Improving Solar Energy System Performance Using Artificial Intelligence (AI)

Abstract

Artificial intelligence can be used to analyze data related to the performance of solar panels, such as temperature, solar radiation, and humidity level, and this is done by collecting this data from sensors located in solar panels and feeding it into machine learning models for analysis. Thus, artificial intelligence can identify conditions that affect the performance of solar panels, such as pollution, dust accumulations, or structural damage, as well as provide feedback for maintenance and cleaning to maintain the maximum benefit from the generated solar energy. It can also be used to analyze data about the performance of solar panels and predict their future performance. Predictions can be made from data, artificial neural networks (ANNs) for machine learning, and long short-term memory (LSTM) networks for deep learning and used to estimate the performance of solar panels under different conditions, such as changes in solar radiation or temperature; then improve the use of solar energy and increase its efficiency. Artificial intelligence can be used in image analysis and optical inspection to evaluate the performance of solar energy systems. It can be used to analyze satellite images, determine the efficiency of solar panels, monitor the condition of solar stations, and verify any defects that may affect the performance of the systems. Deep learning DL algorithms use complex nonlinear combination functions to manipulate data. The research included some deep learning DL techniques, such as deep convolutional neural networks (CNN), long short-term memory (LSTM), and generative adversarial networks (GAN), as deep learning is the most widely used in detecting and diagnosing defects in renewable energy. The research seeks to improve the performance of solar energy systems by linking them to artificial intelligence and activating their application in Iraqi industry.