Resumen
The Smart Readiness Indicator (SRI) is a newly developed framework that measures a building?s technological readiness to improve its energy efficiency. The integration of data obtained from this framework with data derived from Building Information Modeling (BIM) has the potential to yield compelling results. This research proposes an algorithm for a Recommendation System (RS) that uses SRI and BIM data to advise on building energy-efficiency improvements. Following a modular programming approach, the proposed system is split into two algorithmic approaches linked with two distinct use cases. In the first use case, BIM data are utilized to provide thermal envelope enhancement recommendations. A hybrid Machine Learning (ML) (Random Forest?Decision Tree) algorithm is trained using an Industry Foundation Class (IFC) BIM model of CERTH?S nZEB Smart Home in Greece and Passive House database data. In the second use case, SRI data are utilized to develop an RS for Heating, Ventilation, and Air Conditioning (HVAC) system improvement, in which a process utilizes a filtering function and KNN algorithm to suggest automation levels for building service improvements. Considering the results from both use cases, this paper provides a solid framework that exploits more possibilities for coupling SRI with BIM data. It presents a novel algorithm that exploits these data to facilitate the development of an RS system for increasing building energy efficiency.