A Robust Modeling Analysis of Environmental Factors Influencing the Direct Current, Power, and Voltage of Photovoltaic Systems

Solar photovoltaic technology has become a cornerstone of the renewable energy sector over the last 20 years, yet its efficiency remains sensitive to environmental and operational conditions. This study rigorously analyzes how irradiance, temperature, humidity, wind speed, and soiling affect key electrical outputs—Direct current, power, and voltage—of solar panels using advanced robust regression methods: Ridge Regression, Least Absolute Deviation, and M-Estimation. Our results demonstrate that irradiance is the dominant driver of performance, with Ridge Regression coefficients reaching up to 1193 for power. The M-estimation model achieved high predictive accuracy, with R2 Scores of 0.989 for current (Mean Squared Error = 0.0399) and 0.991 for power (Mean Squared Error ≈ 10,445), indicating strong model reliability. voltage prediction was more challenging but still robust (R2 = 0.836, Mean Squared Error = 49.63). Negative impacts from ambient temperature and humidity were consistently observed across models, while wind speed exhibited a beneficial effect by enhancing cooling and thus improving current and power outputs. Soiling was also identified as a critical factor, significantly reducing power and voltage generation. These findings provide quantifiable evidence of how environmental variables shape solar photovoltaic performance and underscore the importance of environmental monitoring and maintenance strategies to optimize energy yield in operational solar power systems.