Publication: Short-term solar irradiation from a sparse pyranometer network
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2018
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Tenllado van der Reijden, Christian
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Abstract
La predicción precisa de la radiación solar es necesaria para estimar correctamente la producción de energía de los sistemas solares fotovoltaicos y su integración en la red eléctrica. Este trabajo explora hasta qué punto las técnicas de Machine Learning pueden ser utilizadas para resolver este problema. La meta es predecir la radiación a corto plazo para un objetivo con varios horizontes de predicción. El objeto de las predicciones es una de las 22 estaciones de redes de piranómetros difusos con observaciones de muestra de resolución 30’. Se analizan las prestaciones y limitaciones de un modelo de Support Vector Machine simple y dos conjuntos de métodos de aprendizaje más sofisticados – Random Forest Regression y Gradient Boosting. Se muestra que todos ellos funcionan bien en condiciones climáticas constantes pero no realizan pronósticos fiables durante días en que las condiciones climáticas cambian rápidamente. Una selección inteligente de funciones es útil para hacer que el modelo sea más eficiente y rápido sin necesariamente mejorar significativamente la fiabilidad de los resultados. Con modelos agregados para escenarios específicos, se debe prestar atención a seguir algunas reglas para no aumentar innecesariamente la complejidad del modelo a expensas de la generalización de nuevos datos. Los modelos de entrenamiento en pequeñas cantidades de datos preseleccionados pueden causar sobreajuste o overfitting.
Accurate forecasting of solar irradiance is necessary for correct estimates of the energy output of solar photovoltaic systems and their integration into the power grid. This paper explores to which extent Machine Learning techniques can be applied to solve this problem. The objective is to predict short-term radiation for a target with various forecast horizons. The target is one of 22 stations of a sparse pyranometer network with sample observations of 30’ resolution. The performance and limitations of a simple Support Vector Machine model and two more sophisticated ensemble learning methods – Random Forest Regression and Gradient Boosting are analyzed. It is shown that all of them perform well in steady weather conditions but fail to make reliable predictions for days with rapid weather changes. A smart feature selection proves useful to make the model more efficient and faster without significantly improving the reliability of the predictions. With aggregated models for specific scenarios one has to pay attention to follow some rules in order not to unnecessarily increase the complexity of the model at the expense of generalization on new data. Training models on small preselected data may cause overfitting.
Accurate forecasting of solar irradiance is necessary for correct estimates of the energy output of solar photovoltaic systems and their integration into the power grid. This paper explores to which extent Machine Learning techniques can be applied to solve this problem. The objective is to predict short-term radiation for a target with various forecast horizons. The target is one of 22 stations of a sparse pyranometer network with sample observations of 30’ resolution. The performance and limitations of a simple Support Vector Machine model and two more sophisticated ensemble learning methods – Random Forest Regression and Gradient Boosting are analyzed. It is shown that all of them perform well in steady weather conditions but fail to make reliable predictions for days with rapid weather changes. A smart feature selection proves useful to make the model more efficient and faster without significantly improving the reliability of the predictions. With aggregated models for specific scenarios one has to pay attention to follow some rules in order not to unnecessarily increase the complexity of the model at the expense of generalization on new data. Training models on small preselected data may cause overfitting.
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Universidad Complutense, Facultad de Informática, curso 2017/2018