Energy saving in smart homes based on consumer behaviour data

This paper discusses how energy can be saved in smart homes without lowering the comfort of the inhabitants, based on consumer behaviour data only. A recommender system was designed, that suggests actions for inhabitants without the necessity for installing additional devices, executing manual configuration or having any other interaction with the system.As a consequence of the devastating earthquake and the resulting nuclear disaster that struck Fukushima in March 2011, concerned members of the public and the government agreed on a major reconsideration of the energy policy. However, such a radical rethinking can only be achieved if private households increase their efforts to save energy. Nevertheless, most research approaches conducted in smart homes in the past years, dealt with convenience rather than with sustainability. The aim of this master thesis is to find a way to save energy without causing significant inconveniences for the consumer. Therefore, the following hypothesis was formulated: “It is possible to design a recommender system that can suggest actions in smart homes based on consumer behaviour, which will lower energy usage but not decrease comfort levels”.The approach followed in this paper, is to mine frequent (and/or periodic) patterns in the event data of the inhabitants electricity usages, recorded by a smart home automation system. These patterns are converted into association rules, prioritized and compared with the current behaviour of the inhabitants. If the system detects opportunities to save energy without decreasing the comfort level, it will send a recommendation to the residents....