User behavior prediction for energy management in smart homes

In this paper, we focus on the prediction of user interactions within a real world scenario of energy management for a smart home. External signals, reflecting the low voltage grid's state, are used to address the challenge of balancing energy demand and generation. An autonomous system to aim at this challenge is proposed, in particular to coordinate decentralized power plants with the electrical load of the smart home. For that two prediction algorithms to estimate the future behavior of the smart home are presented: The Day Type Model and a probabilistic approach based on a first order Semi Markov Model. Some experimental results with real world data of the KIT smart home are presented.