An End-to-End Relearning Framework for Building Energy Optimization

Building HVAC systems face significant challenges in energy optimization due to changing building characteristics and the need to balance multiple efficiency objectives. Current approaches are limited: physics-based models are expensive and inflexible, while data-driven methods require extensive data collection and ongoing maintenance. This paper introduces a systematic relearning framework for HVAC supervisory control that improves adaptability while reducing operational costs. Our approach features a Reinforcement Learning controller with self-monitoring and adaptation capabilities that responds effectively to changes in building operations and environmental conditions. We simplify the complex hyperparameter optimization process through a structured decomposition method and implement a relearning strategy to handle operational changes over time. We demonstrate our framework’s effectiveness through comprehensive testing on a building testbed, comparing performance against established control methods.