A FRAMEWORK TO SIMULATE DIVERSE OCCUPANCY AND PRESENCE SENSING TECHNOLOGY TO REGULATE HEATING AND COOLING ENERGY IN RESIDENTIAL BUILDINGS
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With the rapid progression of human sensing technologies, High Performance Buildings (HPB) are inevitably moving towards the wide scale automation of occupancy detection for energy efficiency purposes. Occupancy patterns influence energy consumption in buildings by governing the Heating, Ventilation and Air Conditioning (HVAC) systems to regulate indoor conditions for human comfort. The integration of emerging sensing systems in residential buildings requires low-cost, low-resolution alternatives that might be subject to inaccuracies and result in errors. In Building Performance Simulation (BPS), occupancy schedules act as proxies for human presence patterns in buildings. This thesis adopts a simulation-based workflow to examine the impact of system sensing errors, like human false sensing, using occupancy schedules to quantify energy loss. A Markov-Chain analysis of the 2018 American Time Use Survey (ATUS) is used to extrapolate transition matrices and generate probabilistic driven occupancy schedules. The aims of this thesis are threefold: i) investigate the evolution and current state of BPS occupancy schedules and their connection to sensing technologies, ii) examine the effect of different human detection system configurations on total energy consumption in false sensing scenarios, and iii) introduce occupancy schedules as a new factor in the decision analysis process of sensing systems. The simulations evaluate the impact of false positives in binary occupancy modelling scenarios using Honeybee as a front-end software and EnergyPlus as a backend Building Energy Modeling (BEM) engine. An integrated approach combining occupancy schedule and sensing technology is finally described for the mutually beneficial enhancement of their performance. Overall, the results indicated that with recommended guidelines and criteria for system configurations, the use of low-cost, low-accuracy sensing technologies is warranted. The thesis provides an overview of the implications of integrating future sensing technology in building thermal energy regulation, from an error evaluation perspective, that must be considered before emerging technologies are eventually deployed across United States residential buildings in the future.