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Deep learning model for home automation and energy reduction in a smart home environment platform

  • S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing
  • Published:
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Abstract

The target of smart houses and enhanced living environments is to increase the quality of life further. In this context, more supporting platforms for smart houses were developed, some of them using cloud systems for remote supervision, control and data storage. An important aspect, which is an open issue for both industry and academia, is represented by how to reduce and estimate energy consumption for a smart house. In this paper, we propose a modular platform that uses the power of cloud services to collect, aggregate and store all the data gathered from the smart environment. Then, we use the data to generate advanced neural network models to create energy awareness by advising the smart environment occupants on how they can improve daily habits while reducing the energy consumption and thus also the costs.

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Acknowledgements

We would like to thank the reviewers for their time and expertise, constructive comments and valuable insight.

Funding

The research presented in this paper is supported by the following projects: ROBIN (PN-III-P1-1.2-PCCDI-2017-0734), NETIO TEL-MONAER and ForestMon (53/05.09.2016, SMIS2014+ 105976), SPERO (PN-III-P2-2.1-SOL-2016-03-0046, 3Sol/2017), ARUT 2018 Project "Decentralized Storage System for Edge Computing—StorEdge" and PN 18 19 02 01: Systems and applications based on convergence between Big Data, intelligent data analysis and advanced machine learning, Contract 9N/16.03.2018.

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Authors and Affiliations

  1. Computer Science Department, University Politehnica of Bucharest, Bucharest, Romania

    Dan Popa & Florin Pop

  2. National Institute for Research and Development in Informatics (ICI), Bucharest, Romania

    Florin Pop

  3. Department of Mathematical Methods and Models, University Politehnica of Bucharest, Bucharest, Romania

    Cristina Serbanescu

  4. Department of Computer Science, University of Salerno, Fisciano, SA, Italy

    Aniello Castiglione

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  1. Dan Popa
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  2. Florin Pop
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Correspondence to Florin Pop.

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This article does not contain any studies with human participants performed by any of the authors.

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Popa, D., Pop, F., Serbanescu, C. et al. Deep learning model for home automation and energy reduction in a smart home environment platform. Neural Comput & Applic 31, 1317–1337 (2019). https://doi.org/10.1007/s00521-018-3724-6

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  • DOI: https://doi.org/10.1007/s00521-018-3724-6

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