Prof. Dr. Gerardo Maria Mauro has been an Associate Professor of Technical Physics at the Department of Engineering of the University of Sannio since 2021. He received the international "2023 Young Investigator Award” in the field of energy efficiency in buildings. His main research topics concern advanced modeling and optimization of systems of heat exchange using numerical methods and machine/deep learning techniques; numerical simulation and optimization of the energy design of buildings; large-scale analysis of building stock using machine/deep learning techniques; and the development and optimization of strategies for the predictive control of energy systems.
Research Keywords & Expertise
Building Design
Building Energy Analys...
Building Energy Modeli...
Energy
Energy & the Environme...
Energy Analysis
Energy Audits
Energy Optimization
Energy Performance
Energy Planning
Energy Plus
Energy Policy
Energy Systems
Energy Systems Analysi...
Thermal Analysis
Thermal Engineering
Thermal System Design
Energy and Buildings
Multi-objective optimi...
Large-scale analysis o...
Development and Optimi...
Investigation of build...
Modeling and optimizat...
Fingerprints
74%
Energy
29%
Energy Performance
29%
Energy Plus
27%
Energy and Buildings
24%
Multi-objective optimization of building energy design or retrofit by coupling dynamic simulations, numerical optimization, meta-modeling
15%
Building Design
12%
Energy Systems
10%
Energy Optimization
8%
Investigation of building thermal envelope as concerns both critical points and innovative components to optimize energy performance
5%
Energy Analysis
5%
Modeling and optimization of thermodynamic components
Short Biography
Prof. Dr. Gerardo Maria Mauro has been an Associate Professor of Technical Physics at the Department of Engineering of the University of Sannio since 2021. He received the international "2023 Young Investigator Award” in the field of energy efficiency in buildings. His main research topics concern advanced modeling and optimization of systems of heat exchange using numerical methods and machine/deep learning techniques; numerical simulation and optimization of the energy design of buildings; large-scale analysis of building stock using machine/deep learning techniques; and the development and optimization of strategies for the predictive control of energy systems.
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