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Journals / Journal of Anatolian Environmental and Animal Sciences / 2022 / Cilt: 7 Sayı: 1

Monthly Average Wind Speed Forecasting in Giresun Province with Fuzzy Regression Functions Approach

Bulanık Regresyon Fonksiyonları Yaklaşımı ile Giresun İli Aylık Ortalama Rüzgâr Hızı Tahmini

Abdullah YILDIRIM
Eren BAŞ
Pages
27–32
Publication Date
2022-03-31
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result of the evaluations, it is concluded that the forecasts obtained by the fuzzy regression functions approach are superior than some other methods in the literature.

📌 3 Key Takeaways

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Objective & Hypothesis:In recent years, fuzzy inference systems have been used as an effective method for forecasting problems instead of classical time series methods.
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Key Findings & Data:To evaluate the performance of the fuzzy regression functions approach used to forecast monthly average wind speed in Giresun Province, the results obtained from many methods suggested in the literature for forecasting problems are compared.
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Conclusion & Impact:As a result of the evaluations, it is concluded that the forecasts obtained by the fuzzy regression functions approach are superior than some other methods in the literature.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Bulanık çıkarım sistemleri · Bulanık regresyon fonksiyonları yaklaşımı · Öngörü

Abstract

In recent years, fuzzy inference systems have been used as an effective method for forecasting problems instead of classical time series methods. Fuzzy inference systems are based on fuzzy sets and use membership values as well as the original data. The fuzzy regression functions approach, which is one of the popular fuzzy inference systems, has different importance from many fuzzy inference systems with its features that it does not have a rule base and is easier to apply, unlike many fuzzy inference systems in the literature. In this study, both the monthly average wind speed forecasting of Giresun Province is performed for the first time in the literature and the fuzzy regression functions approach method is used for the first time in the literature for wind speed forecasting. To evaluate the performance of the fuzzy regression functions approach used to forecast monthly average wind speed in Giresun Province, the results obtained from many methods suggested in the literature for forecasting problems are compared. As a result of the evaluations, it is concluded that the forecasts obtained by the fuzzy regression functions approach are superior than some other methods in the literature.

Özet

Son yıllarda öngörü problemleri için klasik zaman serisi yöntemleri yerine bulanık çıkarım sistemleri etkin bir yöntem olarak kullanılmaya başlanmıştır. Bulanık çıkarım sistemleri, bulanık kümelere dayalıdır ve orijinal verilerin yanı sıra üyelik değerlerini de kullanır. Popüler bulanık çıkarım sistemlerinden biri olan bulanık regresyon fonksiyonları yaklaşımı, literatürdeki birçok bulanık çıkarım sisteminden farklı olarak kural tabanına sahip olmaması ve uygulanmasının daha kolay olması özellikleriyle birçok bulanık çıkarım sisteminden farklı bir öneme sahiptir. Bu çalışmada hem literatürde ilk kez Giresun ilinin aylık ortalama rüzgâr hızı tahmini yapılmakta hem de rüzgâr hızı tahmini için literatürde ilk kez bulanık regresyon fonksiyonları yaklaşımı yöntemi kullanılmaktadır. Giresun ili aylık ortalama rüzgar hızını tahmin etmek için kullanılan bulanık regresyon fonksiyonları yaklaşımının performansını değerlendirmek için, öngörü problemleri için literatürde önerilen birçok yöntemden elde edilen sonuçlar karşılaştırılmıştır. Yapılan değerlendirmeler sonucunda, bulanık regresyon fonksiyonları yaklaşımı ile elde edilen tahminlerin literatürdeki diğer birçok yöntemden daha üstün olduğu sonucuna varılmıştır.

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Cite This Article

YILDIRIM, A., BAŞ, E. (2022). Monthly Average Wind Speed Forecasting in Giresun Province with Fuzzy Regression Functions Approach. Journal of Anatolian Environmental and Animal Sciences, Cilt: 7 Sayı: 1, 27–32. https://ajindex.com/articles/907575

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