Personalized and Adaptive HVAC Control Strategies in Grid-Interactive Buildings

Efficient 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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Civil Engineering

Department dc:contributor.department

Civil and Environmental Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Meimand, Mostafa Ebrahimi

Chair dc:contributor.committeechair

• Jazizadeh Karimi, Farrokh

Committee members dc:contributor.committeemember

• Gao, Xinghua
• Jin, Ming
• Garvin, Michael J.

• Adaptive controller
• HVAC systems
• Personalized thermal comfort models
• Reinforcement learning
• load flexibility

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:42289

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/124525