PSS (Photovoltaic Solar Systems) are a key technology in energy transition, and their efficiency depends on multiple interrelated factors. This study uses a systematic review based on the PRISMA methodology to identify four main categories affecting performance: technological, environmental, design and installation, and operational factors. Notably, technological advances in materials such as perovskites and emerging technologies like tandem and bifacial cells significantly enhance conversion efficiency, fostering optimism in the field. Environmental factors, including solar radiation, temperature, and contaminants, also substantially impact system performance. Design and installation play a crucial role, particularly in panel orientation, solar tracking systems, and the optimization of electrical configurations. Maintenance, material degradation, and advanced monitoring systems are essential for sustaining efficiency over time. This study provides a comprehensive understanding of the field by reviewing 113 articles and analyzing three key areas—materials, application of sizing technologies, and optimization—from 2018 to 2025. The paper also explores emerging trends, such as the development of energy storage systems and the integration of smart grids, which hold promise for enhancing photovoltaic module (PM) performance. The findings highlight the importance of integrating technological innovation, design strategies, and effective operational management to maximize the potential of PM systems, providing a solid foundation for future research and applications across residential, industrial, and large-scale contexts.

This article presents a systematic review of optimization methods applied to enhance the performance of photovoltaic (PV) systems, with a focus on critical challenges such as system design and spatial layout, maximum power point tracking (MPPT), energy forecasting, fault diagnosis, and energy management. The emphasis is on the integration of classical and algorithmic approaches. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA) methodology, 314 relevant publications from 2020 to 2025 were analyzed to identify current trends, methodological advances, and practical applications in the optimization of PV performance. The principal novelty of this review lies in its integrative critical analysis, which systematically contrasts the applicability, performance, and limitations of deterministic classical methods with emerging stochastic metaheuristic and data-driven artificial intelligence (AI) techniques, highlighting the growing dominance of hybrid models that synergize their strengths. Traditional techniques such as analytical modeling, numerical simulation, linear and dynamic programming, and gradient-based methods are examined in terms of their efficiency and scope. In parallel, the study evaluates the growing adoption of metaheuristic algorithms, including particle swarm optimization, genetic algorithms, and ant colony optimization, as well as machine learning (ML) and deep learning (DL) models applied to tasks such as MPPT, spatial layout optimization, energy forecasting, and fault diagnosis. A key contribution of this review is the identification of hybrid methodologies that combine metaheuristics with ML/DL models, demonstrating superior results in energy yield, robustness, and adaptability under dynamic conditions. The analysis highlights both the strengths and limitations of each paradigm, emphasizing challenges related to data availability, computational cost, and model interpretability. Finally, the study proposes future research directions focused on explainable AI, real-time control via edge computing, and the development of standardized benchmarks for performance evaluation. The findings contribute to a deeper understanding of current capabilities and opportunities in PV system optimization, offering a strategic framework for advancing intelligent and sustainable solar energy technologies.

This article addresses the technical, aesthetic, and strategic problem of the limited attention paid to design and selection of materials in photovoltaic system (PSS) support structures despite their direct impact on the efficiency, durability and economic viability of these systems. As the costs of modules and electronic components continues to decrease, the structural elements acquire greater weight in the total cost and long-term performance. Our research comprehensively analyzes the mechanical, environmental, and regulatory factors influencing material selection and structural design in PV mounting systems. The PRISMA methodology was used to perform a systematic review of 122 articles published between 2018 and 2025, which were classified along two axes: materials (mild steel, galvanized steel, aluminum, polymers, and composites) and structural design (angle, orientation, loads, support typology, and adaptation to the environment). The results show that an adequate match between design and climatic conditions improves system stability, efficiency, and service life. With the support of digital modeling and advanced simulations, we identify trends towards modular, lightweight, and adaptive solutions, particularly in architectural applications (BIPV). This work provides a robust and contextualized technical framework that facilitates informed decision-making in solar energy projects, with direct implications for the sustainability, structural resilience, and competitiveness of the PSS sector in different geographical regions.

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems.

This study evaluates the energy performance of a BOGE C 22-2 oil-injected rotary screw compressor under real industrial conditions. Using direct measurements with a power quality analyzer and thermodynamic modeling, key performance indicators such as compression work, mass flow rate, compressor efficiency, and motor efficiency were determined. The results revealed actual efficiencies of 27–48%, significantly lower than the expected 60–70% for this type of equipment, mainly due to partial-load operation and low airflow demand. A low power factor of approximately 0.72 was also observed, caused by a high share of reactive power consumption. To address these inefficiencies, the study recommends the installation of an automatic capacitor bank to improve power quality and the integration of a secondary variable speed compressor to enhance performance under low-demand conditions. These findings underscore the importance of assessing compressor behavior in real-world environments and implementing techno-economic strategies to increase energy efficiency and reduce industrial electricity consumption.

This study aimed to evaluate the thermal performance and operational behavior of an evacuated-tube solar collector field installed in a coastal hotel under real industrial conditions. The work analyzed temperature, irradiance, and mass-flow data to determine instantaneous efficiency and identify performance deterioration associated with fouling. A multivariable regression model was developed to predict collector efficiency as a function of operating parameters. The results showed an average efficiency of 40–55%, with a noticeable decrease attributed to soiling effects. The methodology and findings contribute to improving monitoring-based maintenance strategies and optimizing the energy performance of large-scale domestic hot-water systems.