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Processes
Special Issues
Design, Modeling and Optimization of Solar Energy Systems
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Design, Modeling and Optimization of Solar Energy Systems

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

Deadline for manuscript submissions: 25 July 2025 | Viewed by 837

Special Issue Editor

Dr. Rohit Salgotra

E-Mail Website
Guest Editor
Faculty of Physics and applied computer science, AGH University of Krakow, 30, 30-059 Krakow, Poland
Interests: evolutionary algorithms; nature-inspired algorithms; optimization; soft computing; fuel cells; solar energy storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled "Design, Modeling, and Optimization of Solar Energy Systems", explores the principles, methodologies, and tools necessary for developing efficient and sustainable solar energy solutions. The focus is on the integration of solar technologies in various applications, utilizing advanced modeling techniques to predict performance, and employing optimization strategies to enhance efficiency and cost effectiveness. This Special Issue welcomes publications highlighting the theoretical foundations of solar energy, practical design considerations, simulation methods, and the latest innovations in optimization algorithms and software for solar energy systems.

Our Special Issue will accept a broad range of new advances in the field of solar energy and related renewable sources of energy. The topics include, but are not limited to, the following:

  1. Solar energy system design;
  2. Fuel cells;
  3. Photovoltaic systems;
  4. Hybrid solar energy systems;
  5. Mathematical modeling of solar energy systems;
  6. Performance prediction models;
  7. Solar energy storage;
  8. Efficiency enhancement techniques;
  9. Optimization, performance, and sustainability;
  10. Real-world applications.

Dr. Rohit Salgotra
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 system
  • hybrid solar energy systems
  • mathematical modeling
  • efficiency enhancement techniques
  • optimization, performance, and sustainability

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

Published Papers (1 paper)

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Research

15 pages, 1296 KiB  
Article
Predicting Photovoltaic Energy Production Using Neural Networks: Renewable Integration in Romania
by Grigore Cican, Adrian-Nicolae Buturache and Valentin Silivestru
Processes 2025, 13(7), 2219; https://doi.org/10.3390/pr13072219 - 11 Jul 2025
Viewed by 282
Abstract
Photovoltaic panels are pivotal in transforming solar irradiance into electricity, making them a key technology in renewable energy. Despite their potential, the distribution of photovoltaic systems in Romania remains sparse, requiring advanced data analytics for effective management, particularly in addressing the intermittent nature [...] Read more.
Photovoltaic panels are pivotal in transforming solar irradiance into electricity, making them a key technology in renewable energy. Despite their potential, the distribution of photovoltaic systems in Romania remains sparse, requiring advanced data analytics for effective management, particularly in addressing the intermittent nature of photovoltaic energy. This study investigates the predictive capabilities of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for forecasting hourly photovoltaic energy production in Romania. The results indicate that CNN models significantly outperform LSTM models, with 77% of CNNs achieving an R2 of 0.9 or higher compared to only 13% for LSTMs. The best-performing CNN model reached an R2 of 0.9913 with a mean absolute error (MAE) of 9.74, while the top LSTM model achieved an R2 of 0.9880 and an MAE of 12.57. The rapid convergence of the CNN model to stable error rates illustrates its superior generalization capabilities. Moreover, the model’s ability to accurately predict photovoltaic production over a two-day timeframe, which is not included in the testing dataset, confirms its robustness. This research highlights the critical role of accurate energy forecasting in optimizing the integration of photovoltaic energy into Romania’s power grid, thereby supporting sustainable energy management strategies in line with the European Union’s climate goals. Through this methodology, we aim to enhance the operational safety and efficiency of photovoltaic systems, facilitating their large-scale adoption and ultimately contributing to the fight against climate change. Full article
(This article belongs to the Special Issue Design, Modeling and Optimization of Solar Energy Systems)
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