AI-Enhanced Solar Panel Cleaning Robots: Data Augmentation and Predictive Algorithms for Maximizing Efficiency

Abstract

This study aims to enhance solar energy performance by transforming the Solar Photovoltaic (PV) panels' cleaning robot into an Artificial Intelligence (AI) cleaning robot, utilizing advanced algorithms for predictive, autonomous cleaning, and real-time monitoring. A proposed approach is to optimize solar panel efficiency, focusing on the impact of data augmentation on model performance. This approach employs ensemble learning techniques renowned for their precision and reliability in critical applications. Results were based on comparing original and augmented data performance, showing the impact of features' importance and data augmentation in model performance. This investigation demonstrates the practical implications of integrating AI-driven solutions into real-world solar energy management scenarios.