  Smart home automation systems aim to improve the comfort and convenience of
users in their living environment. However, adapting automation to user needs
remains a challenge. Indeed, many systems still rely on hand-crafted routines
for each smart object.This paper presents an original smart home architecture
leveraging Large Language Models (LLMs) and user preferences to push the
boundaries of personalisation and intuitiveness in the home environment.This
article explores a human-centred approach that uses the general knowledge
provided by LLMs to learn and facilitate interactions with the environment.The
advantages of the proposed model are demonstrated on a set of scenarios, as
well as a comparative analysis with various LLM implementations. Some metrics
are assessed to determine the system's ability to maintain comfort, safety, and
user preferences. The paper details the approach to real-world implementation
and evaluation.The proposed approach of using preferences shows up to 52.3%
increase in average grade, and with an average processing time reduced by 35.6%
on Starling 7B Alpha LLM. In addition, performance is 26.4% better than the
results of the larger models without preferences, with processing time almost
20 times faster.
