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Deep Reinforcement Learning For Real-Time Residential Hvac Control, Evan Mckee
Deep Reinforcement Learning For Real-Time Residential Hvac Control, Evan Mckee
Masters Theses
The model-free Deep Reinforcement Learning (DRL) environment developed for this work attempts to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. The HVAC load associated with heating and cooling is an ideal candidate for price optimization through automation for two reasons: Its power footprint in a typical home is sizeable, and the required level of participation from an inhabitant is passive. HVAC is difficult to accurately model and unique for every home, so online machine learning is used to allow for real-time readjustment in performance. Energy cost for the cooling unit shown in this work is …
A Robust Hierarchical Dispatch Scheme For Active Distribution Networks Considering Home Thermal Flexibility, Cody Rooks
A Robust Hierarchical Dispatch Scheme For Active Distribution Networks Considering Home Thermal Flexibility, Cody Rooks
Masters Theses
Distribution networks are changing from passive absorbers of electric energy to active distribution networks (ADN) capable of operating and participating in electricity markets. In the context of residential microgrids, which is a type of ADN, aggregated home heating, ventilation and air-conditioning (HVAC) loads present a key opportunity to drive operational and economic objectives, facilitate high renewable energy penetration, and enhance both system resiliency and flexibility. A robust, hierarchical dispatch scheme is developed and presented in this paper, which connects an upper level multi-phase distribution optimal power flow (DOPF) to a lower level model predictive control-based (MPC) HVAC fleet controller. The …