Optimizing photovoltaic systems: A meta-optimization approach with GWO-Enhanced PSO algorithm for improving MPPT controllers

Renewable Energy, Год журнала: 2024, Номер 230, С. 120892 - 120892

Опубликована: Июнь 28, 2024

Язык: Английский

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Advances in renewable energy for sustainable development

Renewable Energy, Год журнала: 2023, Номер 219, С. 119377 - 119377

Опубликована: Сен. 28, 2023

Язык: Английский

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Photovoltaic passive cooling via water vapor sorption-evaporation by hydrogel

Applied Thermal Engineering, Год журнала: 2023, Номер 240, С. 122185 - 122185

Опубликована: Дек. 9, 2023

Язык: Английский

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Towards highly efficient solar photovoltaic thermal cooling by waste heat utilization: A review

Energy Conversion and Management X, Год журнала: 2024, Номер 23, С. 100671 - 100671

Опубликована: Июль 1, 2024

Photovoltaic (PV) systems are popular for their reliability and zero fuel costs. However, only around 20 % of solar energy is converted into electricity, while the remainder dissipated as waste heat. Excessive heat affects lifespan PV systems, leading to abnormal operating temperatures. In this notion, Photovoltaic-thermal (PV/T) introduced extract through various cooling techniques harness electrical thermal energies, demonstrating capabilities experimental modeling techniques. Researchers have sought develop optimized based on empirical, semi-empirical, AI-based efficient execution PV/T systems. This study reviews current optimization developments in focusing multiple numerical designs. Various methods, including air, water, phase change materials (PCM) with nanofluids, examined promising contributions efficiency enhancement. Additionally, methods been investigated by incorporating automated processes employing self-automation These aim reduce overall cost establish a self-sustaining performance. Finally, challenges recommendations future research enhancement highlighted.

Язык: Английский

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Sustainable development using integrated energy systems and solar, biomass, wind, and wave technology

Renewable Energy, Год журнала: 2024, Номер unknown, С. 121359 - 121359

Опубликована: Сен. 1, 2024

Язык: Английский

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Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

Environmental Science & Technology, Год журнала: 2024, Номер 58(23), С. 10116 - 10127

Опубликована: Май 27, 2024

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity scarcity of available biomedical data challenge development predictive models. Combining nonlinear together with multicondition descriptors offers a solution using from various assays to create robust model. This work applies (MCDs) develop QSTR (Quantitative Structure–Toxicity Relationship) model based on large set comprising more than 80,000 compounds 59 different end points (122,572 points). The prediction capabilities developed single-task multi-end point models well novel analysis approach use Convolutional Neural Networks (CNN) are discussed. results show that MCDs significantly improves them CNN-1D yields best result (R2train = 0.93, R2ext 0.70). Several structural features showed high level contribution toxicity, including van der Waals surface area (VSA), number nitrogen-containing fragments (nN+), presence S–P fragments, ionization potential, C–N fragments. can be very useful tools predict under conditions, enabling quick assessment new compounds.

Язык: Английский

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The effects of water spray characteristics on the performance of a photovoltaic panel

Journal of Thermal Analysis and Calorimetry, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 18, 2024

Язык: Английский

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Sustainable solutions for healthcare facilities: examining the viability of solar energy systems

Frontiers in Energy Research, Год журнала: 2023, Номер 11

Опубликована: Июль 28, 2023

The healthcare sector is responsible for a significant portion of global carbon dioxide emissions, accounting approximately 5% the total. As energy demand in continues to rise, sustainable solutions are urgently needed. Hospitals and facilities require range engineering services, including heat ventilation air conditioning systems, hot domestic water supply backup electricity systems. These energy-intensive services offer an excellent opportunity integrate renewable sources reduce footprint facilities. This study presents case hospital located Gulf Cooperation Council (GCC) that utilizes solar-collected water-heated system. research aims investigate impact adding multi-solar collector photovoltaic systems facilities, analyze system’s thermodynamic efficiency terms exergy, assess its technical economic viability, gauge adoption rate solar by departments. results demonstrate thermal system provides around 12% total needed system, while PV contributes 29.6% load HVAC explores potential using GCC region, analyzing their technical, thermodynamic, viability. It promotes Middle East identifies gaps related implementation GCC. highlights benefits efficiency, cost savings, environmental sustainability, with implications region beyond. By utilizing can contribute more future.

Язык: Английский

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Detection of Modal Numbers from Field Configurations in Rectangular Waveguides via Machine Learning Models of Noisy Datasets

Опубликована: Янв. 26, 2024

We propose a machine learning (ML) modeling methodology to predict the propagation mode number of electromagnetic (EM) fields inside metallic rectangular waveguide based on field configuration in cross-section, presence noise. consider Transverse Electric (TEmn) modes and assume m n range 0 2 waveguides, where magnitude phase noiseless configurations are obtained from analytical solution electric vector E. generate training/testing datasets that includes 64,000 plots E over spanning various TE frequency 13-17 GHz. Our for training evaluation is classification model, relies primarily Stochastic Gradient Descent (SGD) k-Nearest Neighbors. For real-world scenarios which include noise, we introduce two random distributions datasets; specifically, exponential Gaussian added onto computed E-fields further challenge ML model. discuss limitations proposed approach challenges finding optimal model these types problems. The may be generalized both Magnetic (TMmn) numbers with wide ranges n, as well other waveguides; e.g., circular, elliptical, etc.

Язык: Английский

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Detection of Modal Numbers From Field Configurations in Rectangular Waveguides via Machine Learning Models of Noisy Datasets

IEEE Access, Год журнала: 2024, Номер 12, С. 50623 - 50632

Опубликована: Янв. 1, 2024

We propose a machine learning (ML) modeling methodology to predict the propagation mode number of electromagnetic (EM) fields inside metallic rectangular waveguide based on field configuration in cross-section, presence noise. consider xmlns:xlink="http://www.w3.org/1999/xlink">Transverse Electric ( xmlns:xlink="http://www.w3.org/1999/xlink">TE_mn ) modes and assume xmlns:xlink="http://www.w3.org/1999/xlink">m xmlns:xlink="http://www.w3.org/1999/xlink">n range 0 2 waveguides, where magnitude phase noiseless configurations are obtained from analytical solution electric vector xmlns:xlink="http://www.w3.org/1999/xlink">E . generate training/testing datasets that includes 64,000 plots over spanning various TE frequency 13-17 GHz. Our for training evaluation is classification model, relies primarily xmlns:xlink="http://www.w3.org/1999/xlink">Stochastic Gradient Descent (SGD) xmlns:xlink="http://www.w3.org/1999/xlink">k-Nearest Neighbors For real-world scenarios which include noise, we introduce two random distributions datasets; specifically, exponential Gaussian added onto computed E-fields further challenge ML model. discuss limitations proposed approach challenges finding optimal model these types problems. The may be generalized both Magnetic xmlns:xlink="http://www.w3.org/1999/xlink">TM_mn numbers with wide ranges , as well other waveguides; e.g., circular, elliptical, etc.

Язык: Английский

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