Bi-level energy-efficient occupancy profile optimization integrated with demand-driven control strategy: University building energy saving

Abstract

University (Educational) buildings comprise a considerable contribution of commercial buildings energy consumption. Optimizing operational measures are widely suggested for energy saving in university buildings. Hence, in this study, a bi-level energy-efficient occupancy profile optimization method using a metaheuristic algorithm, integrated with a demand-driven control strategy adjusted with dynamic set-point temperature is proposed to optimize the energy consumption within in a university departmental building. To proceed with the proposed energy saving strategies; first, building’s thermal behavior and AHU system performance are identified and modeled through Artificial Neural Networks (ANN), using the established data acquisition system’s experimentally measured data. Afterwards, the bi-level course timetable optimization has been introduced where, the first level obtained the optimal occupancy profile and the second level assigned the courses to the timeslots within the first level’s solution. Up to 1.23% of the energy was saved, solely by means of optimizing the timetable. Then, the demand-driven control strategy is implemented, and by predicting the heating demand, thermal comfort is maintained while the building energy use is decreased by 11.65%. Finally, the demand-driven control scheme is integrated with the optimized course timetable, which leads to an enhanced controller performance and achieving a saving potential of up to 18.97%.