Prediction of Wind Speed Using Tree-Based Ensemble Algorithms: CatBoost, HistGBM, and XGBoost

Year 2025, Volume: 9 Issue: 1, 145 - 150, 31.07.2025

İlker Mert

Abstract

In this study, three advanced tree-based machine learning models (XGBoost, HistGradientBoosting (HistGBM), and CatBoost) are compared for predicting wind speed (V (m/s)) in an urban area. A dataset covering four years is used to train the models, and their performance is evaluated, especially on the test data. The root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R^2), and P-value are used to evaluate the model's performance. XGBoost is the best amongst all the models with respect to RMSE, MAPE, and R^2 values, which are measured at 0.0416, 0.0089, and 0.9993, respectively. Next, we can have the second best as CatBoost with very successful results, having RMSE of 0.0843 and an R^2 value of 0.9972. The third model, with an RMSE of 0.1174, has an R^2 value of 0.9946. When the p-values are considered, then all estimates of the models is found to be statistically significant. The results indicate that the ensemble type modeling algorithms have very active performance for the time-series problems like estimations of V (m/s). Hence, the XGBoost method is found to be the most efficient and trustworthy for the V (m/s) estimation applications.

Keywords

Wind Speed Prediction , Tree-Based Ensemble Learning , XGBoost , CatBoost , Time Series Forecasting

Ağaç Tabanlı Topluluk Algoritmaları Kullanılarak Rüzgar Hızının Tahmini: CatBoost, HistGBM ve XGBoost

Abstract

Bu çalışmada, kentsel bir bölgede rüzgar hızının (V (m/s)) tahmini amacıyla üç gelişmiş ağaç tabanlı makine öğrenimi modeli (XGBoost, HistGradientBoosting (HistGBM) ve CatBoost) karşılaştırılmıştır. Modellerin eğitilmesinde dört yılı kapsayan bir veri seti kullanılmış ve performansları özellikle test verileri üzerinde değerlendirilmiştir. Model başarımını değerlendirmek için karekök ortalama kare hata (RMSE), ortalama mutlak yüzde hata (MAPE), belirleme katsayısı (R²) ve p-değeri kullanılmıştır. RMSE, MAPE ve R² değerleri sırasıyla 0.0416, 0.0089 ve 0.9993 olarak ölçülen XGBoost modeli, tüm modeller arasında en iyi performansı göstermiştir. İkinci en iyi model olan CatBoost, 0.0843 RMSE ve 0.9972 R² değeri ile oldukça başarılı sonuçlar vermiştir. Üçüncü model olan HistGBM ise 0.1174 RMSE ve 0.9946 R² değerine sahiptir. P-değerleri dikkate alındığında, tüm modellerin tahminlerinin istatistiksel olarak anlamlı olduğu görülmüştür. Elde edilen sonuçlar, topluluk türü modelleme algoritmalarının V (m/s) gibi zaman serisi problemleri için oldukça etkili performans sergilediğini göstermektedir. Bu bağlamda, XGBoost yöntemi V (m/s) tahmini uygulamaları için en verimli ve güvenilir yöntem olarak öne çıkmaktadır.

Keywords

Rüzgar Hızı Tahmini , Ağaç Tabanlı Topluluk Öğrenmesi , XGBoost , CatBoost , Zaman Serisi Tahmini

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