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Advanced Modeling and Simulation for Application in Solar Radiation and Photovoltaic Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 6569

Special Issue Editors

Dr. Manoharan Madhiarasan

E-Mail Website
Guest Editor
Department of Electronics and Computers, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, 500036 Brasov, Romania
Interests: renewable energy systems; artificial intelligence; machine learning; deep learning; optimization; soft computing; modeling and simulation; computer vision and pattern recognition; IoT and embedded systems
Special Issues, Collections and Topics in MDPI journals
Dr. Sameer Al-Dahidi

E-Mail Website
Guest Editor
Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan
Interests: prognostics and health management; predictive maintenance; system analysis; safety analysis; risk assessment and management; resilience; reliability engineering; mathematical modelling; artificial intelligence; machine learning; data mining; optimization; renewable energy; wind and solar photovoltaic systems; nuclear power; energy forecasting; performance analysis; mechanical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed Louzazni

E-Mail Website
Guest Editor
National School of Applied Science, Abdelmalek Essaâdi University, Tetouan B.P. 2117, Morocco
Interests: performance analysis; monitoring; lifetime analysis; fault detection; control management; hybrid renewable energy; mathematical modelling; optimization and meta-heuristic algorithm; computational intelligence; photovoltaic & power energy; forecasting; fuel cell; radar; radio frequency; electromagnetic and electronic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of society and humankind prompted the development of renewable and sustainable energy systems to mitigate the effects of global warming and other environmental and health hazards. Photovoltaic (PV) systems demonstrate great potential and are pollution-free, prompting calls to increase their installed capacity globally. Machine learning, deep learning, optimization algorithms, modeling, and simulation concerning solar radiation and PV systems are the driving forces of sustainable electricity production helping to overcome the challenges of PV integration into the grid. In applied physics, solar radiation theory, realization, and characterization are hot topics. Still, enhancing the efficiency of the PV module, cost reduction, and improved PV conversion technology and storage are ambitious fields of research with huge potential for the development of high-quality PV systems as technology progresses.

This Special Issue provides a forum to address the complex challenges in modeling and simulation, devoted to intelligent methods and algorithms. Solar radiation and PV systems are fascinating and multidisciplinary research domains. Hence, this Special Issue presents an opportunity for professionals and researchers to explore novel strategies, zero energy building, EVs, and technical advancement, particularly highlighting the application of PV systems.

We hope that this Special Issue is of interest to academics, researchers, practitioners, scientists, and industrial delegates, enabling them to share and exchange their original and high-quality articles (new theories, methods, techniques, and applications) and to publish their latest results and progress in modeling and simulation application in the fields of solar radiation and PV systems.

New ideas related, but not limited, to the following primary topics are invited:

❖ Performance Improvement of PV Power Plants.

❖ Solar Thermal System Modeling and Applications.

❖ Hybrid Energy Storage Device.

❖ Solar-associated Hydrogen Production.

❖ Solar Cell Innovation Modeling.

❖ Matric Converter Design and Simulation.

❖ Dynamic Modeling of Hybrid Systems.

❖ Power System Planning and Control Strategies and Techniques.

❖ Zero Energy Building.

❖ Electric Vehicle Modeling using PV System.

❖ Novel Optimization Algorithms.

❖ Solar Radiation Forecasting Models and Uncertainty Quantification.

Dr. Manoharan Madhiarasan
Dr. Sameer Al-Dahidi
Dr. Mohamed Louzazni
Guest Editors

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. Sustainability 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 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

  • modeling
  • simulation
  • solar radiation
  • photovoltaic systems
  • performance analysis
  • uncertainty quantification
  • prediction
  • optimization
  • machine learning
  • artificial intelligence

Published Papers (3 papers)

