Occupant-based DIGITal predictive MANagement to improve the built environment

Graziano Salvalai (Principal Investigator)

DIGITMAN develops a predictive approach, based on occupancy data integrated into a common digital framework, to improve building stocks management by supporting the correct allocation of technical and economic resources during the real operation of buildings. A TRL 5 will be reached, and a prototype tool will be tested in a relevant environment. Integrating dynamic occupancy data into whole life-cycle digital logbooks and using these data to predict the impact of alternative strategies are relatively unexplored fields. Moreover, a common digital framework for building management, based on common languages/interfaces/data matching methods, is still lacking as underlined by the EU. The proposed predictive approach will be based on a set of analytic methods (e.g., ML, MAS) applied to the main pillars of building management (operation, maintenance, safety) and multicriteria approaches, thanks also to the availability of experimental data from 30 buildings managed by three local public authorities.

Contribution to SDGs: 11