## 6 Conclusions  
This work introduces a novel algorithmic approach for an RS that leverages BIM and SRI data. To optimize the utilization of these data, the methodology was segmented into two algorithms, which were each related to one of two use cases. In the first use case, BIM data were employed to offer recommendations for improving the thermal envelope. A hybrid ML algorithm, following a mixed Random Forest-Decision Tree approach, was trained using the BIM model of CERTH's mZEB Smart Home in Greece, which is based on the IFC standard, along with data from the Passive House database. The second use case involved the utilization of SRI data to develop an RS for suggesting improvements for HVAC systems. This process incorporates a filtering function and the KNN algorithm to propose appropriate levels of automation for improving building services. Therefore, to achieve optimal outcomes, a range of algorithms was employed and tested, including KNN, RF, DT, Linear Regression, and Logistic Regression, to assess and determine the optimal outcomes. It is noteworthy that the inputs to the proposed RS exhibit a predominantly static nature over extended periods. These inputs include variables, such as building type, climate, area, SRI impact domain matrix, and construction year. Thus, real-world modifications to buildings would have no impact on the suggestions provided by the RS. Statistical analysis of the findings indicated that although the numerical values exhibit higher levels of predictive accuracy, the categorical variables are significantly influenced by the quality of the obtained data. Furthermore, the qualitative assessment indicated that the results obtained from the RS are aligned with the concepts of building physics and the SRI. Finally, this paper presented an RS that investigates the potential synergies between SRI and BIM data as evidenced by the results obtained from both use cases. It introduced an innovative algorithmic approach that leverages the available data to support the creation of an RS aimed at enhancing building energy efficiency. **Author Contributions:** Conceptualization, M.T.S. and D.I. ; methodology, M.T.S. and P.K. ; software, M.T.S. ; validation, M.T.S. and P.K. ; formal analysis, M.T.S. ; investigation, M.T.S., P.K., D.I. and C.T. ; resources, D.I. ; data curation, M.T.S. ; writing--original draft preparation, M.T.S. and P.K. ; writing--review and editing, M.T.S., P.K. and C.T. ; visualization, M.T.S. ; supervision, P.K. and D.I. ; project administration, P.K. and C.T. ; funding acquisition, C.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research is co-financed by Greece and the European Union (European Social Fund-SF) through the Operational Programme \"Human Resources Development, Education and Lifelong Learning 2014-2020\" in the context of the project \"Support for International Actions of the International Hellenic University\", (MIS 5154651). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available upon request from the authors. The data are not publicly available due to working on more research analyses. **Conflicts of Interest:** Researchers in Information Technologies Institute, Centre of Research & Technology or School of Science and Technology, International Hellenic University. **Abbreviations**  
The following abbreviations are used in this manuscript: