The Impact of Data Augmentation on AI-Driven Predictive Algorithms for Enhanced Solar Panel Cleaning Efficiency

Abstract

This study investigates the impact of data augmentation on predictive maintenance machine learning models for robot solar panel cleaning. Data augmentation techniques like synthetic data generation, time-series transformation (shifting, interpolation, and resampling), and extreme condition simulation were used to enhance data diversity and model generalization. Machine learning algorithms, including logistic regression, support vector machines, deep learning, and ensemble learning, were compared to identify their sensitivity to these techniques. Our experimental findings show that ensemble models (stacking and boosting) show the maximum improvement in predictive accuracy with the added benefit of higher diversity and strength in features. Deep learning models show moderate gains primarily in feature extraction, and simple models such as logistic regression show little impact, indicating the model-dependent effectiveness of data augmentation. Despite better generalization, ensemble methods are at the expense of increased computational cost, indicating a trade-off between accuracy and efficiency. The study employs widely used machine learning frameworks and libraries for data preprocessing, augmentation, model training, and evaluation, ensuring robust and scalable implementation.