Robust wind speed estimation with modified fuzzy regression functions with a noise cluster

Chakravarty, Srinivas; Demirhan, Haydar; Baser, Furkan

doi:10.1016/j.enconman.2022.115815

Energy Conversion and Management

2022

DOI: 10.1016/j.enconman.2022.115815

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Robust wind speed estimation with modified fuzzy regression functions with a noise cluster

Srinivas Chakravarty

Haydar Demirhan

Furkan Baser

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Cited by 8 publications

(2 citation statements)

References 38 publications

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“…It can be concluded that the variable agnostic MFRFN framework developed in this study can be successfully generalised to predict dependent variables in large datasets by using the corresponding dependent variables in a regression scenario. Some of this research has been published in [1][2][3].…”

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Fuzzy Approaches Against Outliers and Applications in Wind Energy

Chakravarty

2023

Bull. Aust. Math. Soc.

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confidence: 99%

“…https://doi.org/10.1017/S0004972723000023 Published online by Cambridge University Press [3] Fuzzy approaches against outliers 3…”

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Fuzzy Approaches Against Outliers and Applications in Wind Energy

Chakravarty

2023

Bull. Aust. Math. Soc.

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Hierarchical fuzzy regression functions for mixed predictors and an application to real estate price prediction

Demirhan,

Baser

2024

Neural Comput & Applic

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Categorical features appear in datasets from almost every practice area, including real estate datasets. One of the most critical handicaps of machine learning algorithms is that they are not designed to capture the qualitative nature of the categorical features, leading to sub-optimal predictions for the datasets with categorical observations. This study focuses on a new fuzzy regression functions framework, namely hierarchical fuzzy regression functions, that can handle categorical features properly for the regression task. The proposed framework is benchmarked with linear regression, support vector machines, deep neural networks, and adaptive neuro-fuzzy inference systems with real estate data having categorical features from six markets. It is observed that the proposed method produces better prediction performance for real estate price prediction than the benchmark methods in a wide variety of real estate markets. Since we provide all the required software codes to implement the proposed hierarchical fuzzy regression functions framework, our approach offers practitioners a readily applicable, high-performing tool for real estate price prediction and other regression problems involving categorical independent features.

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A fuzzy Gaussian process regression function approach for forecasting problem

Egrioglu,

Bas,

Chen

2024

Granul. Comput.

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A fuzzy regression function approach is a fuzzy inference system method whose rules cannot be determined based on expert opinion, unlike a classical fuzzy inference system. In a fuzzy regression function approach, an input matrix consists of memberships obtained by the fuzzy clustering method and lagged variables of the time series. In the fuzzy regression function approach, the output vector corresponding to this input matrix is also created and the parameter estimation for the method is carried out with the ordinary least square method. As it is known, the ordinary least square method assumes that the data are linear. In addition, although it is very useful to include a priori information describing the formation of the data in the model, in most cases this information is not available. It is also inappropriate to use a model that does not accurately characterize the data. However, it is not appropriate to estimate parameters for nonlinear data using the ordinary least square method. One of the methods to be used in such a situation is the Gaussian process regression method. While the parameters of a selected basis function are fitted in the ordinary least squares regression method, how all measured data are related is determined in the Gaussian process regression. Besides, Gaussian process regression is a Bayesian approach, it can provide uncertainty measurements on forecasts. In this study, a fuzzy Gaussian process regression function is proposed. The contribution of this paper is to propose a new fuzzy inference system that can be used to solve nonlinear data by proposing a fuzzy Gaussian process regression function. The performance of the newly proposed method is evaluated based on the closing values of the Bitcoin and Crude oil time series. The performance comparison of the proposed method is evaluated with many different forecasting methods and it is concluded that the proposed method has superior forecasting performance.

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Robust wind speed estimation with modified fuzzy regression functions with a noise cluster

Cited by 8 publications

References 38 publications

Fuzzy Approaches Against Outliers and Applications in Wind Energy

Fuzzy Approaches Against Outliers and Applications in Wind Energy

Hierarchical fuzzy regression functions for mixed predictors and an application to real estate price prediction

A fuzzy Gaussian process regression function approach for forecasting problem

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