IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5553-d876638.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5553/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/15/5553/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    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.
    Full references (including those not matched with items on IDEAS)

    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. Paulína Šujanová & Monika Rychtáriková & Tiago Sotto Mayor & Affan Hyder, 2019. "A Healthy, Energy-Efficient and Comfortable Indoor Environment, a Review," Energies, MDPI, vol. 12(8), pages 1-37, April.
    2. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    3. Flavio Martins & Maria Fatima Almeida & Rodrigo Calili & Agatha Oliveira, 2020. "Design Thinking Applied to Smart Home Projects: A User-Centric and Sustainable Perspective," Sustainability, MDPI, vol. 12(23), pages 1-27, December.
    4. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    5. Ana Salomé García-Muñiz & María Rosalía Vicente, 2021. "The Effects of Informational Feedback on the Energy Consumption of Online Services: Some Evidence for the European Union," Energies, MDPI, vol. 14(10), pages 1-14, May.
    6. Ye, Yuxiang & Koch, Steven F., 2021. "Measuring energy poverty in South Africa based on household required energy consumption," Energy Economics, Elsevier, vol. 103(C).
    7. Muhammad Sharif & Farzana Naheed Khan, 2023. "Unveiling the Implications of Energy Poverty for Educational Attainments in Pakistan: A Multidimensional Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 472-483, September.
    8. Asmussen, Katherine E. & Mondal, Aupal & Bhat, Chandra R., 2022. "Adoption of partially automated vehicle technology features and impacts on vehicle miles of travel (VMT)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 156-179.
    9. Younjoo Cho & Anseop Choi, 2020. "Application of Affordance Factors for User-Centered Smart Homes: A Case Study Approach," Sustainability, MDPI, vol. 12(7), pages 1-23, April.
    10. Jakob Carlander & Bahram Moshfegh & Jan Akander & Fredrik Karlsson, 2020. "Effects on Energy Demand in an Office Building Considering Location, Orientation, Façade Design and Internal Heat Gains—A Parametric Study," Energies, MDPI, vol. 13(23), pages 1-22, November.
    11. Chen, Chien-fei & Nelson, Hannah & Xu, Xiaojing & Bonilla, Gregory & Jones, Nicholas, 2021. "Beyond technology adoption: Examining home energy management systems, energy burdens and climate change perceptions during COVID-19 pandemic," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    12. Attour, Amel & Baudino, Marco & Krafft, Jackie & Lazaric, Nathalie, 2020. "Determinants of energy tracking application use at the city level: Evidence from France," Energy Policy, Elsevier, vol. 147(C).
    13. Birgul Basarir-Ozel & Hande Bahar Turker & Vesile Aslihan Nasir, 2022. "Identifying the Key Drivers and Barriers of Smart Home Adoption: A Thematic Analysis from the Business Perspective," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    14. Lisardo Prieto González & Anna Fensel & Juan Miguel Gómez Berbís & Angela Popa & Antonio de Amescua Seco, 2021. "A Survey on Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(21), pages 1-16, November.
    15. Qian-Cheng Wang & Yi-Xuan Wang & Izzy Yi Jian & Hsi-Hsien Wei & Xuan Liu & Yao-Tian Ma, 2020. "Exploring the “Energy-Saving Personality Traits” in the Office and Household Situation: An Empirical Study," Energies, MDPI, vol. 13(14), pages 1-17, July.
    16. Xuefeng Li & Han Yang & Jin Jia, 2022. "Impact of energy poverty on cognitive and mental health among middle-aged and older adults in China," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-13, December.
    17. Serap Turkyilmaz & Erkut Altindað, 2022. "Analysis of Smart Home Systems in the Context of the Internet of Things in Terms of Consumer Experience," International Review of Management and Marketing, Econjournals, vol. 12(1), pages 19-31.
    18. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    19. Isabel Andrade & Johann Land & Patricio Gallardo & Susan Krumdieck, 2022. "Application of the InTIME Methodology for the Transition of Office Buildings to Low Carbon—A Case Study," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    20. Sovacool, Benjamin K. & Martiskainen, Mari & Furszyfer Del Rio, Dylan D., 2021. "Knowledge, energy sustainability, and vulnerability in the demographics of smart home technology diffusion," Energy Policy, Elsevier, vol. 153(C).

    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:15:y:2022:i:15:p:5553-:d:876638. 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.