A comprehensive analysis of advanced solar panel productivity and efficiency through numerical models and emotional neural networks.

This study presents an in-depth analysis and evaluation of the performance of a standard 200 W solar cell, focusing on the energy and exergy aspects. A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. To address this gap, a numerical model alongside a novel EANN was employed to simulate the system's electrical characteristics, including open-circuit voltage, short-circuit current, system resistances, maximum power point characteristics, and characteristic curves.

Experimental study on the various varieties of photovoltaic panels (PVs) cooling systems to increase their electrical efficiency.

This study investigates the impact of cooling methods on the electrical efficiency of photovoltaic panels (PVs). The efficiency of four cooling techniques is experimentally analyzed. The most effective approach is identified as water-spray cooling on the front surface of PVs, which increases efficiency by 3.

Performance analysis of snail shell biomaterials in solar still for clean water production: nature-inspired innovation for sustainability.

In this current investigation, the experimental performance of a solar still basin was significantly enhanced by incorporating snail shell biomaterials. The outcomes of the snail shell-augmented solar still basin (SSSS) are compared with those of a conventional solar still (CSS). The utilization of snail shells proved to facilitate the reduction of saline water and enhance its temperature, thereby improving the productivity of the SSSS.

Solar desalination system for fresh water production performance estimation in net-zero energy consumption building: A comparative study on various machine learning models.

This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination () in predicting freshwater production and vapor temperatures.