Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning

Monica Borunda (), Adrián Ramírez, Raul Garduno, Gerardo Ruíz, Sergio Hernandez and O. A. Jaramillo
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Monica Borunda: CONACYT-Tecnológico Nacional de México -Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca 62490, Mexico
Adrián Ramírez: Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico
Raul Garduno: Instituto Nacional de Electricidad y Energias Limpias, Cuernavaca 62490, Mexico
Gerardo Ruíz: Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico
Sergio Hernandez: Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico
O. A. Jaramillo: Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco 62580, Mexico

Energies, 2022, vol. 15, issue 23, 1-25

Abstract: Solar energy currently plays a significant role in supplying clean and renewable electric energy worldwide. Harnessing solar energy through PV plants requires problems such as site selection to be solved, for which long-term solar resource assessment and photovoltaic energy forecasting are fundamental issues. This paper proposes a fast-track methodology to address these two critical requirements when exploring a vast area to locate, in a first approximation, potential sites to build PV plants. This methodology retrieves solar radiation and temperature data from free access databases for the arbitrary division of the region of interest into land cells. Data clustering and probability techniques were then used to obtain the mean daily solar radiation per month per cell, and cells are clustered by radiation level into regions with similar solar resources, mapped monthly. Simultaneously, temperature probabilities are determined per cell and mapped. Then, PV energy is calculated, including heat losses. Finally, PV energy forecasting is accomplished by constructing the P 50 and P 95 estimations of the mean yearly PV energy. A case study in Mexico fully demonstrates the methodology using hourly data from 2000 to 2020 from NSRDB. The proposed methodology is validated by comparison with actual PV plant generation throughout the country.

Keywords: clustering; machine learning; solar resource assessment; photovoltaic energy forecasting; regional P 50 and P 95 forecasts (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 (5)

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