LAUSR

Abstract In a smart building, physical and computational elements are integrated to create an environment that is energy-efficient, comfortable, and safe for its occupants. The design and development of smart buildings is a complicated task. Every smart building is a unique structure from the requirements and characteristics standpoints. Therefore, achieving reliability and real-time adaptation to environmental conditions are some of the challenges involved in smart building development. Resolving these issues requires deep insights into control theory, machine learning, system specifications, and design requirements. To address this need, this paper proposes a real-time management system for controlling various aspects of smart buildings (indoor conditions, comfort criteria, security, safety, and costs), and also presents the performance specifications, design requirements, and operating constraints for these systems. The study aims to address two less-attended problems in the related literature of building management and control. First, only a few studies have attempted to include real-time learning of buildings' subjective parameters in the model-based control design. Second, to the best of the authors' knowledge, smart building management studies are primarily focused on optimizing thermal or visual aspects of buildings, and little attention is given to the simultaneous management of all building subsystems and objectives; i.e., considering buildings' physical models, environmental conditions, comfort specifications, and occupants’ preferences and safety in the design. Accordingly, in this paper, we combine machine learning with model-based control approaches to incorporate subjective environmental parameters into the building management structure. In addition, another benefit of this study is that it integrates model-based and learning-based control schemes in a unified management structure for controlling various aspects of building performance. The proposed building management system can be applied to a variety of smart buildings in which the building parameters can be monitored and self-tuned using a well-defined set of control inputs.