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Processes
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Solar Technologies and Photovoltaic Systems
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Solar Technologies and Photovoltaic Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 10 November 2025 | Viewed by 4853

Special Issue Editor

Prof. Dr. Ahmed Alami Merrouni

E-Mail Website
Guest Editor
Materials Science, New Energies and Applications Research Group, LPTPME Laboratory, Department of Physics, Faculty of Sciences, Mohammed 1st University, Oujda 60000, Morocco
Interests: solar energy; green hydrogen; soiling; durability; GIS and land suitability analysis

Special Issue Information

Dear Colleagues,

The transition to renewable energy sources is critical for sustainable development and climate change mitigation. Solar energy, particularly through photovoltaic (PV) systems, is pivotal in this transition. According to recent statistics, global solar PV capacity has experienced unprecedented growth, with installations having surpassed 1,000 GW by 2023. Despite this expansion, significant challenges remain in achieving the cost-effective, large-scale implementation of solar technologies.

Key research areas include the enhancement of PV system efficiency and durability, integration with existing energy grids, and the development of novel materials and manufacturing processes. Addressing these challenges is essential to maximize the potential of solar energy and ensure its viability as a primary energy source.

This Special Issue on “Solar Technologies and Photovoltaic Systems” seeks to publish high-quality research papers focusing on the latest advancements and innovations in solar technology. We invite contributions that address the technical, economic, and environmental aspects of PV systems. Topics of interest include, but are not limited to, the following:

  • Innovative Photovoltaic Materials
  • Artificial Intelligence and application to PV
  • Innovative Storage Systems
  • Advanced Manufacturing Processes
  • System Integration and Grid Compatibility
  • Soiling, cleaning of PV modules
  • Degradation of PV modules and plants components
  • Performance Optimization and Monitoring
  • Environmental and Economic Assessments
  • Future Trends and Innovations

We encourage researchers, engineers, and industry professionals to submit their original research articles, reviews, and case studies to this Special Issue. Your contributions will help drive innovation, foster collaboration, and promote the widespread adoption of solar technology and photovoltaic systems.

We look forward to your submissions.

Prof. Dr. Ahmed Alami Merrouni
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • solar energy
  • PV technologies
  • durability and degradation
  • smart grid
  • building integration
  • PV performances
  • hydrogen electrolysis
  • electrical mobility
  • GIS and land suitability analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

