Development and Evaluation of Solar Radiation Sensor Using Cost-Effective Light Sensors and Machine Learning Techniques

Abstract

The accurate measurement of solar radiation is essential for applications in agriculture, renewable energy, and environmental monitoring. Traditional pyranometers provide high-precision readings but are often costly and inaccessible for large-scale deployment. This study explores the feasibility of using low-cost ambient light sensors combined with statistical and machine learning models based on linear, random forest, and support vector regressions to estimate solar irradiance. To achieve this, an Internet of Things-based system was developed, integrating the light sensors with cloud storage and processing capabilities. A dedicated solar radiation sensor (Davis 6450) served as a reference, and results were validated against meteorological API data. Experimental validation demonstrated a strong correlation between sensor-measured illuminance and solar irradiance using the random forest model, achieving a coefficient of determination (R²) of 0.9922, a root mean squared error (RMSE) of 44.46 W/m², and a mean absolute error (MAE) of 27.12 W/m². These results suggest that low-cost light sensors, when combined with data-driven models, offer a viable and scalable solution for solar radiation monitoring, particularly in resource-limited regions.