# Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings arXiv: https://arxiv.org/abs/2512.25055 ยท HTML: https://mlsanity.com/paper/2512.25055/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. - Builds on 27 of its 79 references that are in the map. - First posted on arXiv 31 Dec 2025. ## Placed in - LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/) ## Builds on (most cited first, 27 of 27) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023) - [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020) - [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024) - [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022) - [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023) - [Large Language Model Guided Tree-of-Thought](https://mlsanity.com/paper/2305.08291/) (2023) - [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023) - [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023) - [Emergent autonomous scientific research capabilities of large language models](https://mlsanity.com/paper/2304.05332/) (2023) - [Demystifying Chains, Trees, and Graphs of Thoughts](https://mlsanity.com/paper/2401.14295/) (2024) - [An In-depth Survey of Large Language Model-based Artificial Intelligence Agents](https://mlsanity.com/paper/2309.14365/) (2023) - [The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents](https://mlsanity.com/paper/2307.07871/) (2023) - [GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation](https://mlsanity.com/paper/2305.18997/) (2023) - [Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks](https://mlsanity.com/paper/2307.10337/) (2023) - [SAGE: Smart home Agent with Grounded Execution](https://mlsanity.com/paper/2311.00772/) (2023) - [ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2407.05365/) (2024) - [BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrieval](https://mlsanity.com/paper/2304.09333/) (2023) - ["Get ready for a party": Exploring smarter smart spaces with help from large language models](https://mlsanity.com/paper/2303.14143/) (2023) - [LiveMind: Low-latency Large Language Models with Simultaneous Inference](https://mlsanity.com/paper/2406.14319/) (2024) - [Large Language Model Interface for Home Energy Management Systems](https://mlsanity.com/paper/2501.07919/) (2025) - [Performance Study of Time Series Databases](https://mlsanity.com/paper/2208.13982/) (2022) - [A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability](https://mlsanity.com/paper/2308.10380/) (2023) ## Built on by (most cited first, 4 of 4) - [Multi-Agent Home Energy Management Assistant](https://mlsanity.com/paper/2602.15219/) (2026) - [Human-AI Collaboration in Large Language Model-Integrated Building Energy Management Systems: The Role of User Domain Knowledge and AI Literacy](https://mlsanity.com/paper/2602.16140/) (2026) - [Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI](https://mlsanity.com/paper/2603.06217/) (2026) - [BuildOcc: A Large Language Model Occupant Agent Platform for Building Energy Research](https://mlsanity.com/paper/2609.02729/) (2026) ## Abstract (arXiv) This study presents a conceptual framework and a prototype assessment for Large Language Model (LLM)-based Building Energy Management System (BEMS) AI agents to facilitate context-aware energy management in smart buildings through natural language interaction. The proposed framework comprises three modules: perception (sensing), central control (brain), and action (actuation and user interaction), forming a closed feedback loop that captures, analyzes, and interprets energy data to respond intelligently to user queries and manage connected appliances. By leveraging the autonomous data analytics capabilities of LLMs, the BEMS AI agent seeks to offer context-aware insights into energy consumption, cost prediction, and device scheduling, thereby addressing limitations in existing energy management systems. The prototype's performance was evaluated using 120 user queries across four distinct real-world residential energy datasets and different evaluation metrics, including latency, functionality, capability, accuracy, and cost-effectiveness. The generalizability of the framework was demonstrated using ANOVA tests. 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%. This benchmarking study moves toward formalizing the assessment of LLM-based BEMS AI agents and identifying future research directions, emphasizing the trade-off between response accuracy and computational efficiency.