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Dr. Jabar H. Yousif

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Faculty of Computing and Technology, Sohar University, P.O. Box 44, Sohar, Oman
Interests: artificial intelligence; intelligent agent; renewable energy modeling; renewable energy optimization; forecasting models; soft computing (ANN & fuzzy logic; cloud computing; natural language processing; virtual/augmented reality

Special Issue Information

Dear Colleagues,

Solar energy is a feasible renewable energy option used to meet power needs at a low cost. Thus, the search for methods to boost PV modules' efficiency and power production has intensified. Therefore, it is necessary to find efficient ways to harness the sun's energy and transform it into electricity using solar photovoltaics (PVs). Developing high-quality technology to make solar energy affordable for consumers and industries increases competitiveness in global markets. This Special Issue focuses on the cutting-edge solar and cell technologies that keep solar power in the energy spotlight.

Topics of interest for publication include, but are not limited to:

Bifacial solar panels;
Building-integrated photovoltaics (BIPV);
Concentration photovoltaic cells (CPVs);
Heterojunction technology (HJT);
Half-cell technology;
Multi-junction solar cells;
Perovskite solar cells;
Crystalline silicon solar cells;
Thin-film solar cells;
Multijunction solar cells
Organic solar cells;
Dye-sensitized solar cells (DSSCs).

Dr. Jabar H. Yousif
Guest Editor

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Keywords

solar cell
solar panel
photovoltaic (PV)
PV design
PV modelling
panel efficiency
production efficiency
renewable energy

Published Papers (1 paper)

Research

17 pages, 4785 KiB

Open AccessArticle

Artificial Neural Network Modelling and Experimental Evaluation of Dust and Thermal Energy Impact on Monocrystalline and Polycrystalline Photovoltaic Modules

by Jabar H. Yousif, Hussein A. Kazem, Haitham Al-Balushi, Khaled Abuhmaidan and Reem Al-Badi

Energies 2022, 15(11), 4138; https://doi.org/10.3390/en15114138 - 04 Jun 2022

Cited by 10 | Viewed by 1781

Abstract

Many environmental parameters affect the performance of solar photovoltaics (PV), such as dust and temperature. In this paper, three PV technologies have been investigated and experimentally analyzed (mono, poly, and flexible monocrystalline) in terms of the impact of dust and thermal energy on PV behavior. Furthermore, a modular neural network is designed to test the effects of dust and temperature on the PV power production of six PV modules installed at Sohar city, Oman. These experiments employed three pairs of PV modules (one cleaned daily and one kept dusty for 30 days). The performance of the PV power production was evaluated and examined for the three PV modules (monocrystalline, polycrystalline, and flexible), which achieved 30.24%, 28.94%, and 36.21%, respectively. Moreover, the dust reduces the solar irradiance approaching the PV module and reduces the temperature, on the other hand. The neural network and practical models’ performance were compared using different indicators, including MSE, NMSE, MAE, Min Abs Error, and r. The Mean Absolute Error (MAE) is used for evaluating the accuracy of the ANN machine learning model. The results show that the accuracy of the predicting power of the six PV modules was considerable, at 97.5%, 97.4%, 97.6%, 96.7%, 96.5%, and 95.5%, respectively. The dust negatively reduces the PV modules’ power production performance by about 1% in PV modules four and six. Furthermore, the results were evident that the negative effect of the dust on the PV module production based on the values of RMSE, which measures the square root of the average of the square’s errors. The average errors in predicting the power production of the six PV modules are 0.36406, 0.38912, 0.34964, 0.49769, 0.46486, and 0.68238. Full article

(This article belongs to the Special Issue Cutting-Edge Solar Panel and Cell Technologies)

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