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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 …
Autonomous Multi-Chemistry Secondary-Use Battery Energy Storage, Mitchell Smith
Autonomous Multi-Chemistry Secondary-Use Battery Energy Storage, Mitchell Smith
Doctoral Dissertations
Battery energy storage is poised to play an increasingly important role in the modern electric grid. Not only does it provide the ability to change the time-of-day and magnitude of energy produced by renewable resources like wind and solar, it can also provide a host of other 3ancillary grid-stabilizing services. Cost remains a limiting factor in deploying energy storage systems large enough to provide these services on the scale required by an electric utility provider. Secondary-use electric vehicle batteries are a source of inexpensive energy storage materials that are not yet ready for the landfill but cannot operate in vehicles …
Optimizing The Implementation Of Green Technologies Under Climate Change Uncertainty, Mohammad Ramshani
Optimizing The Implementation Of Green Technologies Under Climate Change Uncertainty, Mohammad Ramshani
Doctoral Dissertations
In this study, we aim to investigate the application of the green technologies (i.e., green roofs (GRs), Photovoltaic (PV) panels, and battery integrated PV systems) under climate change-related uncertainty through three separate, but inherently related studies, and utilize optimization methods to provide new solutions or improve the currently available methodsFirst, we develop a model to evaluate and optimize the joint placement of PV panels and GRs under climate change uncertainty. We consider the efficiency drop of PV panels due to heat, savings from GRs, and the interaction between them. We develop a two-stage stochastic programming model to optimally place PV …