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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Acknowledgments
This work arises from the project “Predicción del recurso solar usando imágenes satelitales para impulsar el desarrollo sostenible en comunidades aisladas con energía asequible y no contaminante” approved in the Proyecto Espacial Universitario (PEU) from UNAM. The authors wish to thank the PEU program for their support in the elaboration and publication of this work. M.B. also thanks CONACYT for her Catedra Research Position with ID 71557, and to INEEL for its hospitality.
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Borunda, M., Ramírez, A., Liprandi, N., Rodríguez, M., Sánchez, A. (2021). Seasonality Atlas of Solar Radiation in Mexico. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Computational Intelligence. MICAI 2021. Lecture Notes in Computer Science(), vol 13067. Springer, Cham. https://doi.org/10.1007/978-3-030-89817-5_11
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DOI: https://doi.org/10.1007/978-3-030-89817-5_11
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