Language at the Edge: A Privacy-Preserving Natural-Language Interface for Home Energy Management Systems

Paper in proceeding, 2027

Home Energy Management Systems (HEMS) can improve household energy awareness, efficiency, and flexibility, but many interfaces remain difficult for non-expert users to understand and act upon. Natural-language interaction could lower this barrier, yet cloud-based assistants introduce privacy, cost, and connectivity concerns, while large language models are poorly suited to the resource limits of low-cost edge hardware. This paper presents a privacy-preserving home energy advisory system for Raspberry Pi-class devices that assigns a small language model (SLM) a deliberately bounded role: lightweight non-generative components interpret the query, deterministic functions retrieve and analyze structured energy data, and the SLM renders the grounded result as a concise natural-language answer. This separation preserves auditability, limits hallucination risk, and reduces computational overhead. The system is advisory rather than autonomous: it explains electricity prices, photovoltaic generation, household load status, and suggested operating windows for deferrable appliances without directly controlling devices. We implement a proof-of-concept prototype and define an evaluation centered on routing reliability, runtime feasibility, memory footprint, and the practical value of bounded SLM use in a privacy-preserving edge HEMS setting.