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

Oswaldo Hideo Ando Junior,Joylan Nunes Maciel,Jorge Javier Gimenez Ledesma,Vanessa María Serrano Ardila

doi:10.3390/en15030845

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.

Highlights

Solar photovoltaic energy (SPE) is positioned as an energy source that contributes significantly to the diversification of the world’s energy matrix
This study evaluates the performance of the fuzzy time series (FTS) methods combined with the spatial smoothing method to solve the SPE generation forecasting problem considering the spatial dimension and characteristics of a specific photovoltaic system
This study developed two multivariable fuzzy time series (FTS) methods and evaluated their use in the indirect prediction of short-term photovoltaic power generation

Summary

Introduction

Solar photovoltaic energy (SPE) is positioned as an energy source that contributes significantly to the diversification of the world’s energy matrix. In the face of this growth, there are several challenges to consider in terms of the high penetration rates of PVS, being that this type of energy generation varies with the existence of a maximum generation limit that changes over time, from seconds to years [2], which is known as variability. This limit is not known with perfect precision, which is called uncertainty or error. The movement around the sun generates variability that can be predicted, while the variability associated with clouds can be difficult to predict, as well as the uncertainty due to difficulties in forecasting the behavior of weather conditions

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Conclusion