Wind Speed Prediction in Bingol Region: A Deep Learning and Stacking Ensemble Approach

Abstract

This study aims to develop a novel model for wind speed prediction by integrating advanced deep learning techniques with ensemble methods using wind speed data collected from various districts of the Bingol region. The methodology includes rigorous data preprocessing, time‐based feature engineering, STL decomposition, and standardization – all mathematically modeled. A hybrid deep learning model comprising Conv1D, LSTM, and attention mechanisms is implemented alongside a stacking ensemble approach that integrates predictions from Ridge, Random Forest, XGBoost, LightGBM, CatBoost, SVR, and MLP regressors. Model performance is evaluated using RMSE, MAE, R², and EVS, with each district’s data supported by specific mathematical analyses.

Keywords

• Wind speed
• Deep learning
• LSTM
• STL decomposition

Bingöl Bölgesinde Rüzgar Hızı Tahmini: Derin Öğrenme ve Katmanlı Bütünleşik Yaklaşım

Abstract

Bu çalışma, Bingöl bölgesinin çeşitli ilçelerinden toplanan rüzgar hızı verilerini kullanarak, ileri düzey derin öğrenme tekniklerini ensemble yöntemleriyle entegre eden yenilikçi bir rüzgar hızı tahmin modeli geliştirmeyi amaçlamaktadır. Metodoloji, titiz veri ön işleme, zamana dayalı özellik mühendisliği, STL ayrıştırması ve standardizasyonu içermekte olup, tümü matematiksel olarak modellenmiştir. Conv1D, LSTM ve dikkat mekanizmalarını içeren hibrit bir derin öğrenme modeli, Ridge, Random Forest, XGBoost, LightGBM, CatBoost, SVR ve MLP regresörlerinden elde edilen tahminleri birleştiren bir stacking ensemble yaklaşımıyla birlikte uygulanmıştır. Model performansı, RMSE, MAE, R² ve EVS kullanılarak değerlendirilmiş ve her ilçenin verileri belirli matematiksel analizlerle desteklenmiştir.

Keywords

• Rüzgar Hızı
• Derin öğrenme
• UKSB
• STL dönüşümü

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