing predictive models to accurately assess energy needs, positioning her to effectively assist colleagues in building digital twin energy systems for enhanced energy management.
Professor Zhibin Yu, Chair of Energy Engineering, at the School of Engineering of the University of Liverpool. He leads the Energy Research Cluster for the school and holds a Royal Society Industrial Fellowship (2023-2027). Professor Yu’s research focuses on thermal energy technologies and their fundamental thermodynamic, heat transfer, and fluid-dynamic challenges. He is particularly interested in developing novel technologies for sustainable heating, cooling, and power generation, including heat pumps, refrigeration, energy storage, district heating/cooling networks, multi-vector/integrated energy systems, and energy system digital twins. Prof Yu is the lead of WP3.
Dr. Zahra Hajabdollahi Ouderji
I specialize in modeling, dynamic simulation, and optimization of heat pumps integrated with thermal energy storage (TES) to enhance energy flexibility and peak load shifting. Holding a PhD in Mechanical Engineering, I have extensive expertise in energy system modeling, multi-vector energy systems and optimization techniques for sustainable energy applications.
My work begins with conducting dynamic simulations to analyze heat pump behavior under various operating conditions. These simulations provide insights into system performance and are essential for developing a robust digital twin framework that accurately represents real-world operations.
To bridge the gap between simulations and physical performance, I will calibrate and refine digital models using experimental data. This ensures that the digital twin remains adaptive and responsive to real-time conditions, improving accuracy and reliability.
Building on these calibrated models, I develop advanced control strategies for future environmental conditions anticipate to enhance system efficiency, optimize decision-making, and enable intelligent, real-time energy management.
By integrating advanced simulations, ML-driven calibration, and optimized control strategies, I contribute to the development of smarter, more resilient energy solutions for sustainable energy management.