32 pages, 13552 KiB  
Article
The Impact of Data Augmentation on AI-Driven Predictive Algorithms for Enhanced Solar Panel Cleaning Efficiency
by Ali Al-Humairi, Enmar Khalis, Zuhair A. Al Hemyari and Peter Jung
Processes 2025, 13(4), 1195; https://doi.org/10.3390/pr13041195 - 15 Apr 2025
Viewed by 329
Abstract
This study investigates the impact of data augmentation on predictive maintenance machine learning models for robot solar panel cleaning. Data augmentation techniques like synthetic data generation, time-series transformation (shifting, interpolation, and resampling), and extreme condition simulation were used to enhance data diversity and [...] Read more.
This study investigates the impact of data augmentation on predictive maintenance machine learning models for robot solar panel cleaning. Data augmentation techniques like synthetic data generation, time-series transformation (shifting, interpolation, and resampling), and extreme condition simulation were used to enhance data diversity and model generalization. Machine learning algorithms, including logistic regression, support vector machines, deep learning, and ensemble learning, were compared to identify their sensitivity to these techniques. Our experimental findings show that ensemble models (stacking and boosting) show the maximum improvement in predictive accuracy with the added benefit of higher diversity and strength in features. Deep learning models show moderate gains primarily in feature extraction, and simple models such as logistic regression show little impact, indicating the model-dependent effectiveness of data augmentation. Despite better generalization, ensemble methods are at the expense of increased computational cost, indicating a trade-off between accuracy and efficiency. The study employs widely used machine learning frameworks and libraries for data preprocessing, augmentation, model training, and evaluation, ensuring robust and scalable implementation. Full article
(This article belongs to the Special Issue Solar Technologies and Photovoltaic Systems)
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26 pages, 10347 KiB  
Article
Hybrid CSP-PV Combination to Enhance the Green Hydrogen Production in Morocco: Solar Technologies Evaluation and Techno-Economic Analysis
by Abdellatif Azzaoui and Ahmed Alami Merrouni
Processes 2025, 13(3), 769; https://doi.org/10.3390/pr13030769 - 7 Mar 2025
Viewed by 699
Abstract
With the fast-growing implementation of renewable energy projects, Morocco is positioned as a pioneer in green and sustainable development, aiming to achieve 52% of its electricity production from renewable sources by 2030. This ambitious target faces challenges due to the intermittent nature of [...] Read more.
With the fast-growing implementation of renewable energy projects, Morocco is positioned as a pioneer in green and sustainable development, aiming to achieve 52% of its electricity production from renewable sources by 2030. This ambitious target faces challenges due to the intermittent nature of renewable energy, which impacts grid stability. Hydrogen offers a promising solution, but identifying the most cost-effective production configurations is critical due to high investment costs. Despite the growing interest in renewable energy systems, the techno-economic analysis of (Concentrating Solar Power-Photovoltaic) CSP-PV hybrid configurations remain insufficiently explored. Addressing this gap is critical for optimizing hybrid systems to ensure cost-effective and scalable hydrogen production. This study advances the field by conducting a detailed techno-economic assessment of CSP-PV hybrid systems for hydrogen production at selected locations in Morocco, leveraging high-precision meteorological data to enhance the accuracy and reliability of the analysis. Three configurations are analyzed: (i) a standalone 10 MW PV plant, (ii) a standalone 10 MW Stirling dish CSP plant, and (iii) a 10 MW hybrid system combining 5 MW from each technology. Results reveal that hybrid CSP-PV systems with single-axis PV tracking achieve the lowest levelized cost of hydrogen (LCOH2), reducing costs by up to 11.19% and increasing hydrogen output by approximately 10% compared to non-tracking systems. Additionally, the hybrid configuration boosts annual hydrogen production by 2.5–11.2% compared to PV-only setups and reduces production costs by ~25% compared to standalone CSP systems. These findings demonstrate the potential of hybrid solar systems for cost-efficient hydrogen production in regions with abundant solar resources. Full article
(This article belongs to the Special Issue Solar Technologies and Photovoltaic Systems)
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9 pages, 1994 KiB  
Article
Research on Output Prediction Method of Large-Scale Photovoltaic Power Station Based on Gradient-Boosting Decision Trees
by Rongyi Xie, Guobing Pan, Chun Liang, Beimin Lin and Ouyang Yu
Processes 2025, 13(2), 477; https://doi.org/10.3390/pr13020477 - 10 Feb 2025
Viewed by 595
Abstract
As a large number of large-scale photovoltaic (PV) stations are integrated into the power grid, the penetration rate of PV power is growing higher and higher. The intermittency and volatility of PV power generation bring great pressure to the safe and stable operation [...] Read more.
