Dr
Mohammad
Saffari
Academic biography
Dr. Mohammad Saffari is a Principal Investigator at Dublin City University (DCU), Ireland, specialising in advanced sustainable, renewable, and energy-efficient energy systems for buildings and communities. His expertise spans hybrid thermal energy storage, heat pump integration, renewable energy systems, and AI-driven optimisation and intelligent control strategies for next-generation energy infrastructures. His research operates at the intersection of engineering, data science, and sustainability, with the overarching aim of accelerating the global transition towards net-zero, flexible, and resilient energy systems.
Dr. Saffari has an extensive international research portfolio, having contributed to high-impact multidisciplinary projects across Europe and beyond, including Spain, Germany, Australia, and Ireland. His work spans both fundamental research and applied innovation, addressing real-world challenges in building decarbonisation, renewable energy integration, and intelligent energy system design.
He leads pioneering research on hybrid thermal energy storage (TES), including phase change materials (PCM) and thermochemical storage, integrated with renewable technologies such as heat pumps and photovoltaic systems. His research advances intelligent control and optimisation frameworks - combining model predictive control (MPC), multi-objective optimisation, and machine learning- to enhance system performance, reduce carbon emissions, and unlock demand-side flexibility in buildings and district energy systems.
Dr. Saffari has played a leading role in several nationally and internationally funded research initiatives, serving as Principal Investigator (PI) of the SEAI-funded Underground Thermal Energy Storage for Decarbonising District Heating Systems (ThermStore-DH) project and the Horizon Europe-funded Hybrid Services from Advanced Thermal Energy Storage Systems (HYSTORE) project. He also serves as Co-Principal Investigator (Co-PI) and DCU lead collaborator on the SEAI-funded GEONORM geothermal energy project. His research leadership focuses on thermal energy storage, geothermal and underground energy systems, district heating decarbonisation, AI-driven energy optimisation, and smart renewable energy integration.
Through these initiatives, he works in close collaboration with leading academic institutions, industrial stakeholders, and R&D organisations across Europe to advance next-generation thermal energy storage technologies and integrated sustainable energy solutions. His research is supported by advanced modelling and simulation methodologies, including FMU-based co-simulation platforms and AI-enhanced optimisation frameworks, enabling high-fidelity performance analysis, predictive system modelling, and real-time intelligent control of complex multi-energy systems.
Beyond academia, Dr. Saffari is actively engaged in innovation and commercialisation. He is a selected participant in the ConceptionX Venture Programme, where he is developing an AI-driven platform designed to transform building retrofits through intelligent system design, predictive analytics, and techno-economic optimisation. His broader vision is to translate deep-tech research into scalable digital solutions that reshape how buildings are designed, retrofitted, and operated in a low-carbon future.
Research interests
Dr. Mohammad Saffari’s research focuses on advancing next-generation energy systems for buildings and districts, with an emphasis on integrating artificial intelligence, optimisation, and advanced thermal technologies to enable net-zero and flexible energy infrastructures.
His core research interests include:
Hybrid Thermal Energy Storage (TES): Development and integration of advanced storage solutions, including phase change materials (PCM) and thermochemical storage, to enhance energy flexibility, efficiency, and renewable utilisation.AI-Driven Energy Systems & Optimisation: Application of machine learning, deep learning, and hybrid optimisation techniques (e.g., model predictive control (MPC), multi-objective optimisation) for intelligent control, forecasting, and decision-making in complex energy systems.Smart Buildings & Digital Twins:Design and implementation of physics-informed and data-driven digital twins for buildings, enabling real-time monitoring, predictive control, and performance optimisation.Heat Pump & Renewable Integration: Advanced modelling and control of heat pumps integrated with renewable energy sources (e.g., solar PV, geothermal), supporting electrification of heating and decarbonisation.Demand-Side Flexibility & Grid Interaction: Development of strategies to enhance building-to-grid interaction, peak load reduction, and energy flexibility through coordinated control of distributed energy resources.District Heating & Low-Temperature Networks: Optimisation and control of next-generation district heating systems, including low-temperature and renewable-integrated networks.FMU-Based Co-Simulation & Energy System Modelling: Development of high-fidelity simulation frameworks combining Modelica/FMU-based models with Python-based optimisation and AI algorithms.Explainable AI & Hybrid Modelling: Integration of physics-based models with interpretable AI techniques to improve transparency, reliability, and adoption of AI in energy systems.Techno-Economic Analysis & Decision Support: Multi-criteria evaluation of energy systems considering cost, emissions, and flexibility, supporting policy, design, and investment decisions.