Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling

• URL: http://arxiv.org/abs/2510.26603v1
• Date: Thu, 30 Oct 2025 15:33:52 GMT
• Title: Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling
• Authors: Reda El Makroum, Sebastian Zwickl-Bernhard, Lukas Kranzl,
• Abstract summary: This paper presents an agentic AI HEMS where LLMs autonomously coordinate multi-appliance scheduling from natural language requests to device control.<n>A hierarchical architecture combining one orchestrator with three specialist agents uses the ReAct pattern for iterative reasoning.<n>We open-source the complete system including orchestration logic, agent prompts, tools, and web interfaces to enable, extension, and future research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The electricity sector transition requires substantial increases in residential demand response capacity, yet Home Energy Management Systems (HEMS) adoption remains limited by user interaction barriers requiring translation of everyday preferences into technical parameters. While large language models have been applied to energy systems as code generators and parameter extractors, no existing implementation deploys LLMs as autonomous coordinators managing the complete workflow from natural language input to multi-appliance scheduling. This paper presents an agentic AI HEMS where LLMs autonomously coordinate multi-appliance scheduling from natural language requests to device control, achieving optimal scheduling without example demonstrations. A hierarchical architecture combining one orchestrator with three specialist agents uses the ReAct pattern for iterative reasoning, enabling dynamic coordination without hardcoded workflows while integrating Google Calendar for context-aware deadline extraction. Evaluation across three open-source models using real Austrian day-ahead electricity prices reveals substantial capability differences. Llama-3.3-70B successfully coordinates all appliances across all scenarios to match cost-optimal benchmarks computed via mixed-integer linear programming, while other models achieve perfect single-appliance performance but struggle to coordinate all appliances simultaneously. Progressive prompt engineering experiments demonstrate that analytical query handling without explicit guidance remains unreliable despite models' general reasoning capabilities. We open-source the complete system including orchestration logic, agent prompts, tools, and web interfaces to enable reproducibility, extension, and future research.

Related papers

• Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management [39.17062930275755]
  This article proposes a multi-scale agentic AI framework for Open Radio Access Networks (O-RAN)<n>It organizes RAN intelligence as a coordinated hierarchy across the Non-Real-Time (Non-RT), Near-Real-Time (Near-RT), and Real-Time (RT) control loops.<n>We show how these agents cooperate through standardized O-RAN interfaces and telemetry.
  arXiv  Detail & Related papers  (2026-02-15T12:34:01Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• RISER: Orchestrating Latent Reasoning Skills for Adaptive Activation Steering [62.63376387138257]
  We propose a plug-and-play intervention framework that adaptively steers large language models (LLMs) reasoning in activation space.<n>RISER constructs a library of reusable reasoning vectors and employs a lightweight Router to dynamically compose them for each input.<n>The Router is optimized via reinforcement learning under task-level rewards, activating latent cognitive primitives in an emergent and compositional manner.
  arXiv  Detail & Related papers  (2026-01-14T08:04:33Z)
• Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges [13.53016942028838]
  Large language models (LLMs) are capable of using natural language to integrate information, follow instructions, and perform forms of "reasoning" and planning.<n>With its multimodal data streams and orchestrated spanning multiple systems, radiology is uniquely suited to benefit from agents that can adapt to context and automate repetitive yet complex tasks.<n>This review examines the design of such LLM agentic systems, highlights key applications, discusses evaluation methods for planning and tool use, and outlines challenges such as error cascades, tool-use efficiency, and health IT integration.
  arXiv  Detail & Related papers  (2025-10-10T13:56:27Z)
• Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models [99.85131798240808]
  We introduce a novel generative framework called textitGuided Topology Diffusion (GTD)<n>Inspired by conditional discrete graph diffusion models, GTD formulates topology synthesis as an iterative construction process.<n>At each step, the generation is steered by a lightweight proxy model that predicts multi-objective rewards.<n>Experiments show that GTD can generate highly task-adaptive, sparse, and efficient communication topologies.
  arXiv  Detail & Related papers  (2025-10-09T05:28:28Z)
• GridMind: LLMs-Powered Agents for Power System Analysis and Operations [3.7568206336846663]
  This paper presents a multi-agent AI system that integrates Large Language Models (LLMs) with deterministic engineering solvers to enable conversational scientific computing for power system analysis.<n>GridMind addresses workflow integration, knowledge accessibility, context preservation, and expert decision-support augmentation.<n>This work establishes agentic AI as a viable paradigm for scientific computing, demonstrating how conversational interfaces can enhance accessibility while preserving numerical rigor essential for critical engineering applications.
  arXiv  Detail & Related papers  (2025-09-02T16:42:18Z)
• ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction [84.90394416593624]
  Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions.<n>Existing simulation-based data generation methods rely heavily on costly autoregressive interactions between multiple agents.<n>We propose a novel Non-Autoregressive Iterative Generation framework, called ToolACE-MT, for constructing high-quality multi-turn agentic dialogues.
  arXiv  Detail & Related papers  (2025-08-18T07:38:23Z)
• State and Memory is All You Need for Robust and Reliable AI Agents [29.259008600842517]
  Large language models (LLMs) have enabled powerful advances in natural language understanding and generation.<n>Yet their application to complex, real-world scientific remain limited by challenges in memory, planning, and tool integration.<n>Here, we introduce SciBORG, a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution.
  arXiv  Detail & Related papers  (2025-06-30T02:02:35Z)
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving [62.62464518137153]
  DriveTransformer is a simplified E2E-AD framework for the ease of scaling up.<n>It is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention.<n>It achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
  arXiv  Detail & Related papers  (2025-03-07T11:41:18Z)
• Large Language Models for Power Scheduling: A User-Centric Approach [6.335540414370735]
  We introduce a novel architecture for resource scheduling problems by converting an arbitrary user's voice request (VRQ) into a resource allocation vector. Specifically, we design an LLM intent recognition agent to translate the request into an optimization problem (OP), an LLM OP parameter identification agent, and an OP solving agent.
  arXiv  Detail & Related papers  (2024-06-29T15:47:28Z)
• TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
  We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation. Specifically, task decomposition, tool selection, and parameter prediction are assessed. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
  arXiv  Detail & Related papers  (2023-11-30T18:02:44Z)

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.