Problem detection of solar energy storage equipment based on deep learning and image recognition

doi:10.19799/j.cnki.2095-4239.2025.0726

Energy Storage Science and Technology ›› 2025, Vol. 14 ›› Issue (9): 3596-3598.doi: 10.19799/j.cnki.2095-4239.2025.0726

• Energy Storage Test: Methods and Evaluation • Previous Articles Next Articles

Problem detection of solar energy storage equipment based on deep learning and image recognition

Shaohui ZHONG()

School of Cyberspace Security, Changsha Institute of Technology, Changsha 410200, Hunan, China

Received:2025-08-08 Revised:2025-08-13 Online:2025-09-28 Published:2025-09-05

Abstract

Abstract:

With the widespread application of solar energy storage equipment, real-time monitoring of its operating status and problem detection have become crucial. This paper presents a method for detecting issues in solar energy storage equipment, which combines the relevant technologies and theoretical foundations of deep learning and image recognition. By collecting image data from the solar energy storage equipment, deep learning algorithms are utilized to process and analyze the images, achieving accurate identification and localization of potential problems. This method has a high detection accuracy and efficiency, providing strong support for the maintenance and management of solar energy storage equipment.

Key words: solar energy storage equipment, deep learning, image recognition, problem detection

CLC Number:

TP 391

Shaohui ZHONG. Problem detection of solar energy storage equipment based on deep learning and image recognition[J]. Energy Storage Science and Technology, 2025, 14(9): 3596-3598.

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Problem detection of solar energy storage equipment based on deep learning and image recognition

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