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The many benefits offered by green or smart buildings have led to an increase in their construction. In turn, this growth has been accompanied by a rapid evolution of research on this topic. Thus, given the specialist interest, research on the use of artificial intelligence in this type of construction has been gaining space. This topic, although still novel, due to its current and future importance requires a literature review to identify the main actors, evaluate the past and establish future lines of research. The results based on 174 manuscripts detected in Web of Science and Scopus databases allow us to establish the main authors, institutions, countries and journals as well as the seminal papers in this field. Furthermore, through a keywords co-occurrence analysis this study identifies some of the topics that have received most interest in the past as well as some promising future research trends. This bibliometric study analyzes the relationship between the main clusters DML&B (Deep - Machine Learning and Building Constructions) by means of a detailed description of the fundamental concepts identified in the content analysis. It is complemented by a temporal keyword analysis focusing on the economic, social and environmental benefits obtained through green or intelligent buildings. Consequently, this research contributes to the literature by providing an overview of the past and current status of this field, as well as by opening future research lines.

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.