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.
Urban building energy modeling (UBEM) requires a sophisticated computational framework to simulate and analyze the energy performance of buildings within an urban context. UBEM integrates detailed models of individual building systems, environmental conditions, and occupant behavior to comprehensively understand energy consumption patterns in urban areas. This integration is crucial for understanding energy consumption patterns in urban areas. However, a significant challenge in UBEM is the uncertainty in model inputs, especially when expanding the scope from a single building to a district scale. This highlights the importance of accurate input parameters for realistic building performance simulations. The accuracy of building and urban energy simulations is strongly influenced by occupant-related inputs. These inputs are critical as they are influenced by the inherently random nature of human behavior. However, in most existing urban-scale building energy models, fixed default occupant-related schedules are used to model and simulate different building typologies, which might not necessarily capture the stochastic behavior associated with occupants. The main reason is the lack of data availability to model dynamic occupant-related schedules. As a result, unrealistic peaks in energy demand occur when modeling several buildings of the same type, as they would use the same occupancy schedules. Consequently, electric or thermal distribution networks would be over-dimensioned to meet such peak loads. This thesis intends to address the fundamental research queries pertaining to one of the main origins of UBEM uncertainty: occupant-related parameters, how they can be integrated into UBEM, and how these uncertainties impact the urban building energy simulation results. In order to answer these research questions, this research aims to develop a framework to extract typical occupant-related schedules from historical time-series data in mixed-use districts and model them, taking into account the unpredictable aspects of occupant behavior.
This research has the following objectives: (1) creating a comprehensive database by consolidating 3D geometry models with information such as construction year, typology, construction, and material from different public and private sources. This database is viewed as a digital twin that can be augmented with other information about the buildings; (2) developing a standardized building occupancy scheduling data model and proposing a data catalog structure to assign the occupant-related schedules as a function of the building types input to UBEM; (3) developing a data-driven method for extracting representative occupant-related schedules, mainly concerning electrical equipment use within institutional buildings of various types in different climate zones using historical metered data; (4) developing a novel stochastic model to dynamically generate the occupant-related schedules using the Markov-Chain Monte Carlo technique based on the derived electrical equipment schedules that illustrate variations in plug load usage and associated occupancy presence schedules within institutional buildings. The stochastic-based schedules are generated to dynamically model the energy demand at building and urban scale; (5) enhancing the efficiency and precision of the developed stochastic model by substituting the Markov-Chain Monte Carlo method with a Gaussian mixture model to reduce computation time and increase accuracy; (6) incorporating the stochastic model into a UBEM system using the established data model to form building archetypes based on stochastic methods; (7) the final step involves simulating building energy at a neighborhood level and validating the model by comparing its outcomes with actual data.
Overall, the output of this study generates the occupant-related schedules for each new simulation of the buildings to support the stochasticity of the occupant behavior. By addressing the challenge of uncertainty and focusing on occupant-related parameters, this research significantly enhances the accuracy of energy predictions in urban areas. The implications of this work are far-reaching, supporting sustainable and efficient urban building design and operation and informing energy policy and urban planning strategies globally.