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Energies
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Efficient Solar Energy Conversion and Effective Energy Storage
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Efficient Solar Energy Conversion and Effective Energy Storage

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1847

Special Issue Editor

Dr. Mohammad Akrami

E-Mail Website
Guest Editor
Department of Engineering, University of Exeter, Exeter EX4 4QF, UK
Interests: sustainable engineering; energies; biosystems; computational analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue on "Efficient Solar Energy Conversion and Effective Energy Storage" aims to explore recent advancements and innovative approaches in harnessing and storing solar energy. As the demand for renewable energy sources intensifies, the efficient conversion of solar energy into usable power and its subsequent storage become critical components of a sustainable energy infrastructure. This Special Issue will cover a broad range of topics, including novel materials and technologies for photovoltaic cells, advancements in solar thermal systems, and cutting-edge methods for energy storage such as batteries, supercapacitors, and hydrogen storage. Contributions addressing the integration of solar energy systems with existing power grids and the optimization of energy storage solutions to enhance reliability and efficiency are particularly welcome. The goal is to provide a comprehensive overview of current research, facilitate the exchange of ideas, and promote the development of practical solutions for efficient solar energy conversion and storage.

Dr. Mohammad Akrami
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. Energies is an international peer-reviewed open access semimonthly 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 2600 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 conversion
  • photovoltaic cells
  • supercapacitors
  • hydrogen storage
  • renewable energy integration
  • energy grid optimization
  • sustainable energy solutions
  • solar thermal systems
  • energy storage technologies
  • batteries

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Related Special Issue

Published Papers (2 papers)

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Research

27 pages, 4621 KiB  
Article
Thermodynamics-Informed Neural Networks for the Design of Solar Collectors: An Application on Water Heating in the Highland Areas of the Andes
by Mauricio Cáceres, Carlos Avila and Edgar Rivera
Energies 2024, 17(19), 4978; https://doi.org/10.3390/en17194978 - 5 Oct 2024
Viewed by 787
Abstract
This study addresses the challenge of optimizing flat-plate solar collector design, traditionally reliant on trial-and-error and simplified engineering design methods. We propose using physics-informed neural networks (PINNs) to predict optimal design conditions in a range of data that not only characterized the highlands [...] Read more.
This study addresses the challenge of optimizing flat-plate solar collector design, traditionally reliant on trial-and-error and simplified engineering design methods. We propose using physics-informed neural networks (PINNs) to predict optimal design conditions in a range of data that not only characterized the highlands of Ecuador but also similar geographical locations. The model integrates three interconnected neural networks to predict global collector efficiency by considering atmospheric, geometric, and physical variables, including overall loss coefficient, efficiency factors, outlet fluid temperature, and useful heat gain. The PINNs model surpasses traditional simplified thermodynamic equations employed in engineering design by effectively integrating thermodynamic principles with data-driven insights, offering more accurate modeling of nonlinear phenomena. This approach enhances the precision of solar collector performance predictions, making it particularly valuable for optimizing designs in Ecuador’s highlands and similar regions with unique climatic conditions. The ANN predicted a collector overall loss coefficient of 5.199 W/(m2·K), closely matching the thermodynamic model’s 5.189 W/(m2·K), with similar accuracy in collector useful energy gain (722.85 W) and global collector efficiency (33.68%). Although the PINNs model showed minor discrepancies in certain parameters, it outperformed traditional methods in capturing the complex, nonlinear behavior of the data set, especially in predicting outlet fluid temperature (55.05 °C vs. 67.22 °C). Full article
(This article belongs to the Special Issue Efficient Solar Energy Conversion and Effective Energy Storage)
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42 pages, 18667 KiB  
Article
Artificial Intelligence-Based Improvement of Empirical Methods for Accurate Global Solar Radiation Forecast: Development and Comparative Analysis
by Mohamed A. Ali, Ashraf Elsayed, Islam Elkabani, Mohammad Akrami, M. Elsayed Youssef and Gasser E. Hassan
Energies 2024, 17(17), 4302; https://doi.org/10.3390/en17174302 - 28 Aug 2024
Viewed by 701
Abstract
Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation [...] Read more.
Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation methods have been developed to forecast global solar radiation (GSR). With this consideration, this study aims to develop temperature-based GSR models using a commonly utilized approach in machine learning techniques, ANNs, to predict GSR using just temperature data. It also compares the performance of these models to the commonly used empirical technique. Additionally, it develops precise GSR models for five new sites and the entire region, which currently lacks AI-based models despite the presence of proposed solar energy plants in the area. The study also examines the impact of varying lengths of validation datasets on solar radiation models’ prediction and accuracy, which has received little attention. Furthermore, it investigates different ANN architectures for GSR estimation and introduces a comprehensive comparative study. The findings indicate that the most advanced models of both methods accurately predict GSR, with coefficient of determination, R2, values ranging from 96% to 98%. Moreover, the local and general formulas of the empirical model exhibit comparable performance at non-coastal sites. Conversely, the local and general ANN-based models perform almost identically, with a high ability to forecast GSR in any location, even during the winter months. Additionally, ANN architectures with fewer neurons in their single hidden layer generally outperform those with more. Furthermore, the efficacy and precision of the models, particularly ANN-based ones, are minimally impacted by the size of the validation data sets. This study also reveals that the performance of the empirical models was significantly influenced by weather conditions such as clouds and rain, especially at coastal sites. In contrast, the ANN-based models were less impacted by such weather variations, with a performance that was approximately 7% better than the empirical ones at coastal sites. The best-developed models, particularly the ANN-based models, are thus highly recommended. They enable the precise and rapid forecast of GSR, which is useful in the design and performance evaluation of various solar applications, with the temperature data continuously and easily recorded for various purposes. Full article
(This article belongs to the Special Issue Efficient Solar Energy Conversion and Effective Energy Storage)
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