This study introduces an expert-guided decision-support platform developed to improve the selection of building retrofit measures for energy efficiency. The platform addresses a key gap in existing tools by combining expert input with systematic decision logic, offering a more transparent and adaptable approach to retrofit planning. Unlike simulation-based or policy-orientated systems, this platform focuses on supporting real-world decisions at the property level. It allows for both general (global) and building-specific configurations, giving users the flexibility to define and adjust decision criteria based on retrofit needs. A three-phase analytical workflow supports users via assigning expert importance, classifying assessment criteria, and deciding on a ranking method. The strategy combines data-driven and expert-based weighing methodologies, resulting in balanced and context-aware outputs. The system includes an explainable AI module that generates editable reasons for final recommendations, allowing stakeholders to better understand and discuss decisions. The platform’s efficacy was demonstrated through a case study of a mid-terrace house, showing strong potential for supporting consistent, stakeholder-informed, and auditable retrofit decisions. This work contributes a flexible and scalable solution of practical value to planners, housing authorities, and retrofit consultants.

The comprehensive assessment of retrofitting techniques is essential for improving energy efficiency and sustainability in residential buildings. This study presents a novel hybrid decision-making framework that integrates explainable artificial intelligence (XAI) with advanced multi-criteria evaluation techniques. The method uses the Shapley Additive Explanations (SHAP) interaction matrix to obtain weights for each criterion. The matrix reports both the direct effect of each variable and the interaction terms produced by the model. These values tie the weighting scheme to the behaviour recorded in the dataset. The evaluation based on relative utility and nonlinear standardisation (ERUNS) method ranks the retrofit measures. It places all criteria on a shared numeric scale through non-linear standardisation and applies a utility rule that produces one score for each option. The use of SHAP interaction weights together with the ERUNS scoring rule produces a ranking system tied to recorded data and stated weight choices. The proposed methodology shows how each retrofit option responds to the interaction structure of the criteria and the utility rule used to score them. The case study on residential buildings shows how this method works on an actual dataset and produces a defined order of retrofit measures. The results show which options rise or fall under the given criteria and weight settings.