结合风向与降水效应的光伏组件温度预测模型

This study presents an innovativeapproach to estimate the operating temperature of photovoltaic modules by incorporating underexplored climatic factors, such as wind direction and precipitation, in addition to commonly analyzed variables, such as ambient temperature, wind speed, solar irradiance, and relative humidity. The research addresses a gap in the literature, improving the predictive accuracy of photovoltaic module temperature estimation models. The developed methodology is designed to integrate measurement data from any location and was validated using data collected from over two years of measurements, demonstrating that the resulting prediction model is both valid and precise. The methodology employs multiple linear regression to derive the predictive model, ensuring adaptability and accuracy across different environmental contexts. Results indicate a significant improvement in prediction performance compared to other models. This advancement supports better design and operation of distributed photovoltaic systems globally.