Publication: Tracking cloud movement from solar irradiation measurements
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2018
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Abstract
En el siglo XXI, la humanidad tiene enormes problemas con la producción de energía. La alta demanda y el uso de energías basadas en residuos fósiles lleva al calentamiento global y la contaminación del aire, la tierra y agua. La energía nuclear también genera mucha controversia tras los accidentes que causaron gran devastación ambiental. Una de las "balas de plata" es la producción de electricidad mediante "fuentes verdes" como viento, aguao o el Sol. Cada uno de ellos tiene sus pros y sus contras. Hoy en día el Sol se está convirtiendo en una de las principales fuentes de energía. Es renovable, respetuoso del medio ambiente y su coste de producción ha disminuido notablemente gracias a la continua mejora de eficiencia de los paneles solares.
Uno de los factores que impiden una mayor difusión de la energía solar es su carácter impredecible. Los actores implicados en la producción y distribución de electricidad necesitan estimaciones precisas de la cantidad de energía que se generará en las próximas horas para satisfacer de forma efectiva la demanda prevista. Asi mismo, la gestión de las plantas solares mejoraría con una previsión fiable de la irradación solar en plazos aún más cortos.
Así, en este trabajo, trataré de detectar nubes y determinar su dirección y velocidad. Las nubes filtran gran parte de la radiación solar que llega la superficie, derrumbándose la producción eléctrica, especialmente en paneles fotovoltaicos. Por tanto, automatizar el seguimiento y predicción del movimiento de las nubes es de gran utilidad para la predicción de la energía solar.
In the XXI century humankind has massive problems with producing energy due to the global warming, air, earth, water pollution and "little bit" standing aside from nuclear energy after accidents which caused great environmental devastation. One of the "silver bullets" is producing electricity from "green sources" like wind, water, the Sun. Each of them has its pros and cons. Nowadays the Sun is becoming one of the main power sources. It is renewable, eco-friendly and what is more, does not require many expenses to maintain full functionality. Additionally every year the efficiency of solar panels increases. One of the main stoppers for solar energy to become widespread is its inherent uncertainty. Companies producing and distributing electricity require accurate estimates of the energy to be produced in the incoming hours in order to efficiently meet user demands. Even shorter forecast horizons are required to operate solar plants. Thus, in this thesis, I will try to detect clouds and determine its direction and speed. Clouds filter most of solar radiation, heavily impacting solar energy production. Therefore, tracking and predicting clouds movements is of great value for solar energy forecasting.
In the XXI century humankind has massive problems with producing energy due to the global warming, air, earth, water pollution and "little bit" standing aside from nuclear energy after accidents which caused great environmental devastation. One of the "silver bullets" is producing electricity from "green sources" like wind, water, the Sun. Each of them has its pros and cons. Nowadays the Sun is becoming one of the main power sources. It is renewable, eco-friendly and what is more, does not require many expenses to maintain full functionality. Additionally every year the efficiency of solar panels increases. One of the main stoppers for solar energy to become widespread is its inherent uncertainty. Companies producing and distributing electricity require accurate estimates of the energy to be produced in the incoming hours in order to efficiently meet user demands. Even shorter forecast horizons are required to operate solar plants. Thus, in this thesis, I will try to detect clouds and determine its direction and speed. Clouds filter most of solar radiation, heavily impacting solar energy production. Therefore, tracking and predicting clouds movements is of great value for solar energy forecasting.
Description
Máster en Ingeniería Informática, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, curso 2017-2018