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HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Author

Listed:
  • Isaac Machorro-Cano

    (Tecnológico Nacional de México (I. T. Orizaba), Av. Oriente 9, 852. Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

  • Giner Alor-Hernández

    (Tecnológico Nacional de México (I. T. Orizaba), Av. Oriente 9, 852. Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

  • Mario Andrés Paredes-Valverde

    (Tecnológico Nacional de México (I. T. Orizaba), Av. Oriente 9, 852. Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

  • Lisbeth Rodríguez-Mazahua

    (Tecnológico Nacional de México (I. T. Orizaba), Av. Oriente 9, 852. Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

  • José Luis Sánchez-Cervantes

    (CONACYT-Tecnológico Nacional de México (I. T. Orizaba), Av. Oriente 9,852. Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

  • José Oscar Olmedo-Aguirre

    (Department of Electrical Engineering, CINVESTAV-IPN, Av. Instituto Politécnico Nacional 2,508, Col. San Pedro Zacatenco, Delegación Gustavo A. Madero, Mexico City 07360, Mexico)

Abstract

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

Suggested Citation

  • Isaac Machorro-Cano & Giner Alor-Hernández & Mario Andrés Paredes-Valverde & Lisbeth Rodríguez-Mazahua & José Luis Sánchez-Cervantes & José Oscar Olmedo-Aguirre, 2020. "HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving," Energies, MDPI, vol. 13(5), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1097-:d:327103
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    References listed on IDEAS

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    Cited by:

    1. Anna Fensel & Juan Miguel Gómez Berbís, 2021. "Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(8), pages 1-2, April.
    2. Yi Sun & Shihui Li, 2021. "A systematic review of the research framework and evolution of smart homes based on the internet of things," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(3), pages 597-623, July.
    3. Natarajan, Anisha & Krishnasamy, Vijayakumar & Singh, Munesh, 2022. "Occupancy detection and localization strategies for demand modulated appliance control in Internet of Things enabled home energy management system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Ahmed Saad & Samy Faddel & Osama Mohammed, 2020. "IoT-Based Digital Twin for Energy Cyber-Physical Systems: Design and Implementation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    5. Liu, Lucy & Workman, Mark & Hayes, Sarah, 2022. "Net Zero and the potential of consumer data - United Kingdom energy sector case study: The need for cross-sectoral best data practice principles," Energy Policy, Elsevier, vol. 163(C).
    6. Wadim Strielkowski & Olga Kovaleva & Tatiana Efimtseva, 2022. "Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    7. Josimar Reyes-Campos & Giner Alor-Hernández & Isaac Machorro-Cano & José Oscar Olmedo-Aguirre & José Luis Sánchez-Cervantes & Lisbeth Rodríguez-Mazahua, 2021. "Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm," Mathematics, MDPI, vol. 9(3), pages 1-25, January.
    8. Dumiter Florin Cornel & Turcaș Florin Marius & Boiţă Marius, 2023. "Oil Shock Impact Upon Energy Companies Investment Portfolios. Trends and Evolutions in the Energy Consumption Sector," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 33(1), pages 1-27, March.
    9. Christian Pfeiffer & Markus Puchegger & Claudia Maier & Ina V. Tomaschitz & Thomas P. Kremsner & Lukas Gnam, 2020. "A Case Study of Socially-Accepted Potentials for the Use of End User Flexibility by Home Energy Management Systems," Sustainability, MDPI, vol. 13(1), pages 1-19, December.

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