Dr Giorgos Petrou

Research summary

Over the last few years, Giorgos has developed a keen research interest on aspects regarding the monitoring and modelling of the indoor environment, especially relating to indoor overheating and air quality. His modelling experience includes data-driven statistical methods, dynamic thermal simulation models and the integration of the two for the purposes of forward and inverse uncertainty propagation. 

Giorgos' recent work has focused on the following research topics:

• The application of causal inference techniques in built environment research
• The use of Bayesian methods in the calibration of housing stock models of indoor temperature
• Modelling of indoor air quality and indoor overheating 

Giorgos' interests include:

• Indoor Environment
• Building Modelling & Calibration
• Bayesian Methods
• Uncertainty Analysis
• Programming and machine learning

Teaching summary

Giorgos is the lead lecturer and coordinator for the MSc module "Mathematical Modelling Methods for the Built Environment". The aim of the module is to teach the mathematical basis and the application of some of the main methods used in the analysis and use of mathematical models of the built environment. The three key themes are:

• uncertainty analysis (parametric and structural) of models, 
• the optimization (statistic and dynamic) of models, and 
• the use of models for decision-support in relation to cost-benefit, cost-effectiveness and multi-criteria evaluation.

Over the last few years, Giorgos has contributed to several MSc modules. His teaching activities include:

• The delivery of lectures on the topics Building Physics and Thermal Comfort, Sensitivity and Uncertainty Analysis, and Parameteric Analysis using EnergyPlus, jEPlus and Python.
• The delivery of tutorials on Research Concepts and Advanced Building Simulation.

Giorgos has successfully supervised over 10 MSc students.

Publications

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