Multi-Agent Architecture in Distributed Environment Control Systems: vision, challenges, and opportunities

• URL: http://arxiv.org/abs/2502.15663v1
• Date: Fri, 21 Feb 2025 18:41:03 GMT
• Title: Multi-Agent Architecture in Distributed Environment Control Systems: vision, challenges, and opportunities
• Authors: Natasha Astudillo, Fernando Koch,
• Abstract summary: We propose a multi-agent architecture for distributed control of air-cooled chiller systems in data centers.<n>Our vision employs autonomous agents to monitor and regulate local operational parameters and optimize system-wide efficiency.
• Score: 50.38638300332429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing demand for energy-efficient solutions in large-scale infrastructure, particularly data centers, requires advanced control strategies to optimize environmental management systems. We propose a multi-agent architecture for distributed control of air-cooled chiller systems in data centers. Our vision employs autonomous agents to monitor and regulate local operational parameters and optimize system-wide efficiency. We demonstrate how this approach improves the responsiveness, operational robustness, and energy efficiency of the system, contributing to the broader goal of sustainable infrastructure management.

Related papers

• Exploration of Multi-Element Collaborative Research and Application for Modern Power System Based on Generative Large Models [4.854786697610143]
  Generative Large Models (GLMs) provide a data-driven approach to enhancing forecasting, scheduling, and market operations. By leveragingtemporal modeling and reinforcement learning, GLMs enable dynamic energy scheduling, improve grid stability, enhance carbon trading strategies, and strengthen resilience against extreme weather events.
  arXiv  Detail & Related papers  (2025-03-26T17:58:49Z)
• Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control [0.24578723416255752]
  This paper presents a novel approach within the model-free framework of reinforcement learning, aimed at optimizing power network operations without prior expert knowledge. We demonstrate that our approach achieves a consistent reduction in power losses, while ensuring grid stability against potential blackouts.
  arXiv  Detail & Related papers  (2025-03-26T16:20:30Z)
• Hypernetwork-based approach for optimal composition design in partially controlled multi-agent systems [5.860363407227059]
  Partially Controlled Multi-Agent Systems (PCMAS) are comprised of controllable agents, managed by a system designer, and uncontrollable agents, operating autonomously.<n>This study addresses an optimal composition design problem in PCMAS, which involves the system designer's problem, determining the optimal number and policies of controllable agents, and the uncontrollable agents' problem.<n>We propose a novel hypernetwork-based framework that jointly optimize the system's composition and agent policies.
  arXiv  Detail & Related papers  (2025-02-18T07:35:24Z)
• DNN-Powered MLOps Pipeline Optimization for Large Language Models: A Framework for Automated Deployment and Resource Management [0.0]
  This research presents a novel framework that leverages Deep Neural Networks (DNNs) to optimize MLOps pipelines for Large Language Models (LLMs)<n>Our approach introduces an intelligent system that automates deployment decisions, resource allocation, and pipeline optimization while maintaining optimal performance and cost efficiency.
  arXiv  Detail & Related papers  (2025-01-14T14:15:32Z)
• Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework [80.39138462246034]
  We propose the cooperative cognitive dynamic system (CCDS) to optimize the management for UAV swarms. CCDS is a hierarchical and cooperative control structure that enables real-time data processing and decision. In addition, CCDS can be integrated with the biomimetic mechanism to efficiently allocate tasks for UAV swarms.
  arXiv  Detail & Related papers  (2024-05-18T12:45:00Z)
• Open Energy Services -- Forecasting and Optimization as a Service for Energy Management Applications at Scale [0.6495316960934344]
  We promote an approach to split the complex optimization algorithms employed by energy management systems into standardized components. This work is centered around the systematic design of a framework supporting the efficient implementation and operation of such forecasting and optimization services. It describes the implementation of the design concept which we release under the name emphEnergy Service Generics as a free and open source repository.
  arXiv  Detail & Related papers  (2024-02-23T09:46:49Z)
• Global Transformer Architecture for Indoor Room Temperature Forecasting [49.32130498861987]
  This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings. It aims at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems. Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings.
  arXiv  Detail & Related papers  (2023-10-31T14:09:32Z)
• Non-Intrusive Electric Load Monitoring Approach Based on Current Feature Visualization for Smart Energy Management [51.89904044860731]
  We employ computer vision techniques of AI to design a non-invasive load monitoring method for smart electric energy management. We propose to recognize all electric loads from color feature images using a U-shape deep neural network with multi-scale feature extraction and attention mechanism.
  arXiv  Detail & Related papers  (2023-08-08T04:52:19Z)
• 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)
• Active Distribution System Coordinated Control Method via Artificial Intelligence [0.0]
  It is necessary to control the system to provide power reliably and securely under normal voltages and frequency. We suggest that neural networks with self-attention mechanisms have the potential to aid in the optimization of the system.
  arXiv  Detail & Related papers  (2022-07-12T13:46:38Z)
• Optimization for Infrastructure Cyber-Physical Systems [3.0646173923933446]
  Cyber-physical systems (CPS) are systems where a decision making (cyber/control) component is tightly integrated with a physical system (with sensing/actuation) to enable real-time monitoring and control. Some examples of infrastructure CPS include electrical power grids; water distribution networks; transportation and logistics networks; heating, and air conditioning ( ventilation) in buildings. For control optimization, an infrastructure CPS is typically viewed as a system of semi-autonomous sub-systems with a network of sensors.
  arXiv  Detail & Related papers  (2022-05-31T00:58:54Z)
• 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.