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Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

Author

Listed:
  • Eric Hitimana

    (African Center of Excellence in the Internet of Things, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

  • Gaurav Bajpai

    (Department of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

  • Richard Musabe

    (Department of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

  • Louis Sibomana

    (National Council for Science and Technology, Kigali P.O. Box 2285, Rwanda)

  • Jayavel Kayalvizhi

    (Department of Information Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, India)

Abstract

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Suggested Citation

  • Eric Hitimana & Gaurav Bajpai & Richard Musabe & Louis Sibomana & Jayavel Kayalvizhi, 2021. "Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building," Future Internet, MDPI, vol. 13(3), pages 1-19, March.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:3:p:67-:d:513811
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    References listed on IDEAS

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    1. Giulia Bettin & Alberto Zazzaro, 2018. "The Impact of Natural Disasters on Remittances to Low- and Middle-Income Countries," Journal of Development Studies, Taylor & Francis Journals, vol. 54(3), pages 481-500, March.
    2. Diana Martinez-Mosquera & Rosa Navarrete & Sergio Lujan-Mora, 2020. "Modeling and Management Big Data in Databases—A Systematic Literature Review," Sustainability, MDPI, vol. 12(2), pages 1-41, January.
    3. Thanongsak Xayasouk & HwaMin Lee & Giyeol Lee, 2020. "Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    4. Imran & Shabir Ahmad & DoHyeun Kim, 2019. "Design and Implementation of Thermal Comfort System based on Tasks Allocation Mechanism in Smart Homes," Sustainability, MDPI, vol. 11(20), pages 1-24, October.
    5. Eunjeong Choi & Soohwan Cho & Dong Keun Kim, 2020. "Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability," Sustainability, MDPI, vol. 12(3), pages 1-14, February.
    6. Ping Liu & Jin Wang & Arun Kumar Sangaiah & Yang Xie & Xinchun Yin, 2019. "Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
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    Cited by:

    1. Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    2. Angelique Mukasine & Louis Sibomana & Kayalvizhi Jayavel & Kizito Nkurikiyeyezu & Eric Hitimana, 2023. "Correlation Analysis Model of Environment Parameters Using IoT Framework in a Biogas Energy Generation Context," Future Internet, MDPI, vol. 15(8), pages 1-14, August.
    3. Diego Lopez-Bernal & David Balderas & Pedro Ponce & Arturo Molina, 2021. "Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems," Future Internet, MDPI, vol. 13(8), pages 1-14, July.
    4. Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

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