This paper presents research results to improve energy efficiency in one of the crude oil heating furnaces at the “Hermanos Díaz” refinery in Santiago de Cuba, Cuba. It analyzes the main process’s variables and disturbances, and the multivariate dynamic behavior of the F-101 furnace temperature is characterized to evaluate different control strategies. In addition, the design of a linear regulation control law was implemented as a way to solve the limitations of the existing control of the furnace, to control the plant for the first time with a multivariable approach, demonstrating superior performance by guaranteeing decoupling between the variables, decreasing the overruns by 6%, and increasing the response speed of the system by more than 5 min. The comparison with results obtained with other control strategies allowed us to determine the better performance of the furnace by increasing its energy efficiency, evidencing the economic and environmental impact and obtaining as benefits a better dynamic behavior by reducing fuel oil consumption by 5%, equivalent to 0.74 m³/day, which reduces the operating costs of the plant, the temperature of the gasses by 2%, emissions of CO₂ pollutant gas to the environment by between 3 and 5%, and increasing energy efficiency by 1.5%.

Optimizing wind turbine control is a major challenge due to wind variability and nonlinearity. This research seeks to improve the performance of wind turbines by designing and developing hybrid intelligent controllers that combine advanced artificial intelligence techniques. A control system combining deep neural networks and fuzzy logic was implemented to optimize the efficiency and operational stability of a 3.5 MW wind turbine. This study analyzed several deep learning models (LSTM, GRU, CNN, ANN, and transformers) to predict the generated power, using data from the SCADA system. The structure of the hybrid controller includes a fuzzy inference system with 28 rules based on linguistic variables that consider power, wind speed, and wind direction. Experiments showed that the hybrid-GRU controller achieved the best balance between predictive performance and computational efficiency, with an R² of 0.96 and 12,119.54 predictions per second. The GRU excels in overall optimization. This study confirms intelligent hybrid controllers’ effectiveness in improving wind turbines’ performance under various operating conditions, contributing significantly to the field of wind energy.

Nowadays, microgrids (MGs) play a crucial role in modern power systems due to possibility of integrating renewable energies into grid-connected or islanded power systems. The Load Frequency Control (LFC) is an issue of paramount importance to ensure MGs reliable and safe operation. Specifically, in AC MGs, primary frequency control of each energy source can be guaranteed in order to integrate other energy sources. This paper proposes a micro-hydro frequency control scheme, combining the control of a reduced dump load and the nozzle flow control of Pelton turbines operating in autonomous regime. Some works have reported the integration of dump load and flow control methods, but they did not reduce the dump load value and adjust the nozzle flow linearly to the power value demanded by users, causing the inefficient use of water. Simulation results were obtained in Matlab^®/Simulink^® using models obtained from previous research and proven by means of experimental studies. The simulation of the proposed scheme shows that the frequency control in this plant is done in correspondence with the Cuban NC62-04 norm of power energy quality. In addition, it is possible to increase energy efficiency by reducing the value of the resistive dump load by up to 7.5% in a case study. The validation result shows a 60% reduction of overshoot and settling time of frequency temporal behavior of the autonomous micro-hydro.

Biomass gasification technology has been extensively researched around the world; however, there is a need to evaluate the current research landscape and evolutionary direction of research in the broader context of energy transition. A systematic bibliometric analysis of the Web of Science database was performed for articles that fall within the keywords ‘Biomass gasification’ and ‘Energy transition’. A total of 1498 articles were identified; after applying inclusion and exclusion criteria, 1196 articles were selected for final analysis. VOSviewer and Biblometrix were used for the study. Trends in biomass gasification and energy transition were identified as the initial (1994–2008), rise (2009–2018) and prosperity stages (2019 to date). A significant portion (47%) of publications were concentrated in 10 journals, including Renewable Energy , Energy and International Journal of Hydrogen Energy . Among the countries, China leads with 672 publications, followed by the US, India, and Italy. The prominent research areas are hydrogen production, process optimization, exergy analysis, tar reduction and life cycle assessment. Currently, the active area of research is the production of hydrogen‐rich gas through biomass gasification technologies and integrating bioenergy systems with carbon capture and storage to achieve sustainable energy transition goals.

Objective: Detect the hot spots present in the photovoltaic modules, to know what type of maintenance they need to achieve good energy performance. Methods: The bibliographic review method, the inductive-deductive and the experimental method were applied, through visual inspection and infrared thermography to determine the number of panels that are affected, using the temperature variation technique. Results: It was obtained that 7.41% need corrective maintenance, 11.11% preventive maintenance and 81.48% predictive maintenance, this maintenance must be carried out with the objective that the photovoltaic generator does not lose its optimal operability during its work process and decrease its performance, this prediction method is done automatically, the procedure allows you to organize the detection and classification process more effectively. Conclusions: Thermography is a passive, non-contact measurement method. Thermal images show the temperature distribution on the surface of the object. Thermal imaging technology has become one of the most valuable diagnostic tools for predictive maintenance by detecting anomalies that are not normally visible to the naked eye. These can prevent costly system failures before they occur, thereby achieving that reduces the occurrence of breakdowns, increases the overall performance factor, improves the profitability conditions of the project, reduces costs associated with its operation.