Microservices and Machine Learning Algorithms for Adaptive Green Buildings

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.

Recent innovations in solar energy education and research towards sustainable energy development

“…Fluidized bed and bioreactors [117,118], absorption chillers [119], Hydrogen fuel and ammonia production [86,120,121], energy efficient buildings [20,122], Fuel cells [123], iron ore agglomeration [124], wastewater treatment [125], medical sterilization [126,127], cooling of building by triple vacuum-glazed windows [128] Solar PV Smart textiles [129], agrivoltaic system [130,131], electric vehicles [132], flexible solar cells [133], road structures and marine applications [134,135], hybridization with biogas system [136], solar home systems [137].…”

Review of Artificial Intelligence Techniques inGreen/Smart Buildings.

“…An adaptive architecture at runtime for green buildings was presented by Rodríguez-Gracia [31]. The presented solution was used to equip the domotic system of the CIESOL green building with the ability to adapt to energy (between 20-40% less energy) user behaviour by integrating machine learning algorithms for decision making into the domotic system.…”