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Related Experiment Videos

Personalized Federated Actor-Critic Learning for Joint Cost-Comfort Optimization in Energy Communities.

Sotirios Spantideas1, Anastasios Giannopoulos2

  • 1Department of Electrical and Electronics Engineering, School of Engineering, University of West Attica, 12244 Athens, Greece.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a personalized federated deep reinforcement learning method (pFedMe) for smart homes. The new approach optimizes energy costs and thermal comfort in energy communities, outperforming existing methods.

Keywords:
deep reinforcement learning (DRL)energy communityenergy management system (EMS)energy storage systems (ESSs)heating, ventilation, and air conditioning (HVAC) systemshome automationpersonalized federated learning with using Moreau envelopes (pFedMe)smart homes

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Area of Science:

  • Artificial Intelligence
  • Smart Buildings
  • Renewable Energy Systems

Background:

  • Home energy management systems (HEMS) offer intelligent control for thermal comfort and energy efficiency but face limitations due to personalized environmental observability.
  • Smart buildings are increasingly forming energy communities to enable intelligence sharing via federated learning.
  • Existing federated learning schemes can lead to sub-optimal decisions in decentralized HEMS.

Purpose of the Study:

  • To propose a novel personalized federated deep reinforcement learning method (pFedMe) for joint optimization of energy cost and household comfort in multi-smart home energy communities.
  • To enhance the decision-making capabilities of HEMS agents by enabling shared intelligence and personalized learning.

Main Methods:

  • A personalized federated deep reinforcement learning method (pFedMe) utilizing Moreau envelopes is proposed.
  • A Twin-Delayed Deep Deterministic Policy Gradient (TD3) actor-critic model is employed for dynamic state observation and control action suggestion for Energy Storage Systems and indoor temperature regulation.
  • The TD3 model is designed to mitigate overestimation bias and improve training stability in intelligent agents.

Main Results:

  • Simulations using real data demonstrate that pFedMe achieves a beneficial trade-off between energy cost and thermal comfort.
  • The pFedMe framework consistently outperforms baseline methods (FedAvg and Fedprox) in convergence speed and overall reward.
  • The proposed method achieved approximately 10% energy cost reduction compared to FedAvg and Fedprox, with marginal impact on thermal comfort.

Conclusions:

  • The proposed pFedMe method effectively optimizes energy cost and thermal comfort in multi-smart home energy communities.
  • Personalized federated learning with TD3 actor-critic models offers a promising approach for intelligent HEMS.
  • pFedMe demonstrates superior performance in convergence and energy savings, paving the way for more efficient smart energy systems.