Personalized and Adaptive HVAC Control Strategies in Grid-Interactive Buildings

dc.contributor.authorMeimand, Mostafa Ebrahimien
dc.contributor.committeechairJazizadeh Karimi, Farrokhen
dc.contributor.committeememberGao, Xinghuaen
dc.contributor.committeememberJin, Mingen
dc.contributor.committeememberGarvin, Michael J.en
dc.contributor.departmentCivil and Environmental Engineeringen
dc.date.accessioned2025-02-07T09:00:13Zen
dc.date.available2025-02-07T09:00:13Zen
dc.date.issued2025-02-06en
dc.description.abstractEfficient control of HVAC (Heating, Ventilation, and Air Conditioning) systems is crucial for balancing demand and supply of energy in buildings, particularly during peak demand pe-riods. This dissertation aims to address three research gaps. First, previous research effortshave focused on decreasing energy consumption over peak time while considering comfort asa fixed range of temperatures or using generic indices for a population rather than focusingon individual thermal preferences. In response to this gap, a novel occupant-centric con-trol strategy is proposed to minimize energy costs while prioritizing personalized comfort.The proposed controller is tested in a simulation environment under different contextualconditions and in a real-world testbed. Second, another challenge of the existing HVACsystem controllers is finding the right balance between energy cost and occupant comfort inco-optimization formulations. The proposed balance should be adapted to different environ-ments. To address this challenge, an evolutionary Reinforcement Learning (RL) approachis introduced that enables the system to learn and adapt the trade-off coefficient betweenenergy and comfort optimization, enhancing the system's adaptability to different environ-mental and contextual conditions. Third, existing load flexibility models mainly considerspace-related factors and often overlook individual preferences. In the final phase, we shiftour focus from spaces to people and examine how current load flexibility models may affectindividual thermal comfort. Also, we devise a feature to predict load-shedding potentialbased on user properties. The performance of these three frameworks/models is assessedthrough a comprehensive uncertainty quantification analysis, taking into account the di-versity in occupants' preferences and the number of individuals present. Furthermore, theproposed approaches are compared with benchmark controllers from existing literature in asimulated environment. To validate their feasibility, a real-world experiment in an apart-ment unit as a practical test-bed is conducted. This research aims to improve the energyefficiency of HVAC systems, improve overall comfort experience, and evaluate the effect ofindividual comfort based on the current load flexibility models.en
dc.description.abstractgeneralHeating, Ventilation, and Air Conditioning (HVAC) systems are essential for maintaining comfort in buildings but account for a significant portion of energy use, especially during peak demand times when energy consumption is at its highest. Optimizing HVAC systems can help reduce costs and energy peaks, yet traditional approaches often overlook the diverse comfort needs of occupants. As buildings become more connected to the energy grid, there is a growing need for smarter, occupant-centered HVAC strategies that balance energy savings with personalized comfort. This research explores innovative solutions to address these chal-lenges. First, it introduces advanced control strategies that incorporate individual comfortpreferences into HVAC systems. These strategies help reduce energy peaks while ensuringoccupants remain comfortable, creating systems that are more efficient and user-friendly.Second, the study develops adaptable technologies, using cutting-edge artificial intelligence,to make HVAC systems smarter. These systems can learn and adjust to changing environ-ments, making them more effective in both simulations and real-world scenarios. Finally,the research shifts focus from buildings as a whole to the people inside them. It evaluateshow energy-saving programs affect individual comfort and proposes new tools to predict andoptimize energy savings without sacrificing comfort. By combining personalized comfortmodels, adaptable technologies, and user-focused strategies, this work paves the way forbuilding systems that save energy, reduce costs, and prioritize the well-being of occupants.The findings not only highlight practical solutions for today's energy challenges but also offera glimpse into the future of sustainable and adaptive building design.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:42289en
dc.identifier.urihttps://hdl.handle.net/10919/124525en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAdaptive controlleren
dc.subjectHVAC systemsen
dc.subjectPersonalized thermal comfort modelsen
dc.subjectReinforcement learningen
dc.subjectload flexibilityen
dc.titlePersonalized and Adaptive HVAC Control Strategies in Grid-Interactive Buildingsen
dc.typeDissertationen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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