IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i16p4320-d256363.html
   My bibliography  Save this article

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

Author

Listed:
  • Diego Rodríguez-Gracia

    (Ministry of Education and Vocational Training, the Andalusian Regional Government, 04008 Almería, Spain)

  • José A. Piedra-Fernández

    (Applied Computing Group, University of Almería, 04120 Almería, Spain)

  • Luis Iribarne

    (Applied Computing Group, University of Almería, 04120 Almería, Spain)

  • Javier Criado

    (Applied Computing Group, University of Almería, 04120 Almería, Spain)

  • Rosa Ayala

    (Applied Computing Group, University of Almería, 04120 Almería, Spain)

  • Joaquín Alonso-Montesinos

    (Solar Energy Research Centre (CIESOL), University of Almeria, 04120 Almería, Spain)

  • Capobianco-Uriarte Maria de las Mercedes

    (Economy and Business Department, University of Almería, 04120 Almería, Spain)

Abstract

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings.

Suggested Citation

  • Diego Rodríguez-Gracia & José A. Piedra-Fernández & Luis Iribarne & Javier Criado & Rosa Ayala & Joaquín Alonso-Montesinos & Capobianco-Uriarte Maria de las Mercedes, 2019. "Microservices and Machine Learning Algorithms for Adaptive Green Buildings," Sustainability, MDPI, vol. 11(16), pages 1-22, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4320-:d:256363
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/16/4320/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/16/4320/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dounis, A.I. & Caraiscos, C., 2009. "Advanced control systems engineering for energy and comfort management in a building environment--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1246-1261, August.
    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. Muhammad Fayaz & DoHyeun Kim, 2018. "Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic," Energies, MDPI, vol. 11(1), pages 1-22, January.
    2. Murat Kunelbayev & Yedilkhan Amirgaliyev & Talgat Sundetov, 2022. "Improving the Efficiency of Environmental Temperature Control in Homes and Buildings," Energies, MDPI, vol. 15(23), pages 1-15, November.
    3. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    4. Shunling Ruan & Haiyan Xie & Song Jiang, 2017. "Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization," Sustainability, MDPI, vol. 9(9), pages 1-22, September.
    5. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    6. De Lorenzi, Andrea & Gambarotta, Agostino & Morini, Mirko & Rossi, Michele & Saletti, Costanza, 2020. "Setup and testing of smart controllers for small-scale district heating networks: An integrated framework," Energy, Elsevier, vol. 205(C).
    7. Manzano-Agugliaro, Francisco & Montoya, Francisco G. & Sabio-Ortega, Andrés & García-Cruz, Amós, 2015. "Review of bioclimatic architecture strategies for achieving thermal comfort," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 736-755.
    8. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    9. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    10. 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.
    11. Altwies, Joy E. & Nemet, Gregory F., 2013. "Innovation in the U.S. building sector: An assessment of patent citations in building energy control technology," Energy Policy, Elsevier, vol. 52(C), pages 819-831.
    12. Juan-Carlos Fraile & Julio San-José & Ana González-Alonso, 2014. "A Boiler Room in a 600-Bed Hospital Complex: Study, Analysis, and Implementation of Energy Efficiency Improvements," Energies, MDPI, vol. 7(5), pages 1-22, May.
    13. Jiang, Lai & Yao, Runming & Liu, Kecheng & McCrindle, Rachel, 2017. "An Epistemic-Deontic-Axiologic (EDA) agent-based energy management system in office buildings," Applied Energy, Elsevier, vol. 205(C), pages 440-452.
    14. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    15. Wang, Zhu & Wang, Lingfeng & Dounis, Anastasios I. & Yang, Rui, 2012. "Multi-agent control system with information fusion based comfort model for smart buildings," Applied Energy, Elsevier, vol. 99(C), pages 247-254.
    16. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    17. Ghadi, Yazeed Yasin & Rasul, M.G. & Khan, M.M.K., 2016. "Design and development of advanced fuzzy logic controllers in smart buildings for institutional buildings in subtropical Queensland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 738-744.
    18. Byung-Ki Jeon & Eui-Jong Kim, 2021. "LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    19. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    20. Peeraya Inyim & Mostafa Batouli & Maria Presa Reyes & Triana Carmenate & Leonardo Bobadilla & Ali Mostafavi, 2018. "A Smartphone Application for Personalized and Multi-Method Interventions toward Energy Saving in Buildings," Sustainability, MDPI, vol. 10(6), pages 1-19, May.

    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:jsusta:v:11:y:2019:i:16:p:4320-:d:256363. 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.