  This paper discusses how usage patterns and preferences of inhabitants can be
learned efficiently to allow smart homes to autonomously achieve energy
savings. We propose a frequent sequential pattern mining algorithm suitable for
real-life smart home event data. The performance of the proposed algorithm is
compared to existing algorithms regarding completeness/correctness of the
results, run times as well as memory consumption and elaborates on the
shortcomings of the different solutions. We also present a recommender system
based on the developed algorithm that provides recommendations to the users to
reduce their energy consumption. The recommender system was deployed to a set
of test homes. The test participants rated the impact of the recommendations on
their comfort. We used this feedback to adjust the system parameters and make
it more accurate during a second test phase.
