Residential buildings contribute 30% of the UK’s total final energy consumption. However, with less than one percent of its housing stock being replaced annually, retrofitting existing homes has significant importance in meeting energy-efficiency targets. Consequently, many physics-based and data-driven models and tools have been developed to analyse the effects of retrofit strategies from various points of view. This paper aims to develop a data-driven AI model that predicts buildings’ energy performance based on their features under various retrofit scenarios. In this context, four different machine learning models were developed based on the EPC (Energy Performance Certificate) dataset for residential buildings and standard assessment procedure (SAP) guidelines in the UK. Additionally, an interface was designed that enables users to analyse the effect of different retrofit strategies on a building’s energy performance using the developed AI models. The results of this study revealed the artificial neural network as the most accurate predictive model, with a coefficient of determination (R²) of 0.82 and a mean percentage error of 11.9 percent. However, some conceptual irregularities were observed across all the models when dealing with different retrofit scenarios. All summary, such tools can be further improved to offer a potential alternative or support to physics-based models, enhancing the efficiency of retrofitting processes in buildings.

The assessment of buildings’ energy performance plays a critical role in achieving global sustainability goals, particularly in reducing carbon emissions and improving energy efficiency. In this context, various modelling approaches have been developed to evaluate building energy performance. Among them, data-driven models, such as machine learning (ML) algorithms, have gained significant attention in recent years due to their scalability, fast development process, and high predictive accuracy. However, a key limitation of these models is their limited interpretability, which can negatively affect their application particularly in decision-making and retrofit planning processes. To address this issue, SHapley Additive exPlanations (SHAP) has emerged as a promising approach for interpreting complex ML models by quantifying the contribution of each input feature to the model’s predictions. As a result, this study developed an XGBoost ML model that predicts energy performance of residential buildings in the UK with an R² value of more than 0.98. After that, SHAP method was applied to explore and explain the effect of individual features on model outcomes, which highlighted that SHAP framework can be a strong complementary approach for enhancing the interpretability and practical applicability of black-box models in building energy performance analysis.

This study aimed to improve indoor air quality (IAQ) in an existing college building in London by addressing two key pollutants: PM_2.5 particles (from indoor and outdoor sources) and SARS-CoV-2 as a biological contaminant. Various mitigation strategies were assessed, including hybrid ventilation that combined CIBSE-recommended rates with partial window and door opening. The effectiveness of HEPA-based air purifiers (APs) and upper-room ultraviolet germicidal irradiation (UVGI) systems with different intensities was also evaluated for reducing viral transmission and the basic reproduction number (R₀). To manage PM_2.5 in the kitchen, HEPA and in-duct MERV13 filters were integrated into the ventilation system. Results showed that hybrid ventilation outperformed mechanical systems by achieving greater reductions in infection probability (P_I) and maintained higher performance as the number of infectors increased, showing only a 2.5–16% drop, compared to 35% with mechanical ventilation. An R₀ analysis indicated that UVGI is more suitable in high-risk settings, while APs combined with hybrid ventilation are effective in lower-risk scenarios. The findings also emphasize that combining Supply–Exhaust ventilation with APs or MERV13 filters is crucial for maintaining safe IAQ in kitchens, aligning with the WHO’s short- and long-term exposure limits.