Using consumer behavior data to reduce energy consumption in smart homes

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Authors
Schweizer, Daniel
Zehnder, Michael
Zanatta, Danilo
Rodriguez, Miguel
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Publication date
2015
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04B - Conference paper
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Proceedings of the IEEE 2015 International Conference on Machine Learning and Applications
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GuangZhou
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Abstract
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.
Keywords
smart cities, smar homes, energy saving, recommender systems, association rules, unsupervised learning, internet of things
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English
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Yes
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Published
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SCHWEIZER, Daniel, Michael ZEHNDER, Holger WACHE, Danilo ZANATTA, Miguel RODRIGUEZ und Hans Friedrich WITSCHEL, 2015. Using consumer behavior data to reduce energy consumption in smart homes. In: Proceedings of the IEEE 2015 International Conference on Machine Learning and Applications. GuangZhou. 2015. Verfügbar unter: https://doi.org/10.26041/fhnw-1004