Efficient Solar Panel Maintenance: A Data-Driven Strategy using Ant Colony Optimization and Innovative Cleaning Index

Addressing the pivotal challenge of enhancing power output in solar power plants through optimized cleaning schedules, this research introduces an innovative cleaning index. This index, which encompasses power output, cleaning penalties, and associated costs, becomes the focal point for the Ant Colony Optimization (ACO) algorithm to devise adaptive and efficient cleaning schedules. Leveraging a data-driven approach with an extensive dataset encompassing solar panel output voltage and environmental variables, the study seamlessly integrates ACO to navigate the intricate solution space. The proposed cleaning index not only acts as a sophisticated evaluation tool but also aligns with the dynamic nature of solar energy production. This research makes a significant contribution by providing a comprehensive view of cleaning schedule efficiency and showcasing the adaptability of ACO in response to dynamic environmental conditions. Real-world applicability is underscored by incorporating actual data, ensuring practical relevance and reliability. The study's findings offer actionable insights for optimizing solar panel performance, leading to reduced operational costs and minimized environmental impact. In conclusion, this work propels the field forward by presenting a robust and practical framework for the optimization of solar panel cleaning, promising substantial advancements in efficiency and sustainability within solar power generation.