Fault Detection in Solar Panels Using Time Series Analysis and CNN

An automated and efficient way to check solar panels for flaws has become essential as the demand for solar energy systems grows worldwide. Our project takes a double approach to meeting this demand. First, we apply the convolutional neural network (CNN) VGG16 for image-based solar power panel fault identification. This technique uses transfer learning to detect surface irregularities with accuracy. Second, we use the ARIMA model to forecast possible problems and analyse power generation data to identify performance trends we demonstrate the application of ARIMA for time series analysis in solar power plants. The goal is to detect underperforming and faulty solar panels by analysing 30 days of power generation data. ARIMA is employed to forecast the expected power output of each panel, and deviations from the forecast indicate potential issues. This method ensures timely detection of malfunctioning panels, leading to improved maintenance efficiency and increased energy yield.