Smart Solar Panel Maintenance: A Data-Driven Approach Using IoT and Machine Learning

This paper presents a solution for optimizing solar panel maintenance through a simple Arduino-based cleaning system. The system utilizes sensors to monitor environmental factors and trigger automated cleaning as needed. Additionally, a machine learning (ML) model forecasts future conditions and predicts cleaning requirements based on historical data, ensuring proactive maintenance that minimizes downtime and maximizes energy production. The paper demonstrates how this Arduino-based smart cleaning system is more efficient than manual cleaning of solar panels. It details the system’s design, implementation, experimental results, and economic implications, highlighting benefits such as reduced labor costs, lower water consumption, and increased energy efficiency. Furthermore, the paper discusses potential future advancements, including enhanced sensor integration and AI-powered tracking capabilities.