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

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Publicado en:Technologies vol. 13, no. 5 (2025), p. 182
Autor principal: Nava-Pintor, Jesús Antonio
Otros Autores: Alcalá-Rodríguez, Uriel E, Guerrero-Osuna, Héctor A, Mata-Romero, Marcela E, Lopez-Neri, Emmanuel, García-Vázquez Fabián, Solís-Sánchez, Luis Octavio, Carrasco-Navarro, Rocío, Luque-Vega, Luis F
Publicado:
MDPI AG
Materias:
Accuracy
Humidity
Deep learning
Agricultural production
Internet of Things
Forecasting
Environmental monitoring
Productivity
Machine learning
Performance evaluation
Irradiance
Radiation
Radiation detectors
Efficiency
Feasibility studies
Agriculture
Embedded systems
Low cost
Pyranometers
Solar energy
Artificial intelligence
Environmental conditions
Root-mean-square errors
Neural networks
Sensors
Illuminance
Alternative energy sources
Light
Radiation measurement
Solar radiation
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2227-7080
DOI:10.3390/technologies13050182
Fuente:Materials Science Database