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2021 | OriginalPaper | Chapter

Seasonality Atlas of Solar Radiation in Mexico

Authors : Mónica Borunda, Adrián Ramírez, Nayeli Liprandi, Miriam Rodríguez, Alejandro Sánchez

Published in: Advances in Computational Intelligence

Publisher: Springer International Publishing

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Abstract

Due to the imminent climate-change emergency, it is urgent to boost the exploitation of renewable resources to produce clean energy, being solar energy one of the most promising ones. However, one of the greatest challenges that solar energy faces is its intermittency. Thus, to get the biggest benefit from this resource, especially for photovoltaic generation, it is required to predict its availability to estimate variations in energy production. As the first step for solar radiation forecasting, a seasonality analysis is mandatory to obtain better results. In this work, we perform a seasonality analysis of solar radiation in Mexico using Machine Learning. Specifically, we accomplish a cluster analysis of solar radiation data in locations representative of the different climate conditions in Mexico to obtain a seasonality atlas of the solar resource. Cluster analysis is performed with two algorithms, k-means and k-medoids. Finally, the Silhouette method is used to validate the results.

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previous chapter Finding Significant Features for Few-Shot Learning Using Dimensionality Reduction
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Metadata
Title
Seasonality Atlas of Solar Radiation in Mexico
Authors
Mónica Borunda
Adrián Ramírez
Nayeli Liprandi
Miriam Rodríguez
Alejandro Sánchez
Copyright Year
2021
Book
Advances in Computational Intelligence

Print ISBN: 978-3-030-89816-8

Electronic ISBN: 978-3-030-89817-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-89817-5_11

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