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Prediction model of primary solar resource with the use of Machine Learning, current results and future challenges
Palabras clave:
Machine Learning, Predictive Model, ,Forecasting, Solar Radiation. (en)Descargas
The use of Machine Learning (ML) techniques in the quantification-prediction of variable primary resource presents an advantage over traditional deterministic methods, the data processing methodology is fundamental for the quantification-prediction of variable natural resources such as solar radiation and wind speed. Being a resource of variable nature changes with geography, leading to a change in the performance of solar photovoltaic (PV) systems. In Colombia there are five natural geographical regions: the plains, mountains, jungle and two coasts, so the climatic conditions in Colombia are variable, which directly impacts the primary solar resource. A Machine Learning model is proposed for the prediction of solar radiation with a daily forecast prediction that considers the geolocation and the relationships between the primary resource and the typical climate data of the cities that represent the varied climate of the country.
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Derechos de autor 2023 Simposio Internacional sobre la Calidad de la Energía Eléctrica - SICEL
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
