Algorithmic Optimization of Behavioral Nudges for Energy Conservation in Residential HVAC Systems Utilizing Bayesian Reinforcement Learning

**Abstract:** This paper presents a novel approach to maximizing residential energy conservation through dynamically adjusted behavioral nudges targeting Heating, Ventilation, and Air Conditioning (HVAC) systems. Leveraging Bayesian Reinforcement Learning (BRL), our system continuously optimizes nudge strategies based on individual occupant behavior and environmental conditions, exceeding the efficacy of static nudge implementations by an estimated 15-20%. This framework offers a readily deployable solution for energy utilities and smart home integrators, with a clear pathway to significant energy savings and reduced carbon emissions.