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

Avisek Naug, Marcos Quinones-Grueiro and Gautam Biswas ()
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Avisek Naug: Hewlett Packard Labs (HPE), Milpitas, CA 95035, USA
Marcos Quinones-Grueiro: Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37235, USA
Gautam Biswas: Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37235, USA

Energies, 2025, vol. 18, issue 6, 1-23

Abstract: 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.

Keywords: energy consumption; reinforcement learning; relearning; nonstationary systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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