<ul><li>A scientist in Sweden has developed a new hybrid local features-based method using thermographs to identify faulty solar panels.</li><li>The method achieved 98% training accuracy and 96.8% testing accuracy in monitoring PV systems.</li><li>It utilizes infrared thermography to capture thermographs and applies preprocessing techniques for improved quality.</li><li>Compared to other AI approaches, the proposed method showed high performance in identifying faulty panels.</li></ul>

Scientist develops machine-learning method to identify faulty solar panels

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