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Maroc Maroc - WN.COM - Science - 23/Oct 08:15

Machine learning-based fault detection technique for bifacial PV

A UAE research team developed a hybrid 1D-CNN and random forest model to detect multiple faults in bifacial PV systems, including dust, shading, aging, and cracks. Using simulated I-V curves and a 180-day synthetic dataset, the model achieved up to 100% accuracy in general state detection and 97.6% in specific fault classification. A research team led by the University of Sharjah in the United Arab Emirates has developed a novel machine learning approach for fault detection in bifacial PV systems. The method combines a one-dimensional convolutional neural network (1D-CNN) with the random forest (RF) ensemble learning method to identify specific...

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How dirt accumulation reduces the efficiency of solar panels over time

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The impact of dust on PV systems in arid coastal environments

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5,432°F: New ultrahigh temperature method can produce 6 times more clean hydrogen

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Wearable patch for early skin cancer detection developed

oncologynews.com.au - 31/Oct 15:48

Researchers at Wake Forest University School of Medicine, USA, have developed a ground-breaking, battery-free wearable patch that could help detect...

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Chinese research team sets efficiency record for next-generation solar cells

wn.com - 28/Oct 07:50

NANJING, Oct. 28 (Xinhua) -- A joint research team in China has developed a new method that, for the first time, has enabled a key type of advanced...

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