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Microservices and Machine Learning Algorithms for Adaptive Green Buildings

Ministry of Education and Vocational Training, the Andalusian Regional Government, 04008 Almería, Spain
Applied Computing Group, University of Almería, 04120 Almería, Spain
Solar Energy Research Centre (CIESOL), University of Almeria, 04120 Almería, Spain
Economy and Business Department, University of Almería, 04120 Almería, Spain
Author to whom correspondence should be addressed.
Sustainability 2019, 11(16), 4320;
Received: 30 May 2019 / Revised: 31 July 2019 / Accepted: 7 August 2019 / Published: 9 August 2019
(This article belongs to the Special Issue Energy Efficiency and Sustainability in Buildings)
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. View Full-Text
Keywords: adaptive systems; machine learning; microservices; smart building adaptive systems; machine learning; microservices; smart building
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MDPI and ACS Style

Rodríguez-Gracia, D.; Piedra-Fernández, J.A.; Iribarne, L.; Criado, J.; Ayala, R.; Alonso-Montesinos, J.; Maria de las Mercedes, C.-U. Microservices and Machine Learning Algorithms for Adaptive Green Buildings. Sustainability 2019, 11, 4320.

AMA Style

Rodríguez-Gracia D, Piedra-Fernández JA, Iribarne L, Criado J, Ayala R, Alonso-Montesinos J, Maria de las Mercedes C-U. Microservices and Machine Learning Algorithms for Adaptive Green Buildings. Sustainability. 2019; 11(16):4320.

Chicago/Turabian Style

Rodríguez-Gracia, Diego, José A. Piedra-Fernández, Luis Iribarne, Javier Criado, Rosa Ayala, Joaquín Alonso-Montesinos, and Capobianco-Uriarte Maria de las Mercedes. 2019. "Microservices and Machine Learning Algorithms for Adaptive Green Buildings" Sustainability 11, no. 16: 4320.

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