Saudi Cultural Missions Theses & Dissertations
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Item Restricted Identifying Occupancy Patterns and Profiles and their Influence on Energy Performance in High-Density Higher Education Buildings(Bashar Alfalah, 2022-12-22) Alfalah, Bashar; Shahrestani, Mehdi; Shao, LiBuildings significantly impact the environment as they account for 36% of global final energy consumption and 37% of total carbon emissions. Therefore, reducing energy consumption and mitigating carbon emissions in the building sector is of paramount importance. In order to reduce energy consumption, several factors should be considered. Among them, building occupants are one of the key drivers for the operation of building services that directly influence energy consumption and energy-related emissions in buildings. Providing information regarding building occupancy at different times of day or periods within a year can potentially contribute to finding approaches for the efficient operation of buildings in terms of energy consumption and energy-related carbon emissions. However, educational buildings, known to have high-density occupancy rates, have not been studied as sufficiently as residential and office buildings. This is owing to the paucity of occupancy data and the difficulty of collecting such data accurately. Due to these limitations, previous studies on educational buildings have limited their scope of work to a specific area in buildings during working hours on workdays, such as a classroom, lecture room, or specific floor(s) with a limited number of occupants. Therefore, the contribution of this research to the existing literature is addressing the paucity of detailed occupancy data for higher educational buildings by using high-accuracy infrared video camera sensors, identifying occupancy patterns and profiles in high-density higher education buildings, predicting future occupancy numbers in buildings at any given day by developing prediction models, and assessing the impacts of occupants on building energy consumption through the development of building energy simulation model. In addition, the occupancy data in this research were collected from an entire high-density higher educational building; a type of building that has not been explored adequately in the literature. The use of high-accuracy infrared video camera sensors was to address the issues related to the uncertainties associated with the collected occupancy data. The occupancy data was collected for a period of 12 months with five minutes intervals. The collected data was analysed using a multi-analytical approach to obtain insights, which helped to identify the main four drivers influencing the presence of occupants and identify their three patterns and profiles in a high-density case study library building. The processed data were then used as inputs to develop two machine-learning models. The use of such high-resolution occupancy data for training the prediction models increased the capability of these models to predict occupancy numbers in the building at any given day with high accuracy of over 95%. Additionally, the impact of changing the occupancy numbers, from -30% to +30% of the initial numbers, on energy consumption was assessed through the development of a building energy simulation model for an extended period of one year.18 0