Automated Smart Solar Panel System Fault Detection and Energy for Solar Panels Using Convolutional Neural Networks (CNN) and Deep Learning
DOI:
https://doi.org/10.52783/jns.v14.3525Keywords:
Solar Fault Detection, Energy Consumption Prediction, Computer Vision, Convolutional Neural Networks CNNs), Time-Series Analysis.Abstract
Employing a combination of machine learning, deep learning, and computer vision techniques for detection and energy usage predictions. Two Convolutional Neural Network (CNN)-based models are included in the system: one is intended to identify flaws including dust, cracks, and shading, while the other is intended to detect the existence of solar panels. To identify and categorize fault types and their severity, CNN models scan high-resolution pictures obtained through continuous monitoring. A regression-based machine learning model is used to forecast future energy output by utilizing environmental variables and past data to predict energy consumption. Long-term energy forecasts are further improved by time-series analysis, which makes maintenance and optimization tactics more successful. The Flask framework is used to create the solution, and a MySQL database is used to store maintenance records, energy forecasts, and fault detection logs. Scalable, real-time solar farm monitoring is supported by this integrated system, which lowers operating expenses and boosts output. This research aids in the effective and sustainable management of solar energy by integrating fault detection and energy forecasts into a single framework
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