Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL

• URL: http://arxiv.org/abs/2412.20946v2
• Date: Tue, 15 Jul 2025 07:46:57 GMT
• Title: Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL
• Authors: Nicolas M Cuadrado Avila, Samuel Horváth, Martin Takáč,
• Abstract summary: This work studies the challenge of optimal energy management in building-based microgrids through a collaborative and privacy-preserving framework.<n>We evaluate two common RL algorithms (PPO and TRPO) in different collaborative setups to manage distributed energy resources.<n>Our approach emphasizes reducing energy costs and carbon emissions while ensuring privacy.
• Score: 5.195669033269619
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work studies the challenge of optimal energy management in building-based microgrids through a collaborative and privacy-preserving framework. We evaluated two common RL algorithms (PPO and TRPO) in different collaborative setups to manage distributed energy resources (DERs) efficiently. Using a customized version of the CityLearn environment and synthetically generated data, we simulate and design net-zero energy scenarios for microgrids composed of multiple buildings. Our approach emphasizes reducing energy costs and carbon emissions while ensuring privacy. Experimental results demonstrate that Federated TRPO is comparable with state-of-the-art federated RL methodologies without hyperparameter tuning. The proposed framework highlights the feasibility of collaborative learning for achieving optimal control policies in energy systems, advancing the goals of sustainable and efficient smart grids. Our code is accessible \href{https://github.com/Optimization-and-Machine-Learning-Lab/energy_fed_trpo.git}{\textit{this repo}}.

Related papers

• A Reinforcement Learning Approach for Optimal Control in Microgrids [43.122212629962235]
  Microgrids provide a promising solution by enabling localized control over energy generation, storage, and distribution.<n>This paper presents a novel reinforcement learning (RL)-based methodology for optimizing microgrid energy management.
  arXiv  Detail & Related papers  (2025-06-28T20:10:00Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning [40.71019623757305]
  This paper presents Sinergym, an open-source Python-based virtual testbed for large-scale building simulation, data collection, continuous control, and experiment monitoring.
  arXiv  Detail & Related papers  (2024-12-11T11:09:13Z)
• Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning [67.95280175998792]
  A novel adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association ins. We employ inverse RL (IRL) to automatically learn reward functions without manual tuning. We show that the proposed MA-AL method outperforms traditional RL approaches, achieving a $14.6%$ improvement in convergence and reward value.
  arXiv  Detail & Related papers  (2024-09-27T13:05:02Z)
• Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems [3.716849174391564]
  We study the energy-sharing problem in a system consisting of several DERs. We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production. We propose distributed decision-making policies to solve the formulated problem, where we utilize the notion of dynamic regret as the performance metric.
  arXiv  Detail & Related papers  (2024-05-29T11:54:11Z)
• EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management [0.0]
  This paper introduces EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management framework. It enables user-centric and multi-objective energy management by allowing each prosumer to select from a range of personal management objectives. The efficacy of EnergAIze was evaluated through case studies employing the CityLearn simulation framework.
  arXiv  Detail & Related papers  (2024-04-02T23:16:17Z)
• Generalized Policy Learning for Smart Grids: FL TRPO Approach [6.058785372434129]
  Federated Learning (FL) can train models on heterogeneous datasets while maintaining data privacy. This paper introduces a framework that combines FL with a Trust Region Policy Optimization (FL TRPO) aiming to reduce energy-associated emissions and costs.
  arXiv  Detail & Related papers  (2024-03-27T10:47:06Z)
• Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households [3.901860248668672]
  We present a real-life pilot study that studies the effectiveness of reinforcement-learning (RL) in coordinating the power consumption of 8 residential buildings to jointly track a target power signal. Our results demonstrate satisfactory power tracking, and the effectiveness of the RL-based ranks which are learnt in a purely data-driven manner.
  arXiv  Detail & Related papers  (2023-10-29T21:10:38Z)
• A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks [53.561797148529664]
  Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. The current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization.
  arXiv  Detail & Related papers  (2023-06-25T13:10:38Z)
• Energy Management of Multi-mode Plug-in Hybrid Electric Vehicle using Multi-agent Deep Reinforcement Learning [6.519522573636577]
  Multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization. This paper studies a multi-agent deep reinforcement learning (MADRL) control method for energy management of the multi-mode PHEV. Using the unified DDPG settings and a relevance ratio of 0.2, the proposed MADRL system can save up to 4% energy compared to the single-agent learning system and up to 23.54% energy compared to the conventional rule-based system.
  arXiv  Detail & Related papers  (2023-03-16T21:31:55Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Low Emission Building Control with Zero-Shot Reinforcement Learning [70.70479436076238]
  Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency. We show it is possible to obtain emission-reducing policies without a priori--a paradigm we call zero-shot building control.
  arXiv  Detail & Related papers  (2022-08-12T17:13:25Z)
• Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization [57.88118988775461]
  We propose PROjected Feasibility (PROF) to enforce convex operational constraints within neural policies. We demonstrate PROF on two applications: energy-efficient building operation and inverter control.
  arXiv  Detail & Related papers  (2021-05-19T01:58:10Z)
• Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem [4.338938227238059]
  Microgrids with energy storage systems and distributed renewable energy sources play a crucial role in reducing the consumption from traditional power sources and the emission of $CO$. Connecting multi microgrid to a distribution power grid can facilitate a more robust and reliable operation to increase the security and privacy of the system. The proposed model consists of three layers, smart grid layer, independent system operator (ISO) layer and power grid layer.
  arXiv  Detail & Related papers  (2021-03-10T23:01:22Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.