Using consumer behavior data to reduce energy consumption in smart homes
This work addresses energy efficiency for smart home users, but it is incremental as it builds on existing pattern mining methods.
The paper tackled reducing energy consumption in smart homes by learning usage patterns and preferences, proposing a frequent sequential pattern mining algorithm and a recommender system that was tested in homes, with participants providing feedback to improve accuracy.
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.