Abstract
—As solar energy becomes more widely used as an alternative sustainable power source, efficient maintenance techniques must be put in place to guarantee the long-term performance and financial sustainability of solar panel installations. This study presents a comprehensive framework for optimal maintenance scheduling aimed at minimizing the lifecycle expenses of solar panels while maximizing their efficiency and operational lifespan. The proposed framework integrates predictive maintenance techniques with real-time monitoring systems and advanced data analytics to forecast potential failures and optimize maintenance activities. The method involves mathematical modelling of dust build-up, energy output, and maintenance expenses in addition to data analysis using historical weather data and solar panel performance records. This research provides a financial analysis of lifecycle costs (LCC) over a 10-year period, where the system undergoes 19 cleaning interventions. Based on the analysis, the cost for purchasing energy from the main grid versus using solar panels amounts to R 576,199.90, while the cost of cleaning solar panels is R 91,855.00. This brings the total LCC to R 668,055.40 over 10 years. This research highlights the detrimental impact of dust accumulation on solar panel efficiency, showing that without regular cleaning, energy production drastically declines, and reach end of life cycle by the fifth year in a 10-year span. The life cycle cost (LCC) analysis demonstrated that maintaining solar panel systems, even with cleaning expenses, is far more cost-effective than relying solely on grid electricity. Keywords— Photovoltaic (PV) systems, solar energy, Model predictive control (MPC)