Renewable energy generation forecasting in Turkey via intuitionistic fuzzy time series approach

Çakır, Süleyman

doi:10.1016/j.renene.2023.05.132

Cited by 13 publications

(1 citation statement)

References 51 publications

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“…The predictive model about fuzzy time series (FTS) has been applied to a variety of forecasting issues, including forecasts for Alabama university enrolment (S. M. Chen, 2002; S. M. Chen & Chung, 2006;Jeng et al, 2006;Song & Chissom, 1993;Tanuwijaya & Chen, 2009), stock price (M. Y. Chen & Chen, 2015; T. L. Chen et al, 2007;Cheng & Yang, 2018;Singh & Borah, 2014;Tavares et al, 2022), renewable energy (Çakır, 2023;Severiano et al, 2021), lumpy skin disease (Punyapornwithaya et al, 2023), temperature forecast (Lee et al, 2006), and portfolio returns forecast (Rubio et al, 2016), with the goal of lowering future uncertainty, also improving and streamlining planning.…”

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confidence: 99%

Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series

Vianita

2024

JTAM

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The improvement of plantation forecasting accuracy, particularly with regard to coffee production, was an essential aspect of earth observations for the purpose of informing plantation management alternatives. These decisions included strategic and tactical decisions on supply chain operations and financial decisions. Many research initiatives have used a variety of methodologies to the forecasting of plantation areas and related industries, such as coffee production. One of these methods was known as the fuzzy time series (FTS) technique. This study combined ratio-interval and frequency density to get universe of discourse and partition followed by adopted weighted and modified that weighted. The first step was defined universe of discourse using ratio-interval algorithm. The second step was partition the universe of discourse using ratio-interval algorithm followed by frequency density partitioning. The third step was fuzzyfication. The fourth step built fuzzy logic relationship (FLR) and fuzzy logic relationship group (FLRG). The fifth step was adopted the modification weighted. The last step was defuzzyfication. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 1.24% for proposed method.

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confidence: 99%

Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series

Vianita

2024

JTAM

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Quaternion Intuitionistic Fuzzy Fusion Process: Applications to the Classification of Photo-Voltic-Solar-Power Plants

Joshi,

Singh,

Singh

2024

Int. J. Fuzzy Syst.

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Intuitionistic fuzzy time series forecasting method based on dendrite neuron model and exponential smoothing

Cansu,

Bas,

Egrioglu

et al. 2024

Granul. Comput.

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Methods based on artificial neural networks for intuitionistic fuzzy time series forecasting can produce successful forecasting results. In the literature, exponential smoothing methods are hybridised with artificial neural networks due to their simple and efficient structures to improve the forecasting performance. The contribution of this paper is to propose a new forecasting approach combining exponential smoothing methods and intuitionistic fuzzy time series. In this study, a forecasting algorithm based on the dendrite neuron model and simple exponential smoothing methods is proposed for modelling intuitionistic fuzzy time series. In the fuzzification stage of the proposed method, the intuitionistic fuzzy c-means method is used. The proposed method is a modular method using two separate dendrite neuron model neural networks and the grey wolf optimisation algorithm is used to estimate all parameters of the method. The performance of the proposed method is applied on four different random time series obtained for Index of Coin Market Cap and the performance of the method is compared with some other fuzzy forecasting methods. As a result of the analyses, it is concluded that the proposed modular method has better forecasting results than other methods.

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Renewable energy generation forecasting in Turkey via intuitionistic fuzzy time series approach

Cited by 13 publications

References 51 publications

Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series

Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series

Quaternion Intuitionistic Fuzzy Fusion Process: Applications to the Classification of Photo-Voltic-Solar-Power Plants

Intuitionistic fuzzy time series forecasting method based on dendrite neuron model and exponential smoothing

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