Ayrık dalgacık dönüşümü ve Xgboost ile rüzgâr gücü tahmini
Rüzgâr gücü tahmini, sistem işletmecisi ve santraller için gerilim ve frekans kontrolü, yük kontrolü, ünite planlaması, bakım planlaması ve elektrik marketi hareketleri için gereklidir. Süreksiz bir kaynak olan rüzgârdan elde edilen güce ait zaman serisi durağan bir yapıda değildir. Rüzgâr gücü zaman serisi çeşitli sebeplerden dolayı aykırı veriler barındırmaktadır. Bu durum tahmin modellerinde başarıyı düşürmektedir. Bu çalışmada rüzgâr gücü tahmin modelinin en iyi sonucu vermesi için rüzgâr gücü verisi ayrık dalgacık dönüşümü ile dönüştürülmüştür. Dönüştürülen veriler, karar ağacı tabanlı, gradyan arttırmaya dayanan bir algoritma olan Xgboost ile eğitilmiştir. Test için ayrılan veriler tahmin edilmiştir. Ayrık dalgacık dönüşümü-Xgboost modeli her mevsimden seçilen dört ay için ayrı ayrı tasarlanmış, ayrık dalgacık dönüşümü olmadan sadece Xgboost ile tasarlanan model ile MAE, RMSE ve R2 hata metrikleriyle karşılaştırılmıştır. Ayrık dalgacık dönüşümü-Xgboost ile tasarlanan modeller daha başarılı sonuçlar vermiştir.
Anahtar Kelimeler:
Rüzgâr gücü, Rüzgâr gücü tahmini, Ayrık dalgacık dönüşümü, Xgboost
Wind power forecasting with discrete wavelet transform and Xgboost
Wind power forecasting is necessary for system operator and wind farm for voltage and frequency control, load dispatch, unit commitment, maintenance planning and electricity market actions. Wind power time series, which is a intermittent source, is not stationary and contains various outliers. This situation reduces the success of forecasting models. In order for the wind power forecasting model to give the best results, in this paper the wind power data was transformed with discrete wavelet transform. Transformed data were trained and forecested with Xgboost, a decision tree based, gradient boosting algorithm. Proposed model were designed separately for a selected month from each season. These models were compared with MAE, RMSE, R2 error metrics by the models designed with Xgboost without discrete wavelet transform. Discrete wavelet and Xgboost model gave more successful results than Xgboost model.
Keywords:
Wind power, Wind power forecasting, Discrete wavelet transform, Xgboost,
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- Başlangıç: 2009
- Yayıncı: Isparta Uygulamalı Bilimler Üniversitesi
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