The increase in global energy demand, environmental problems and geopolitical tensions due to the control of finite conventional energy resources; these are reasons that have currently focused the attention of scientists on the applications of solar energy. The objective of this contribution is to reflect the trends in research regarding applications of solar energy. The materials and methods used in this investigation consisted of a search and bibliometric analysis carried out in the academic directory Scopus. A group of publications was detected under specific search criteria. The information detected was processed with text mining elements in the visualization software and bibliometric map exploration of VOSviewer science. The article dead sections, records of the first applications of solar energy, the social environment of solar energy applications, the first scientific meetings of global connotation in this subject, and bibliometrics of scientific activity focused on the applications of solar energy in the 21st century. As a result of the research, a sociological and anthropological vision of the man / energy interaction is exposed; This complements lines of research such as sustainable production and consumption, energy management and climate change. Conclusions: The trend in these investigations today, is to growth, and are focused on: heating and cooling of buildings, electric power generation, both in concentrated and distributed forms; and energy conversion for industrial processes.

This paper presents a procedure for the energy optimization of domestic hot water (DHW) systems in hotels located in tropical climates that use centralized air conditioning systems. The study aims to maximize heat recovery from chillers and reduce the fuel consumption of auxiliary heaters by optimizing operational variables such as water mass flow in the primary and secondary DHW circuits and outlet temperature of the backup system. The optimization is implemented using genetic algorithms (GA), which enable the identification of the most efficient flow configurations under variable thermal demand conditions. The proposed methodology integrates a thermoenergetic model validated with real operational data and considers the dynamic behavior of hotel occupancy and water demand. The results show that the optimized strategy reduces auxiliary heating use by up to 75%, achieving annual energy savings of 8244 kWh, equivalent to 2.3 tons of fuel, and preventing the emission of 10.5 tons of CO₂. This study contributes to the design of sustainable energy systems in the hospitality sector and provides replicable strategies for similar climatic and operational contexts.

The transition toward next-generation energy systems requires advanced computational tools capable of supporting accurate, adaptive, and data-driven energy performance assessment. Within this context, Energy Performance Indicators (EnPIs) established under the ISO 50001 framework remain essential for monitoring energy efficiency and continuous improvement; however, conventional indicators are often based on static or simplified relationships that do not adequately capture the dynamic, nonlinear, and multivariable behavior of modern buildings and energy management systems. This systematic review analyzes the integration of ISO 50001-based EnPIs with intelligent algorithms and artificial intelligence techniques for enhanced energy management. The review follows a PRISMA-inspired methodology, using Scopus as the primary database and Web of Science and Google Scholar as complementary sources. From 5442 initial records, 2691 studies were screened and 283 articles were selected for detailed analysis, supported by a bibliometric keyword co-occurrence analysis using VOSviewer 1.6.20. The results show a clear evolution from traditional energy indicators and normalized baselines toward computational modeling approaches based on regression analysis, machine learning, deep learning, forecasting, anomaly detection, and optimization algorithms. These methods improve the predictive capability, adaptability, and operational relevance of EnPIs by incorporating climatic, occupancy, temporal, and operational variables. The reviewed evidence indicates that intelligent algorithms can strengthen ISO 50001 energy management systems by enabling dynamic baselines, early detection of abnormal consumption patterns, predictive decision-making, and continuous operational optimization. Nevertheless, challenges remain regarding data quality, model interpretability, methodological standardization, and practical integration into certified energy management frameworks. Overall, this review highlights that the future of energy performance assessment does not rely on replacing conventional EnPIs, but on transforming them into intelligent, computationally supported indicators for sustainable, resilient, and next-generation energy management systems.