Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling
Accelerate Your Energy Transition with Intelligent Automation
This paper introduces an agentic AI Home Energy Management System (HEMS) that utilizes Large Language Models (LLMs) to autonomously coordinate multi-appliance scheduling from natural language requests to device control. It features a hierarchical multi-agent architecture (orchestrator + specialists) and a ReAct pattern for iterative reasoning, integrating external APIs like Google Calendar and ENTSO-E for context. Evaluation across Llama-3.3-70B, Qwen-3-32B, and GPT-OSS-120B shows Llama-3.3-70B achieves 100% cost-optimal scheduling for multiple appliances, outperforming others. The system addresses user interaction barriers in HEMS adoption by enabling natural language-based scheduling without technical parameter specification. All components are open-source.
Executive Impact: Proven Performance & Savings
See how agentic AI delivers tangible results in residential energy management, driving efficiency and optimizing costs.
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Llama-3.3-70B Optimality
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Multi-Appliance Scheduling Time
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Token Efficiency (Llama-3.3-70B)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Agentic AI Architecture
The system employs a hierarchical multi-agent architecture with one orchestrator agent and three specialist agents, all powered by the same LLM, separating strategic coordination from domain-specific optimization.
ReAct-based Orchestration
The orchestrator agent uses the ReAct pattern for iterative decision-making, reasoning about system state, deciding on actions, executing them through tools, observing outcomes, and adapting its strategy dynamically.
Scheduling Performance
Evaluation shows Llama-3.3-70B achieving 100% optimality for single and multi-appliance scheduling, outperforming other models which struggle with complex multi-appliance coordination.
Analytical Query Handling
The system can perform direct analytical queries using a 'calculate_window_sums' tool, but consistent success requires explicit workflow guidance through progressive prompt engineering.
Deployment & Sustainability
Deployment considerations include model selection, prompt engineering, and the sustainability trade-off of LLM inference's energy footprint. Strategies like model distillation and edge deployment are discussed.
Security & Robustness
A multi-layer defense strategy prevents prompt injection attacks through pre-LLM validation, pattern-based detection, and privilege separation via XML wrapping, ensuring system integrity and cost efficiency.
100%
Optimal Scheduling Success Rate with Llama-3.3-70B
Enterprise Process Flow
User Request (Natural Language)
→
Orchestrator Agent (ReAct Loop)
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Fetch API Data (Prices, Calendar)
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Delegate to Specialist Agents
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Specialist Agents Optimize (WM, DW, EV)
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Aggregate Recommendations
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Execute Schedules to Devices
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User Summary
| Model | Success Rate | Avg Execution Time | Key Learnings |
|---|---|---|---|
| Llama-3.3-70B | 100% | 14.7s |
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| Qwen-3-32B | 20% | 31.4s |
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| GPT-OSS-120B | 0% | 16.3s |
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| Traditional HEMS | N/A | Milliseconds |
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Context-Aware Deadline Inference for EV Charging
The EV Charger Agent optimizes 6-hour charging sessions with calendar-driven deadline awareness. Unlike fixed-schedule appliances, it integrates constraints inferred from Google Calendar events. For instance, an event titled 'Working Hours - in Office' scheduled Monday-Friday 8:00 AM to 6:00 PM allows the orchestrator to infer EV charging deadlines based on work schedules without explicit user instruction. This demonstrates how the system can dynamically adapt scheduling to user's real-world commitments, significantly enhancing user convenience and reducing manual input.
Calculate Your AI-Driven Energy Savings
Estimate the potential annual cost savings and operational efficiency gains for your enterprise by adopting an Agentic AI HEMS.
Implementation Roadmap
Our proven roadmap guides you from initial strategy to full-scale deployment.
Discovery & Strategy
Assess current energy consumption, define objectives, and tailor AI HEMS integration strategy.
System Integration
Integrate Agentic AI HEMS with smart home devices, energy meters, and external APIs.
Pilot Deployment & Optimization
Deploy in a controlled environment, gather feedback, and fine-tune algorithms for peak performance.
Scale & Monitoring
Expand to full operational scale, implement continuous monitoring, and ensure ongoing efficiency.
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