Improving Sustainable Management of University Buildings Based on Occupancy Data

Abstract

The sustainability of buildings during their life cycle could be increased by optimizing their facility management. In this sense, data-driven approaches could support the improvement of building operation and maintenance (O&M), because they can exploit collected data to provide useful correlations to assess the sustainability performance depending on the surrounding constraints. Universities are among the most relevant and largest organizations, generally hosted in multistory buildings, that could take advantage of such data to improve the sustainable goals of class occupancy and timetable. A high level of classroom occupancy is the main goal for class timetabling, and its effect on other O&M performance generally is overlooked. In the literature, class timetabling effects on university O&M, and especially on elevator maintenance tasks, have not yet been addressed in depth. Therefore this work adopted a data-driven approach to jointly optimize class scheduling and corrective maintenance actions required for elevators in university buildings. Elevator use is influenced greatly by schedule-dependent occupant movement, and thus is one of the main components of the total maintenance costs, and significantly affects safety performance. A 15-month experimental campaign on a university campus hosting as many as 7,000 occupants daily was performed to correlate occupant presence and movement with the number of corrective actions on elevators. The data-driven correlation was integrated with open-source timetabling software to assess the impact of alternative timetables (affecting occupant movement and occupancy levels) on expected maintenance needs. According to the results, the optimized timetable can reduce current elevator maintenance needs by 65%, whereas the classroom occupancy performance is reduced by only 7%, thus still leading to sustainable building use. The proposed optimization approach allows facility managers to implement a university class timetabling that achieves higher maintenance cost savings, thus moving toward more-sustainable management of building scheduling and maintenance performance in a joint manner. Sustainable management of university buildings should take into account the optimization of maintenance tasks, due to their impacts on time, costs, workforce, and business continuity needs. Such maintenance needs are related strongly to class occupancy and timetable, which imply user flows and activities, especially in complex and multistory buildings. Elevators are strongly influenced by scheduling-dependent occupants movement, and thus are one of the main components of the total maintenance costs, and significantly affect safety performance. Data-driven approaches can reduce uncertainties regarding unpredictable faults depending on scheduling. Data collected by computer maintenance management systems regarding end-user requests can be used to provide correlations between occupancy and maintenance needs, in terms of their implementation in building automation and performance systems. Such correlations can (1) support facility managers in predicting critical conditions implying corrective actions; (2) inform decision-makers about how to better define facility management contracts, because they can estimate additional efforts based on building use; and (3) improve the maintenance sustainability by allowing decision-makers to adopt optimized class occupancy (when correlations are implemented, for example, in simulation tools). The proposed approach to timetable optimization could be extended to other scheduling-based activities (e.g., offices, meeting and cultural centers, large medical offices, recreation buildings, other administrative buildings open to the public, mixed-use buildings, and ideally, large and long-term construction sites), and systems or components with which users can interact deeply depending on scheduled tasks (e.g., lighting systems, ductless air conditioning devices, safety handles, and paving).