  This paper presents a case study of a recommender system that can be used to
save energy in smart homes without lowering the comfort of the inhabitants. We
present an algorithm that uses consumer behavior data only and uses machine
learning to suggest actions for inhabitants to reduce the energy consumption of
their homes. The system mines for frequent and periodic patterns in the event
data provided by the Digitalstrom home automation system. These patterns are
converted into association rules, prioritized and compared with the current
behavior of the inhabitants. If the system detects an opportunities to save
energy without decreasing the comfort level it sends a recommendation to the
residents.