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Research

16 pages, 18422 KiB  
Article
A Machine Learning Approach to Estimating Solar Radiation Shading Rates in Mountainous Areas
by Luting Xu, Yanru Li, Xiao Wang, Lei Liu, Ming Ma and Junhui Yang
Sustainability 2024, 16(2), 931; https://doi.org/10.3390/su16020931 - 22 Jan 2024
Cited by 1 | Viewed by 860
Abstract
Quantification of shading effects from complex terrain on solar radiation is essential to obtain precise data on incident solar radiation in mountainous areas. In this study, a machine learning (ML) approach is proposed to rapidly estimate the shading effects of complex terrain on [...] Read more.
Quantification of shading effects from complex terrain on solar radiation is essential to obtain precise data on incident solar radiation in mountainous areas. In this study, a machine learning (ML) approach is proposed to rapidly estimate the shading effects of complex terrain on solar radiation. Based on two different ML algorithms, namely, Ordinary Least Squares (OLS) and Gradient Boosting Decision Tree (GBDT), this approach uses terrain-related factors as input variables to model and analyze direct and diffuse solar radiation shading rates. In a case study of western Sichuan, the annual direct and diffuse radiation shading rates were most correlated with the average terrain shading angle within the solar azimuth range, with Pearson correlation coefficients of 0.901 and 0.97. The GBDT-based models achieved higher accuracy in predicting direct and diffuse radiation shading rates, with R2 values of 0.982 and 0.989, respectively, surpassing the OLS-based models by 0.081 and 0.023. In comparisons between ML models and classic curve-fitting models, the GBDT-based models consistently performed better in predicting both the direct radiation shading rate and the diffuse radiation shading rate, with a standard deviation of residuals of 0.330% and 0.336%. The OLS-based models also showed better performance compared to the curve-fitting models. Full article
(This article belongs to the Special Issue Advanced Modeling and Simulation for Application in Solar Radiation and Photovoltaic Systems)
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19 pages, 7932 KiB  
Article
Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms
by Edna S. Solano and Carolina M. Affonso
Sustainability 2023, 15(10), 7943; https://doi.org/10.3390/su15107943 - 12 May 2023
Cited by 5 | Viewed by 1445
Abstract
This paper proposes an ensemble voting model for solar radiation forecasting based on machine learning algorithms. Several ensemble models are assessed using a simple average and a weighted average, combining the following algorithms: random forest, extreme gradient boosting, categorical boosting, and adaptive boosting. [...] Read more.
This paper proposes an ensemble voting model for solar radiation forecasting based on machine learning algorithms. Several ensemble models are assessed using a simple average and a weighted average, combining the following algorithms: random forest, extreme gradient boosting, categorical boosting, and adaptive boosting. A clustering algorithm is used to group data according to the weather, and feature selection is applied to choose the most-related inputs and their past observation values. Prediction performance is evaluated by several metrics using a real-world Brazilian database, considering different prediction time horizons of up to 12 h ahead. Numerical results show the weighted average voting approach based on random forest and categorical boosting has superior performance, with an average reduction of 6% for MAE, 3% for RMSE, 16% for MAPE, and 1% for R2 when predicting one hour in advance, outperforming individual machine learning algorithms and other ensemble models. Full article
(This article belongs to the Special Issue Advanced Modeling and Simulation for Application in Solar Radiation and Photovoltaic Systems)
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32 pages, 7506 KiB  
Article
Performance Analysis of a Hybrid Renewable-Energy System for Green Buildings to Improve Efficiency and Reduce GHG Emissions with Multiple Scenarios
by Hani Al-Rawashdeh, Omar Ali Al-Khashman, Jehad T. Al Bdour, Mohamed R. Gomaa, Hegazy Rezk, Abdullah Marashli, Laith M. Arrfou and Mohamed Louzazni
Sustainability 2023, 15(9), 7529; https://doi.org/10.3390/su15097529 - 4 May 2023
Cited by 7 | Viewed by 3375
Abstract
A hybrid system, such as solar and wind, may be more successful than nonhybrid systems in accelerating the transition from conventional to renewable power sources. However, these new energy sources have several challenges, such as intermittency, storage capacity, and grid stability. This paper [...] Read more.
A hybrid system, such as solar and wind, may be more successful than nonhybrid systems in accelerating the transition from conventional to renewable power sources. However, these new energy sources have several challenges, such as intermittency, storage capacity, and grid stability. This paper presents a complete analysis and study of a hybrid renewable-energy system (HRES) to convert a facility into a green building and reduce its dependence on conventional energy by generating clean energy with near-zero greenhouse-gas (GHG) emissions. The proposed system aims to reduce the energy bill of a hotel in Petra, Jordan, by considering different sustainable energy resource configurations in a grid-connected hybrid renewable energy system (GHRES). The hybrid optimization of multiple energy resources (HOMER) grid software was utilized on the hybrid systems to study ways to improve their overall efficiency and mitigate GHG emissions from an economic perspective. The hybrid system components included in the simulation were a solar photovoltaic (PV) system, a wind turbine (WT) system, a diesel generator (DG), and a converter. Five scenarios (PV–Converter–DG–Grid, PV–Converter–Battery–DG–Grid, WT–DG–Grid, PV–WT–Converter–Battery–DG–Grid, PV–WT–Converter–DG–Grid) were considered. The optimal configuration had a USD 1.16 M total net present cost, USD 0.0415/kWh cost of energy, 15.8% effective internal rate of return, and an approximately 77% reduction in carbon emissions compared to the base case. Full article
(This article belongs to the Special Issue Advanced Modeling and Simulation for Application in Solar Radiation and Photovoltaic Systems)
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