Physics-Based and Data-Driven Retrofitting Solutions for Energy Efficiency and Thermal Comfort in the UK: IoT-Validated Analysis

The application of building retrofitting solutions targeting improved energy efficiency and thermal comfort is significantly influenced by environmental and climate conditions. This study aims to automate a reliable dataset and enhance the predictability of the post-retrofit performance of the buildings. The proposed hybrid methodology utilises physics-based and data-driven methods to evaluate a range of retrofitting scenarios across diverse UK climate zones and validates an automated dataset with real-time data collected via IoT (Internet of things)-based sensors. This hybrid method enables a comprehensive assessment of retrofitting solutions' impacts on building performance. The collected data create a reliable dataset and serve as the foundation for training machine learning (ML) prediction models and support decisions in retrofit strategies. The findings reveal that in cool-humid climates, the air source heat pumps significantly perform better when compared to 58 heating systems in terms of the balance of energy efficiency and thermal comfort. Moreover, Water Source Heat Pumps (WSHPs) are recommended for colder regions. As a result, zone-specific retrofitting strategies with seasonal adjustments are recommended for achieving optimum energy efficiency and thermal comfort.