When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
Considering the ever-growing increase in the world energy consumption and the fact that buildings contribute a large portion of the global energy consumption arises a need for detailed investigation towards more effective energy performance of buildings. Thus, monitoring, estimating, and reducing buildings’ energy consumption have always been important concerns for researchers and practitioners in the field of energy management. Since more than 80% of energy consumption happens during the operation phase of a building’s life cycle, efficient management of building operation is a promising way to reduce energy usage in buildings. Among the parameters influencing the total building energy consumption, building occupants’ presence and preferences could have high impacts on the energy usage of a building. However, the occupancy parameter is one of the most difficult parameters to predict due to the uncertainties associated with it. To consider the effect of occupancy on building energy performance, different occupancy models, which aim to estimate the space utilization patterns, have been developed by researches. However, providing a comprehensive occupancy model, which could capture all important occupancy features, is still under development. Moreover, researchers investigated the effect of the application of occupancy-centered control strategies on the efficiency of the energy-consuming systems. However, there are still many challenges in this area of research mainly related to collecting, processing, and analyzing the occupancy data and the application of intelligent control strategies. In addition, generally, there is an inverse relationship between the energy consumption of operational systems and the comfort level of occupants using these systems. As a result, finding a balance between these two important concepts is crucial to improve the building operation. The optimal operation of building energy-consuming systems is a complex procedure for decision-makers, especially in terms of minimizing the energy cost and the occupants’ discomfort.
On this premise, this research aims to develop a new simulation-based multi-objective optimization model of the energy consumption in open-plan offices based on occupancy dynamic profiles and occupants’ preferences and has the following objectives: (1) developing a method for extracting detailed occupancy information with varying time-steps from collected Real-Time Locating System (RTLS) occupancy data. This method can capture different resolution levels required for the application of intelligent, occupancy-centered local control strategies of different building systems; (2) developing a new time-dependent inhomogeneous Markov chain occupancy prediction model based on the derived occupancy information, which distinguishes the temporal behavior of different occupants within an open-plan office; (3) improving the performance of the developed occupancy prediction model by determining the near-optimum length of the data collection period, selecting the near-optimum training dataset, and finding the most satisfying temporal resolution level for analyzing the occupancy data; (4) developing local control algorithms for building energy-consuming systems; and (5) integrating the energy simulation model of an open-plan office with an optimization algorithm to optimally control the building energy-consuming systems and to analyze the trade-off between building energy consumption and occupants’ comfort. After conducting an extensive literature review and finding the shortcomings of available occupancy monitoring techniques, this study uses a suitable sensing technique (i.e., Bluetooth RTLS) to collect detailed occupancy data. The monitoring technique distinguishes between different occupants within an open-plan office so that the occupancy profiles can reflect the occupancy changes in shared spaces with acceptable accuracy. Applying occupant behavior analytics on the raw occupancy data collected by RTLS leads to the generation of occupant-specific features including the personal profile for each occupant with varying time-steps. Having this information, a new adaptive probabilistic occupancy prediction model is developed to be used for occupancy prediction of open-plan offices. Recognizing different occupants using proper sensing technique eventually leads to the identification and application of different occupants’ preferences related to the building systems. Thus, local control of these systems is achievable. Having the occupancy information along with the indoor environmental conditions, this study contributes to the exploration of solutions produced by the integration of a simulation model with a multi-objective optimization process.
It is found that the occupancy perdition model is able to accurately estimate occupancy patterns of the open-plan office at occupant and zone levels. Also, the proposed integrated model improves the energy management of buildings by developing intelligent, optimal, and occupancy-centered local control strategies and evaluating the effect of them on building energy-consuming systems and the occupants’ satisfaction.