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.
@article{arxiv.1510.00165,
title = {Using consumer behavior data to reduce energy consumption in smart homes},
author = {Daniel Schweizer and Michael Zehnder and Holger Wache and Hans-Friedrich Witschel and Danilo Zanatta and Miguel Rodriguez},
journal= {arXiv preprint arXiv:1510.00165},
year = {2015}
}
Comments
To be presented at IEEE International Conference of Machine Learning and Applications (ICMLA, Dec. 2015). arXiv admin note: text overlap with arXiv:1509.05722