Building occupants, building physics, HVAC systems, and controls make up a complex dynamical system with continuous and discrete states and interactions, i.e., a hybrid system. Traditionally, occupant physics and behavior are abstracted from the building engineering through static design requirements and simple setpoints. At most, the interaction between the systems is captured by changing setpoints based on occupancy. This abstraction neglects the autonomy of users to change setpoints and create over-rides that often forfeit the savings that the control system aimed to achieve. As users increasingly interact with home energy IoT devices, this shared autonomy will grow in significance. This research proposes a framework that may serve as a model-based estimator for building controls that realize energy savings without invasive and ineffective behavioral interventions. The human-in-the-loop (HITL) framework consists of three components to model a single-zone single-occupant system: zone thermodynamics, a physiological comfort model, and a behavior model based on Social Cognitive Theory (SCT). Simulations of the HITL control system demonstrate the importance of capturing the hybrid dynamics from discrete actions (e.g., changes to the thermostat) and transients associated with physiological comfort and decision-making.