Ultra Kısa Vadeli Rüzgar Enerjisi Tahmininde Gelişmiş Optimizasyon ve Hibrit Derin Öğrenme Modellerinin Kullanımı: Hatay, Türkiye Örneği

Öz

Bu çalışma, Bu çalışma, ultra kısa vadeli rüzgar enerjisi tahmininde doğruluğu ve kararlılığı artırmak amacıyla tasarlanmış uçtan uca bir veri hattı ile hibrit derin öğrenme yaklaşımlarının performans karşılaştırmasını sunmaktadır. SCADA tabanlı çok değişkenli verilere, anomali giderme işlemi DBSCAN ile, özellik seçimi ise RFECV tabanlı olarak uygulanmıştır. Optuna gibi gelişmiş bir hiperparametre optimizasyon aracı, ilerici arama ve budama stratejileri kullanarak modelleri (SDAE temel, CNN-LSTM ve GRU-LSTM) eğitmiştir. Performans, t+1'den t+6'ya kadar MAE, RMSE ve R² metrikleriyle değerlendirilmiştir. Bulgular, hibrit mimarilerin temel (SDAE) modele kıyasla önemli bir üstünlük sergilediğini göstermektedir: CNN-LSTM tüm ufuklarda sürekli olarak yüksek doğruluk sergilerken, GRU-LSTM özellikle en kısa ufukta (t+1'de R²=0.9976) en düşük hata değerlerini üretmiştir. CNN-LSTM'nin kararlılığı ufuk uzadıkça korunmuş ve t+6'da bile R²=0.79'luk yüksek bir performans elde edilmiştir. Bu çalışma, en kısa vadeli tahminler için GRU-LSTM, ufuk uzadıkça daha kararlı tahminler için ise CNN-LSTM'in operasyonel kullanımını önermektedir. Sonuçlar, hibrit modellerin endüstriyel uygulamalar için güvenilir bir temel oluşturduğunu ve belirsizlik modellemesi ile Sayısal Hava Tahmini (NWP) entegrasyonuyla daha ileri kazanımlar elde edilebileceğini göstermektedir.

Anahtar Kelimeler

• Rüzgar gücü kestirimi
• ultra-kısa dönem tahmin
• CNN-LSTM
• GRU-LSTM
• RFECV
• Optuna
• DBSCAN
• SCADA

Use of Advanced Optimization and Hybrid Deep Learning Models in Ultra-Short-Term Wind Energy Forecasting: The Case of Hatay, Türkiye

Öz

This study presents a performance comparison of hybrid deep learning approaches with an end- to-end data pipeline designed to enhance accuracy and stability in ultra-short-term wind power forecasting. Anomaly removal using DBSCAN and feature selection based on RFECV are applied to multivariate SCADA-based data. An advanced hyperparameter optimization tool, Optuna, trained the models (SDAE baseline, CNN-LSTM, and GRU-LSTM) using progressive search and pruning strategies. Performance is evaluated using MAE, RMSE, and R² metrics from t+1 up to t+6 horizons. The findings indicate a significant superiority of the hybrid architectures over the baseline (SDAE) model: CNN-LSTM maintains consistently high accuracy across all horizons, while GRU-LSTM yields the lowest error metrics specifically at the shortest horizon (achieving an R²=0.9976 at t+1). The stability of the CNN-LSTM is maintained as the forecasting horizon extends, achieving a respectable performance of R²=0.79 even at t+6. This work proposes the operational use of GRU-LSTM for the shortest-term forecasts and CNN-LSTM for more stable predictions as the horizon lengthens. The results demonstrate that hybrid models establish a reliable foundation for industrial applications and suggest further gains are possible through the integration of uncertainty modeling and Numerical Weather Prediction (NWP).

Anahtar Kelimeler

• Wind power forecasting
• ultra-short term forecast
• CNN-LSTM
• GRU-LSTM
• RFECV
• Optuna
• DBSCAN
• SCADA

Kaynakça

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