Computational Methods in Building Energy Efficiency Research

Dear Colleagues,

A major challenge to achieving global decarbonisation targets is building retrofitting and increasing energy efficiency. Meeting net-zero targets will require increasingly impractical rates of deployment across a range of energy-efficient retrofit measures, e.g., heat pumps, insulation, glazing, etc. Computational methods that are utilized in research in the built environment is a rapidly emerging field. Large-scale, data-driven solutions are becoming essential for analysis and optimisation of retrofit interventions.

Furthermore, while a body of work is emerging to address retrofitting and other challenges in meeting net-zero decarbonisation targets in the built environment, considerations of future sustainability are largely absent. With large shifts in climate and weather patterns predicted to occur over the next few decades, considering the effects of future climate on the choice and sustainability of energy-efficient retrofitting is becoming increasingly important. Predictive modelling incorporating future climate scenarios is of particular interest for this Special Issue.

The aims of this Special Issue are to explore recent developments in computational methods for establishing energy efficiency in buildings, including around targeted retrofit interventions; predictive modelling, including of future climate change; statistical archetypes; and optimisation. Topics related to computational methods and energy efficiency in buildings may include, but are not limited to:

• Artificial intelligence and machine learning;
• Digital twins;
• Predictive analytics;
• Extreme climate change and future climate scenarios;
• Decision support systems;
• Optimisation;
• Uncertainty quantification.

Dr. Wil Ward
Dr. Hadi Arbabi
Guest Editors

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