Analysis of Model-Agnostic Meta-Reinforcement Learning on Automated HVAC Control

Date

2025-01-01

Author

Filiz, Ulas
Hekimoglu, Mehmet Burak
Alioğlu, Alper
Ulusoy, İlkay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper introduces a Model-Agnostic Meta-Reinforcement Learning framework for HVAC automation, integrating Model Agnostic Meta-Learning with Double Deep Q-Networks to improve adaptability across varying environmental conditions. The proposed approach is evaluated using Sinergym, an EnergyPlus-integrated RL Simulation framework, and benchmarked against conventional RL-based HVAC controllers. Results demonstrate that Model-Agnostic Meta-Learning integrated Double Deep Q-Network achieves a 7% reduction in overall power consumption while dynamically adapting to climate variations. These findings highlight the potential of Model Agnostic Meta-Learning in optimizing HVAC control strategies.

Subject Keywords

EnergyPlus, HVAC, MAML-DDQN, Model-Agnostic Meta-Learning, Reinforcement Learning, Sinergym

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105015435215&origin=inward
https://hdl.handle.net/11511/115807

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Department of Electrical and Electronics Engineering, Conference / Seminar