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Energy Management System Based on a Gamified Application for Households

Author

Listed:
  • Manuel Avila

    (School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico
    These authors contributed equally to this work.)

  • Juana Isabel Méndez

    (School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico
    These authors contributed equally to this work.)

  • Pedro Ponce

    (School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico
    These authors contributed equally to this work.)

  • Therese Peffer

    (Institute for Energy and Environment, University of California, Berkeley, CA 94720, USA
    These authors contributed equally to this work.)

  • Alan Meier

    (Energy and Efficiency Institute, University of California, Davis, CA 95616, USA
    These authors contributed equally to this work.)

  • Arturo Molina

    (School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico
    These authors contributed equally to this work.)

Abstract

Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users’ patterns, goals, and needs, by creating energy consumption awareness and helping current change habits. In this way, energy manager systems (EMSs) monitor and manage electrical appliances, automate and schedule actions, and make suggestions regarding electrical consumption. Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money. Therefore, this paper proposes a personalized gamification strategy for an EMS through an adaptive neuro-fuzzy inference system (ANFIS) decision-making engine to classify the level of electrical consumption and persuade the end-user to reduce and modify consumption patterns, saving energy and money with gamified motivations. These strategies have proven to be effective in changing consumer behavior with intrinsic and extrinsic motivations. The interfaces consider three cases for summer and winter periods to calculate the saving-energy potentials: (1) for a type of user that is interested in home-improvement efforts while helping to save energy; (2) for a type of user that is advocating to save energy; (3) for a type of user that is not interested in saving energy. Hence, each interface considers the end-user’s current consumption and the possibility to modify their consumption habits using their current electrical devices. Finally, an interface displaying the electrical consumption for each case exemplifies its linkage with EMSs.

Suggested Citation

  • Manuel Avila & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2021. "Energy Management System Based on a Gamified Application for Households," Energies, MDPI, vol. 14(12), pages 1-27, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3445-:d:572845
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    References listed on IDEAS

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    1. Spartaco Albertarelli & Piero Fraternali & Sergio Herrera & Mark Melenhorst & Jasminko Novak & Chiara Pasini & Andrea-Emilio Rizzoli & Cristina Rottondi, 2018. "A Survey on the Design of Gamified Systems for Energy and Water Sustainability," Games, MDPI, vol. 9(3), pages 1-34, June.
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    7. Roberto Chiosa & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings," Energies, MDPI, vol. 14(1), pages 1-28, January.
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    Cited by:

    1. Adán Medina & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Arturo Molina, 2022. "Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats," Energies, MDPI, vol. 15(17), pages 1-16, August.
    2. Adán Medina & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Using Deep Learning in Real-Time for Clothing Classification with Connected Thermostats," Energies, MDPI, vol. 15(5), pages 1-28, March.
    3. Juana Isabel Méndez & Adán Medina & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces," Energies, MDPI, vol. 15(15), pages 1-29, July.
    4. Omar Mata & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2023. "Energy Savings in Buildings Based on Image Depth Sensors for Human Activity Recognition," Energies, MDPI, vol. 16(3), pages 1-22, January.
    5. Rima Aridi & Jalal Faraj & Samer Ali & Mostafa Gad El-Rab & Thierry Lemenand & Mahmoud Khaled, 2021. "Energy Recovery in Air Conditioning Systems: Comprehensive Review, Classifications, Critical Analysis, and Potential Recommendations," Energies, MDPI, vol. 14(18), pages 1-31, September.

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