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Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces

Author

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  • Juana Isabel Méndez

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, Mexico)

  • Adán Medina

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, Mexico)

  • Pedro Ponce

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, Mexico)

  • Therese Peffer

    (Institute for Energy and Environment, University of California, Berkeley, CA 94720, USA)

  • Alan Meier

    (Energy and Efficiency Institute, University of California, Davis, CA 95616, USA)

  • Arturo Molina

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, Mexico)

Abstract

In 2021, the residential sector had an electricity consumption of around 39% in México. Householders influence the quantity of energy they manage in a home due to their preferences, culture, and economy. Hence, profiling the householders’ behavior in communities allows designers or engineers to build strategies that promote energy reductions. The household socially connected products ease routine tasks and help profile the householder. Furthermore, gamification strategies model householders’ habits by enhancing services through ludic experiences. Therefore, a gamified smart community concept emerged during this research as an understanding that this type of community does not need a physical location but has similar characteristics. Thus, this paper proposes a three-step framework to tailor interfaces. During the first step, the householder type and consumption level were analyzed using available online databases for Mexico. Then, two artificial neural networks were built, trained, and deployed during the second step to tailor an interactive interface. Thus, the third step deploys an interactive and tailored dashboard. Moreover, the research analysis reflected the predominant personality traits. Besides, some locations have more electricity consumption than others associated with the relative humidity, the outdoor temperature, or the poverty level. The interactive dashboard provides insights about the game elements needed depending on the personality traits, location, and electricity bill. Therefore, this proposal considers all householders (typical and non-typical users) to deploy tailored interfaces designed for smart communities. Currently, the game elements proposed during this research are reported by the literature, so their adoption is assured.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5553-:d:876638
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    References listed on IDEAS

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    1. Marikyan, Davit & Papagiannidis, Savvas & Alamanos, Eleftherios, 2019. "A systematic review of the smart home literature: A user perspective," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 139-154.
    2. Lei Qi & Jing Guo, 2019. "Development of smart city community service integrated management platform," International Journal of Distributed Sensor Networks, , vol. 15(6), pages 15501477198, June.
    3. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    4. Antonio Paone & Jean-Philippe Bacher, 2018. "The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art," Energies, MDPI, vol. 11(4), pages 1-19, April.
    5. Chatzigeorgiou, I.M. & Andreou, G.T., 2021. "A systematic review on feedback research for residential energy behavior change through mobile and web interfaces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Ponce, Pedro & Peffer, Therese & Molina, Arturo & Barcena, Sergio, 2020. "Social creation networks for designing low income interfaces in programmable thermostats," Technology in Society, Elsevier, vol. 62(C).
    7. Dominic J. Bednar & Tony G. Reames, 2020. "Recognition of and response to energy poverty in the United States," Nature Energy, Nature, vol. 5(6), pages 432-439, June.
    8. Chen, Sherry Y. & Macredie, Robert D., 2005. "The assessment of usability of electronic shopping: A heuristic evaluation," International Journal of Information Management, Elsevier, vol. 25(6), pages 516-532.
    9. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    10. 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.
    11. 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.
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