LLM-based AI Agents for Smart Building Energy Management
Published:Dec 31, 2025 18:51
•1 min read
•ArXiv
Analysis
This paper introduces a novel framework for using LLMs to create context-aware AI agents for building energy management. It addresses limitations in existing systems by leveraging LLMs for natural language interaction, data analysis, and intelligent control of appliances. The prototype evaluation using real-world datasets and various metrics provides a valuable benchmark for future research in this area. The focus on user interaction and context-awareness is particularly important for improving energy efficiency and user experience in smart buildings.
Key Takeaways
- •Proposes a context-aware LLM-based AI agent for smart building energy management.
- •Framework includes perception, central control, and action modules.
- •Evaluated using real-world residential energy datasets.
- •Demonstrates promising performance in device control, memory tasks, scheduling, and energy analysis.
- •Identifies areas for improvement in cost estimation tasks.
Reference
“The results revealed promising performance, measured by response accuracy in device control (86%), memory-related tasks (97%), scheduling and automation (74%), and energy analysis (77%), while more complex cost estimation tasks highlighted areas for improvement with an accuracy of 49%.”