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Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 16 Sayı: 2

Comparative Analysis of Regression Models for Wind Energy Forecasting

Rüzgar Enerjisi Tahmini için Regresyon Modellerinin Karşılaştırmalı Analizi

Ziya Çetin0009-0004-1597-8471
Yıldırım Özüpak(DICLE UNIVERSITY)0000-0001-8461-8702
Emrah Aslan(MARDIN ARTUKLU UNIVERSITY)0000-0002-0181-3658
Pages
408–424
Publication Date
2026-06-01
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AI Summary & Key Takeaways

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

These findings show that power forecasting for wind-turbines can be made more efficient using machine learning techniques and that such models have significant potential in power generation forecasting.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, the performances of various regression models are compared for predicting the wind turbine generation power using SCADA data from a wind-turbine in Turkey.
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Key Findings & Data:The main objective of the study is to evaluate different machine learning algorithms to accurately predict the active power generation of the turbine under the influence of different parameters such as wind speed, wind direction, theoretical power curve.
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Conclusion & Impact:These findings show that power forecasting for wind-turbines can be made more efficient using machine learning techniques and that such models have significant potential in power generation forecasting.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Rüzgar Türbini · Güç Tahmini · Makine Öğrenmesi

Abstract

In this study, the performances of various regression models are compared for predicting the wind turbine generation power using SCADA data from a wind-turbine in Turkey. The main objective of the study is to evaluate different machine learning algorithms to accurately predict the active power generation of the turbine under the influence of different parameters such as wind speed, wind direction, theoretical power curve. For this purpose, a total of 10 different regression models including CatBoost, XGB, Extra Trees, Random Forest, Decision Tree, Gradient Boosting, XGBRF, Linear, SVR and AdaBoost are applied. The success of the models was evaluated by performance metrics such as R² score and RMSE. The results revealed that the CatBoost Regressor model provided the highest accuracy with an R² score of 98.25%, while the XGB Regressor and Extra Trees Regressor models performed well with R² scores of 98.14% and 97.76%, respectively. Furthermore, the weekly analysis revealed that wind speed and the theoretical power curve have a significant impact on the active power generation of the turbine, while wind-direction shows a lower relationship. These findings show that power forecasting for wind-turbines can be made more efficient using machine learning techniques and that such models have significant potential in power generation forecasting.

Özet

Bu çalışmada, Türkiye'deki bir rüzgar türbininden elde edilen SCADA verilerini kullanarak rüzgar türbini üretim gücünü tahmin etmek için çeşitli regresyon modellerinin performansları karşılaştırılmıştır. Çalışmanın ana amacı, rüzgar hızı, rüzgar yönü, teorik güç eğrisi gibi farklı parametrelerin etkisi altında türbinin aktif güç üretimini doğru bir şekilde tahmin etmek için farklı makine öğrenimi algoritmalarını değerlendirmektir. Bu amaçla, CatBoost, XGB, Extra Trees, Random Forest, Decision Tree, Gradient Boosting, XGBRF, Linear, SVR ve AdaBoost dahil olmak üzere toplam 10 farklı regresyon modeli uygulanmıştır. Modellerin başarısı, R² puanı ve RMSE gibi performans ölçütleri ile değerlendirilmiştir. Sonuçlar, CatBoost Regressor modelinin %98,25 R² puanı ile en yüksek doğruluğu sağladığını, XGB Regressor ve Extra Trees Regressor modellerinin ise sırasıyla %98,14 ve %97,76 R² puanları ile iyi performans gösterdiğini ortaya koymuştur. Ayrıca, haftalık analiz, rüzgar hızı ve teorik güç eğrisinin türbinin aktif güç üretimi üzerinde önemli bir etkiye sahip olduğunu, rüzgar yönünün ise daha düşük bir ilişki gösterdiğini ortaya koydu. Bu bulgular, rüzgar türbinleri için güç tahmininin makine öğrenimi teknikleri kullanılarak daha verimli hale getirilebileceğini ve bu tür modellerin güç üretimi tahmininde önemli bir potansiyele sahip olduğunu göstermektedir.

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Cite This Article

Çetin, Z., Özüpak, Y., Aslan, E. (2026). Comparative Analysis of Regression Models for Wind Energy Forecasting. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 16 Sayı: 2, 408–424. https://doi.org/10.21597/jist.1814809

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