Akbal, Yıldırım

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Profile URL
https://hdl.handle.net/20.500.14411/8486
Name Variants
Akbal,Y.
Y.,Akbal
Akbal, Yıldırım
A., Yildirim
Yildirim, Akbal
A.,Yıldırım
Y., Akbal
A.,Yildirim
Yıldırım, Akbal
Akbal, Yildirim
Job Title
Doçent Doktor
Email Address
yildirim.akbal@atilim.edu.tr
Main Affiliation
Mathematics
Status
Former Staff
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Scholarly Output

7

Articles

7

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44

Scopus Citation Count

47

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6.29

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Canadian Journal of Mathematics1
Colloquium Mathematicum1
Communications in Algebra1
International Journal of Green Energy1
International Journal of Number Theory1
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Author: Unlu, Kamil DemirberkHas files: false

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    Article
    Citation - WoS: 23
    Citation - Scopus: 27
    A Univariate Time Series Methodology Based on Sequence-To Learning for Short To Midterm Wind Power Production
    (Pergamon-elsevier Science Ltd, 2022) Akbal, Yildirim; Unlu, Kamil Demirberk
    The biggest wind farm of Turkey is placed at Manisa which is located in the Aegean Region. Electricity is a nonstorable commodity for that reason, it is very important to have a strong forecast and model of the potential electricity production to plan the electricity loads. In this study, the aim is to model and forecast electricity production of the wind farms located at Manisa by using a univariate model based on sequence-to-sequence learning. The forecasting range of the study is from short term to midterm. The strength of the proposed model is that; it only needs its own lagged value to make forecasts. The empirical evidences show that the model has high coefficient of variation (R-2) in short term and moderate R-2 in the midterm forecast. Although in the midrange forecasts R-2 slightly decreases mean squared error and mean absolute error shows that the model is accurate also in the midterm forecasts. The proposed model is not only strong in hourly electricity production forecasts but with a slight modification also in forecasting the minimum, maximum and average electricity production for a fixed range. This study concludes with two fresh and intriguing future research ideas.