Machine Learning for Solar Resource Assessment Using Satellite Images

Luis Eduardo Ordoñez Palacios, Víctor Bucheli Guerrero and Hugo Ordoñez
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Luis Eduardo Ordoñez Palacios: Escuela de Ingeniería de Sistemas y Computación (EISC), Facultad de Ingeniería, Universidad del Valle, Cali 760001, Colombia
Víctor Bucheli Guerrero: Escuela de Ingeniería de Sistemas y Computación (EISC), Facultad de Ingeniería, Universidad del Valle, Cali 760001, Colombia
Hugo Ordoñez: Departamento de Sistemas, Facultad de Electrónica y Telecomunicaciones, Universidad del Cauca, Popayán 190001, Colombia

Energies, 2022, vol. 15, issue 11, 1-13

Abstract: Understanding solar energy has become crucial for the development of modern societies. For this reason, significant effort has been placed on building models of solar resource assessment. Here, we analyzed satellite imagery and solar radiation data of three years (2012, 2013, and 2014) to build seven predictive models of the solar energy obtained at different altitudes above sea level. The performance of four machine learning algorithms was evaluated using four evaluation metrics, MBE, R 2 , RMSE, and MAPE. Random Forest showed the best performance in the model with data obtained at altitudes below 800 m.a.s.l. The results achieved by the algorithm were: 4.89, 0.82, 107.25, and 41.08%, respectively. In general, the differences in the results of the machine learning algorithms in the different models were not very significant; however, the results provide evidence showing that the estimation of solar radiation from satellite images anywhere on the planet is feasible.

Keywords: satellite imagery; meteorological data; renewable energy; photovoltaic systems; predictive model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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