As a large number of large-scale photovoltaic (PV) stations are integrated into the power grid, the penetration rate of PV power is growing higher and higher. The intermittency and volatility of PV power generation bring great pressure to the safe and stable operation of the distribution network. In order to realize scientific energy dispatching and optimization, the predicted output of large PV stations is the data basis and prerequisite. The output prediction method of large PV stations is studied in this paper, and a prediction method based on gradient-boosting decision trees is proposed. In the method, the original data are first collected, and the sample set is established through the steps of data interpolation, supplement, and integration, and then the sample set is pre-processed by data cleaning and normalization. The model training and PV output prediction during the test period are carried out based on the pre-processed data. Finally, the prediction results are imported into the error analysis module. The feasibility and accuracy of the proposed method are analyzed by comparing it with the traditional method. The results show that the normalized mean absolute error (nMAE) and normalized root mean square error (nRMSE) of the proposed method are 7.31% and 11.78%, respectively, while the nMAE and nRMSE of the traditional method are 11.67% and 20.39%, respectively. Thus, the prediction performance of the proposed method is superior to that of the traditional method. Full article
(This article belongs to the Special Issue Solar Technologies and Photovoltaic Systems)
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29 pages, 9348 KiB  
Article
Sustainable and Self-Sufficient Fresh Water Through MED Desalination Powered by a CPV-T Solar Hybrid Collector: A Numerical and Experimental Study
by Armin Buchroithner, Andreas Heinz, Richard Felsberger, Hermann Schranzhofer, Richard Heimrath, Rupert Preßmair and Hannes Wegleiter
Processes 2024, 12(12), 2732; https://doi.org/10.3390/pr12122732 - 2 Dec 2024
Cited by 1 | Viewed by 1157
Abstract
The effects of global warming are severely recognizable and, according to the OECD, 47% of the world’s population will soon live in regions with insufficient drinking water. Already, many countries depend on desalination for fresh water supply, but such facilities are often powered [...] Read more.
The effects of global warming are severely recognizable and, according to the OECD, 47% of the world’s population will soon live in regions with insufficient drinking water. Already, many countries depend on desalination for fresh water supply, but such facilities are often powered by fossil fuels. This paper presents an energy self-sufficient desalination system that runs entirely on solar power. Sunlight is harvested using parabolic trough collectors with an effective aperture area of 1.5 m × 0.98 m and a theoretical concentration ratio of 150 suns, in which a concentrator photovoltaic thermal (CPV-T) hybrid-absorber converts the radiation to electricity and heat. This co-generated energy runs a multi-effect distillation (MED) plant, whereby the waste heat of multi-junction concentrator solar cells is used in the desalination process. This concept also takes advantage of synergy effects of optical elements (i.e., mirrors), resulting in a cost reduction of solar co-generation compared to the state of the art, while at the same time increasing the overall efficiency to ~75% (consisting of an electrical efficiency of 26.8% with a concurrent thermal efficiency of 48.8%). Key components such as the parabolic trough hybrid absorber were built and characterized by real-world tests. Finally, results of system simulations, including fresh water output depending on different weather conditions, degree of autonomy, required energy storage for off-grid operation etc. are presented. Simulation results revealed that it is possible to desalinate around 2,000,000 L of seawater per year with a 260 m2 plant and 75 m3 of thermal storage. Full article
(This article belongs to the Special Issue Solar Technologies and Photovoltaic Systems)
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17 pages, 6804 KiB  
Article
Prediction of Heat Transfer in a Hybrid Solar–Thermal–Photovoltaic Heat Exchanger Using Computational Fluid Dynamics
by Sandro Guadalupe Pérez Grajales, Teresa Hernández Ortíz, Rogelio Martinez-Oropeza, Tabai Torres, López-Pérez Luis Adrián, Javier Delgado-Gonzaga, Armando Huicochea and David Juárez-Romero
Processes 2024, 12(10), 2296; https://doi.org/10.3390/pr12102296 - 20 Oct 2024
Viewed by 1360
Abstract
Solar energy is one of the main renewable energy resources due to its abundance. It can be used for two purposes, thermal or photovoltaic applications. However, when the resource obtained is mixed, it is called photovoltaic thermal hybrid, where the solar panels generate [...] Read more.
Solar energy is one of the main renewable energy resources due to its abundance. It can be used for two purposes, thermal or photovoltaic applications. However, when the resource obtained is mixed, it is called photovoltaic thermal hybrid, where the solar panels generate electricity and are provided with a heat exchanger to absorb energy through a water flow. This is one of the techniques used by the scientific community to reduce the excess temperature generated by solar radiation in the cells, improving the electrical efficiency of photovoltaic systems and obtaining fluid with higher temperature. In this work, the thermal behavior of a heat exchanger equipped with fins in its interior to increase the thermal efficiency of the system was analyzed using CFD (Computational Fluid Dynamics). The results showed that the average fluid outlet temperature was 75.31 °C, considering an incident irradiance of 1067 W/m2 and a fluid inlet temperature of 27 °C. The operating conditions were obtained from published experimental studies, achieving 97.7% similarity between the two. This was due to the boundary conditions of the heat flux (1067 W/m2) impinging directly on the coupled cells and the heat exchanger in a working area of 0.22 m2. Full article
(This article belongs to the Special Issue Solar Technologies and Photovoltaic Systems)
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