The Next Generation of Smart Home Energy Management
Rui Li – EcoFlow
Diverse user objectives and increasingly complex residential energy scenarios demand Smart Home Energy Management Systems (SHEMS) that are efficient, reliable, explainable, and personalized. This work shows how large language models (LLMs) enable the next generation of smart home energy management. We propose an agentic HEMS architecture in which LLMs act as a coordinated trinity of retriever, generator, and interpreter, supporting natural interaction, automated problem formulation, and flexible reasoning. The architecture adapts to evolving user objectives and expanding household energy ecosystems across the residential energy lifecycle. Real‑world evaluations demonstrate sustained cost reductions alongside more proactive, highly personalized, and user‑centric energy management experiences.
