Industry 4.0 is in continuous technological growth that benefits all sectors of industry and society in general. This article reviews the Digital Twin (DT) concept and the interest of its application in photovoltaic installations. It compares how other authors use the DT approach in photovoltaic installations to improve the efficiency of the renewable energy generated and consumed, energy prediction and the reduction of the operation and maintenance costs of the photovoltaic installation. It reviews how, by providing real-time data and analysis, DTs enable more informed decision-making in the solar energy sector. The objectives of the review are to study digital twin technology and to analyse its application and implementation in PV systems.

Industry 4.0 is in continuous technological growth that benefits all sectors of industry and society in general. This article reviews the concept of Digital Twin (DT) and the interest of its application in photovoltaic installations. A comparison is made on how other authors use the DT approach in photovoltaic installations to improve the efficiency of the renewable energy generated and consumed, the energy prediction and the reduction of the operation and maintenance costs of the photovoltaic installation. It is reviewed how, by providing real-time data and analysis, DTs enable more informed decisions in the solar energy sector. The objectives of the review are to study the digital twin technology and to analyze its application and implementation in photovoltaic systems.

Efficiently controlling and managing photovoltaic installations for self-consumption is key to making optimal use of these systems. The first step to achieving that control is modeling the generation of electricity by photovoltaic panels with digital twins. This paper presents an experimental evaluation of four photovoltaic power prediction models in a real-world environment using high-resolution, annual data from a university facility. A mathematical model and three neural network models (MLP, LSTM, and GRU) are compared under standardized metrics. The results demonstrate that the Multilayer Perceptron (MLP) achieves the highest overall accuracy and seasonal consistency. It outperforms the other three models, particularly in scenarios where the relationships between meteorological variables and power generation are predominantly static. However, the mathematical model maintains a competitive performance in conditions of low variability, such as in the summer and autumn. The seasonal analysis reveals the importance of selecting and adjusting models according to operational and climatic contexts. This study’s main contribution is identifying the MLP as the most efficient option for implementing digital twins in photovoltaic installation control. This facilitates real-time monitoring, optimization, and predictive maintenance and contributes to smart, sustainable energy management in buildings to align with climate neutrality guidelines.

Nowadays, due to the considerable increase in energy demand, the reduction of CO₂ emissions, the increase in energy efficiency and the lack of knowledge of the population in the current electricity system, both at residential and industrial levels, the need to implement measures that promote the common benefit of society has arisen. Photovoltaic energy has been gaining importance as a sustainable alternative to traditional energy sources, especially in Spain, where it is one of the most advantageous options thanks to its geographical location. In order to help consumers at household, SME or industrial levels to understand the complex electricity market, a new tool designed to optimize savings is presented. This application analyzes individual consumption patterns in detail and proposes savings strategies based on both tariffs and the implementation of solar photovoltaic installations. By integrating actual consumption data and PV generation estimations, the tool generates different customized scenarios, allowing the user to minimize their costs. The results of this study underline the importance of adopting renewable energy solutions, offering practical ideas for both policy makers and consumers.

This study explores sustainable practices in energy resource management, with the aim of optimizing energy consumption while maintaining quality of life. It focuses on energy efficiency in the residential sector of the autonomous community of Extremadura. The methodology involved collecting data from 50 households, using a computer tool to assess and classify energy efficiency levels. The results showed that homes built before 1994 had low energy ratings, whereas those built afterward demonstrated improvement, reflecting a growing environmental awareness. It was found that small investments, such as the installation of control devices and the adoption of conscious consumption habits, can significantly improve energy efficiency. The study highlights the importance of raising community awareness about energy efficiency and notes that, despite limitations in the data, the findings provide valuable insights for future interventions. In conclusion, energy efficiency is key to a sustainable future, and we all share the responsibility of improving it.

STEM outreach evaluations often rely on full-sample averages and general enjoyment metrics, overlooking how specific pedagogical factors relate to students without a consolidated vocation. This study evaluates the “Desayuna Ingeniería” (Breakfast Engineering) STEM outreach programme to identify intervention variables associated with perceived increases in pre-university students’ vocational interest in industrial engineering. In the retrospective survey (N = 584), 48.8% of participants reported increased interest after the visit. Focusing specifically on the target subsample of students with low or moderate prior interest, who represent the “undecided” group (n = 408), binary logistic regression models examined how active workshop participation, overall workshop satisfaction, and mentor gender configuration were associated with perceived vocational change. Active workshop participation showed a positive predictive trend that did not reach the prespecified significance threshold (p = 0.056), overall workshop satisfaction showed no statistically significant association (p = 0.697), and evidence regarding mentor gender configuration was inconclusive. Exploratory gender-stratified analyses suggested that perceived increases were primarily associated with baseline interest in female students, and with baseline interest alongside a positive, although non-significant, trend for active participation (p = 0.052) in male students. Brief interventions may benefit from prioritising hands-on involvement, whereas fostering girls’ baseline STEM motivation at earlier school stages remains a theoretical hypothesis for future longitudinal studies.