Human in the loop preference calibration for home energy manage 2027 Applied

As residential energy systems become increasingly electrified and coordination-intensive, home energy management systems (HEMS) must operate economically while remaining aligned with evolving user-authorized comfort and service boundaries. Existing personalization approaches rely on questionnaire initialization, fixed preference parameters, or indirect behavioral corrections, limiting alignment under changing needs and the use of runtime natural-language feedback for long-horizon calibration. This paper proposes a large language model (LLM)-enabled human-in-the-loop framework that converts user utterances into authorization-aware, contract-constrained, machine-checkable events for downstream control and preference updating. The LLM is a contract-constrained semantic interface, not a direct controller. Explicit intents support bounded execution and direct profile correction, whereas vague feedback requires option negotiation and user confirmation before bounded updating. A Contract-Informed Preference Update (CIPU) mechanism combines structured interaction evidence with projection and trust-region stabilization, while model predictive control remains the safety-constrained execution layer. In simulations, the proposed method reduces normalized preference distance by 22.50% and complaints by 74.46% relative to Survey MPC across three role-conditioned profiles.