Estudio de la degradación del campo solar
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2021
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Abstract
La energía no renovable se ha utilizado durante muchos años, pero los recursos de la Tierra son limitados. La energía no renovable provoca daños sobre el planeta favoreciendo al cambio climático, por lo que en las últimas décadas se está apostando por las energías renovables.
La energía renovable es la solución ante muchos problemas que se causan en el medio ambiente utilizando energía no renovable. Una de las energías renovables más punteras es la energía fotovoltaica la cual permite obtener energía eléctrica a través de la radiación solar.
A pesar de que la energía solar es una energía limpia que no produce contaminación, la vida útil de los paneles solares es limitada ya que estos se van degradando con el paso del tiempo.
Por tanto, es interesante conocer el rendimiento de los campos solares para conocer cuánto se han degrado los campos solares y saber si están cumpliendo la degradación anual establecida en la garantía del fabricante. Esto va a ayudar a no desechar módulos que aún funcionen correctamente, y así evitar que se creen gran cantidad de residuos.
Como la energía solar depende en gran medida de factores ambientales, se utilizan sensores para conocer datos de radiación y temperatura. Estos sensores van a permitir predecir cuál es la potencia antes de instalar una planta solar. En este trabajo se muestra cómo, utilizando modelos de aprendizaje automático se puede obtener el rendimiento de los campos solares utilizando gran cantidad de datos muestreados con los sensores.
Además, se va a presentar un método que permite mediante el análisis de datos, comparar el rendimiento entre varios años y estudiar si esa diferencia corresponde con la degradación del campo solar.
Non-renewable energy has been used for many years, but the Earth's resources are limited. Non-renewable energy causes damage to the planet and contributes to climate change. Therefore, in recent decades, renewable energy has been the solution to many problems. Renewable energy is the solution to many problems that are caused in the environment using non-renewable energy. One of the most advanced renewable energies is photovoltaic energy, which allows to obtain electrical energy through solar radiation. Although solar energy is a clean energy that does not produce pollution, the useful life of solar fields is limited because they degrade over time. Thus, it is interesting to know the performance of the solar fields to know how much the solar fields have degraded and to know if they are complying with the guarantee indicated by the manufacturer. This will help to avoid discarding modules that are still working properly, and thus avoid creating a large amount of waste. As solar energy depends to a large extent on environmental factors, sensors are used to determine radiation and temperature data. These sensors will make it possible to predict the power output before installing a solar plant. By means of machine learning models, the performance of solar fields can be obtained using a large amount of data sampled with the sensors. On the other hand, by processing and analyzing the data, we compare the performance between several years and study whether this difference corresponds to the degradation of the solar field.
Non-renewable energy has been used for many years, but the Earth's resources are limited. Non-renewable energy causes damage to the planet and contributes to climate change. Therefore, in recent decades, renewable energy has been the solution to many problems. Renewable energy is the solution to many problems that are caused in the environment using non-renewable energy. One of the most advanced renewable energies is photovoltaic energy, which allows to obtain electrical energy through solar radiation. Although solar energy is a clean energy that does not produce pollution, the useful life of solar fields is limited because they degrade over time. Thus, it is interesting to know the performance of the solar fields to know how much the solar fields have degraded and to know if they are complying with the guarantee indicated by the manufacturer. This will help to avoid discarding modules that are still working properly, and thus avoid creating a large amount of waste. As solar energy depends to a large extent on environmental factors, sensors are used to determine radiation and temperature data. These sensors will make it possible to predict the power output before installing a solar plant. By means of machine learning models, the performance of solar fields can be obtained using a large amount of data sampled with the sensors. On the other hand, by processing and analyzing the data, we compare the performance between several years and study whether this difference corresponds to the degradation of the solar field.
Description
Trabajo de Fin de Máster en internet de las Cosas, Facultad de Informática UCM, Departamento de Sistemas Informáticos y Computación, Curso 2020/21.