Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting

Vanessa María Serrano Ardila, Joylan Nunes Maciel, Jorge Javier Gimenez Ledesma and Oswaldo Hideo Ando Junior
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Vanessa María Serrano Ardila: Latin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil
Joylan Nunes Maciel: Latin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil
Jorge Javier Gimenez Ledesma: Latin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil
Oswaldo Hideo Ando Junior: Research Group on Energy & Energy Sustainability (GPEnSE), Cabo de Santo Agostinho 54518-430, PE, Brazil

Energies, 2022, vol. 15, issue 3, 1-21

Abstract: Solar photovoltaic energy has experienced significant growth in the last decade, as well as the challenges related to the intermittency of power generation inherent to this process. In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS). Two FTS methods are proposed and evaluated to obtain a global horizontal irradiance (GHI) value. The first is the weighted method and the second is the fuzzy information granular method. Using the direct proportionality of the power with the GHI, the spatial smoothing process was applied, obtaining spatial irradiance on which a first-order low pass filter was applied to simulated power photovoltaic system generation. Thus, this study proposed indirect and direct forecasting of solar photovoltaic generation which was statistically evaluated and the results showed that the indirect prediction showed better performance with GHI than the power simulation. Error statistics, such as RMSE and MBE, show that the fuzzy information granular method performs better than the weighted method in GHI forecasting.

Keywords: fuzzy time series; photovoltaic energy prediction; short-term forecasting (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 complete reference list from CitEc
Citations: View citations in EconPapers (3)

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