IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i5p1097-d327103.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/5/1097/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/5/1097/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schuelke-Leech, Beth-Anne & Barry, Betsy & Muratori, Matteo & Yurkovich, B.J., 2015. "Big Data issues and opportunities for electric utilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 937-947.
    2. Ngoc Thien Le & Watit Benjapolakul, 2019. "Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques," Energies, MDPI, vol. 12(16), pages 1-13, August.
    3. Shancang Li & Li Da Xu & Shanshan Zhao, 2015. "The internet of things: a survey," Information Systems Frontiers, Springer, vol. 17(2), pages 243-259, April.
    4. Jaesung Park & Taeyeon Kim & Chul-sung Lee, 2019. "Development of Thermal Comfort-Based Controller and Potential Reduction of the Cooling Energy Consumption of a Residential Building in Kuwait," Energies, MDPI, vol. 12(17), pages 1-22, August.
    5. Johannes Thema & Felix Suerkemper & Johan Couder & Nora Mzavanadze & Souran Chatterjee & Jens Teubler & Stefan Thomas & Diana Ürge-Vorsatz & Martin Bo Hansen & Stefan Bouzarovski & Jana Rasch & Sabine, 2019. "The Multiple Benefits of the 2030 EU Energy Efficiency Potential," Energies, MDPI, vol. 12(14), pages 1-19, July.
    6. Andrew Whitmore & Anurag Agarwal & Li Xu, 2015. "The Internet of Things—A survey of topics and trends," Information Systems Frontiers, Springer, vol. 17(2), pages 261-274, April.
    7. Se-Hoon Jung & Jun-Ho Huh, 2019. "A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL," Sustainability, MDPI, vol. 11(13), pages 1-25, June.
    8. Francesco Mancini & Gianluigi Lo Basso & Livio de Santoli, 2019. "Energy Use in Residential Buildings: Impact of Building Automation Control Systems on Energy Performance and Flexibility," Energies, MDPI, vol. 12(15), pages 1-21, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Anna Fensel & Juan Miguel Gómez Berbís, 2021. "Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(8), pages 1-2, April.
    5. 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.
    6. Dasappa, Nirupam Sannagowdara & Kumar G, Krishna & Somu, Nivethitha, 2024. "Multi-sensor data fusion framework for energy optimization in smart homes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    7. 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.
    8. 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).
    9. 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.
    10. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arfi, Wissal Ben & Nasr, Imed Ben & Kondrateva, Galina & Hikkerova, Lubica, 2021. "The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    2. Hong Jiang & Shuyu Sun & Hongtao Xu & Shukuan Zhao & Yong Chen, 2020. "Enterprises' network structure and their technology standardization capability in Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 749-765, July.
    3. Chae, Bongsug (Kevin), 2018. "The Internet of Things (IoT): A Survey of Topics and Trends using Twitter Data and Topic Modeling," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190376, International Telecommunications Society (ITS).
    4. Qinghua Zheng & Chutong Yang & Haijun Yang & Jianhe Zhou, 2020. "A Fast Exact Algorithm for Deployment of Sensor Nodes for Internet of Things," Information Systems Frontiers, Springer, vol. 22(4), pages 829-842, August.
    5. Damminda Alahakoon & Rashmika Nawaratne & Yan Xu & Daswin Silva & Uthayasankar Sivarajah & Bhumika Gupta, 2023. "Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities," Information Systems Frontiers, Springer, vol. 25(1), pages 221-240, February.
    6. Peter M. Bednar & Christine Welch, 0. "Socio-Technical Perspectives on Smart Working: Creating Meaningful and Sustainable Systems," Information Systems Frontiers, Springer, vol. 0, pages 1-18.
    7. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    8. Payam Hanafizadeh & Parastou Hatami & Morteza Analoui & Amir Albadvi, 2021. "Business model innovation driven by the internet of things technology, in internet service providers’ business context," Information Systems and e-Business Management, Springer, vol. 19(4), pages 1175-1243, December.
    9. Federica Cena & Luca Console & Assunta Matassa & Ilaria Torre, 2019. "Multi-dimensional intelligence in smart physical objects," Information Systems Frontiers, Springer, vol. 21(2), pages 383-404, April.
    10. Oscar Brousse & Charles H. Simpson & Ate Poorthuis & Clare Heaviside, 2024. "Unequal distributions of crowdsourced weather data in England and Wales," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    11. Pan Wang & Ricardo Valerdi & Shangming Zhou & Ling Li, 2015. "Introduction: Advances in IoT research and applications," Information Systems Frontiers, Springer, vol. 17(2), pages 239-241, April.
    12. Shang, Juan & Li, Pengfei & Li, Ling & Chen, Yong, 2018. "The relationship between population growth and capital allocation in urbanization," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 249-256.
    13. Jens Passlick & Sonja Dreyer & Daniel Olivotti & Lukas Grützner & Dennis Eilers & Michael H. Breitner, 2021. "Predictive maintenance as an internet of things enabled business model: A taxonomy," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 67-87, March.
    14. Salvatore T. March & Gary D. Scudder, 2019. "Predictive maintenance: strategic use of IT in manufacturing organizations," Information Systems Frontiers, Springer, vol. 21(2), pages 327-341, April.
    15. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    16. Tussyadiah, Iis, 2020. "A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism," Annals of Tourism Research, Elsevier, vol. 81(C).
    17. Takano, Yasutomo & Kajikawa, Yuya, 2019. "Extracting commercialization opportunities of the Internet of Things: Measuring text similarity between papers and patents," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 45-68.
    18. Dameri, Renata Paola & Benevolo, Clara & Veglianti, Eleonora & Li, Yaya, 2019. "Understanding smart cities as a glocal strategy: A comparison between Italy and China," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 26-41.
    19. Federica Cena & Silvia Likavec & Amon Rapp, 2019. "Real World User Model: Evolution of User Modeling Triggered by Advances in Wearable and Ubiquitous Computing," Information Systems Frontiers, Springer, vol. 21(5), pages 1085-1110, October.
    20. Muhammad Tahir & Muhammad Sardaraz & Shakoor Muhammad & Muhammad Saud Khan, 2020. "A Lightweight Authentication and Authorization Framework for Blockchain-Enabled IoT Network in Health-Informatics," Sustainability, MDPI, vol. 12(17), pages 1-23, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1097-:d:327103. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.