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Rüzgar hızı tahminlemesi rüzgar güç dönüşüm sistemleri için oldukça önemlidir. Bu çalışmada kısa vadeli rüzgar hızı tahminlemesi için hibrit bir ayrıklaştırma yöntemi önerilmiştir. Önerilen yöntemde Toplu ampirik mod ayrıştırma (Ensemble Empirical Mode Decomposition, EEMD) ve Ampirik dalgacık dönüşümü (Emprical wavelet transform, EWT) birlikte kullanılmıştır. İlk defa kullanılan bu kombinasyon sonucunda elde edilen ayrıklaştırılmış rüzgar hızı sinyalleri kısmi otokorelasyon fonksiyonu (Partial autocorrelation function, PACF) ile öznitelik çıkarma işlemine tabi tutulmuştur. Elde edilen öznitelikler, geri beslemeli sinir ağına (Back propagation neural networks, BPNN) uygulanmak suretiyle çok adımlı rüzgar hız tahminleme işlemi gerçekleştirilmiştir. Önerilen modelin birbirinden bağımsız teknikler kullanılarak yapılan tekil ayrıklaştırmaya göre çok daha doğru ve güvenilir sonuçlar verdiği tespit edilmiştir. Çalışmada kullanılan veriler Tokat Gaziosmanpaşa Üniversitesi Taşlıçiftlik Kampüsü içerisinde kurulan ölçüm istasyonundan toplanmıştır. Önerilen hibrit model, yüksek hassasiyetli rüzgar hızı tahminleri için güvenilir, güçlü ve etkili olduğu kadar veri madenciliği uygulamalarında da kolaylıkla kullanılabilir. Tahmin performansının genel tahmin doğruluğu yaygın olarak kullanılan üç genel hata değerlendirme endeksi olan determinasyon katsayısı (determination coefficient (R2), ortalama mutlak yüzde hata (mean absolute percent error (MAPE) ve ortalama karekök hata (root mean square error (RMSE)) ile gerçekleştirildi.
Wind speed forecast is very important for wind power conversion systems. In this study, a hybrid separation method was suggested for short-term wind speed forecast. In the recommended method, mass empirical mode separation (EEMD) and empirical wavelet transformation (EWT) were used together. For the first time this combination was used, the separated wind speed signals obtained by the partial autocorrelation function (PACF) were subjected to the process of authentication. The obtained properties are applied to the back propagation neural networks (BPNN) and the multi-step wind speed forecast process is carried out. The proposed model has been found to give much more accurate and reliable results than the single separation made using independent techniques. The data used in the study was collected from the measurement station established within the Tokat Gaziosmanpaşa University Taşlıçiftlik Campus. The recommended hybrid model can be easily used in data mining applications as reliable, powerful and effective for high-decision wind speed forecasts. The overall predictive accuracy of the forecast performance was achieved by the determination ratio (determination coefficient (R2), the average absolute percentage error (MAPE) and the average root mean square error (RMSE).
Wind speed estimation is very important for wind power conversion systems. In this study, a hybrid discretization method is proposed for short-term wind speed estimation. In the proposed method, Ensemble Empirical Mode Decomposition (EEMD) and Empirical wavelet transform (EWT) are used together. Discretized wind speed signals obtained as a result of this combination used for the first time were subjected to feature extraction process with partial autocorrelation function (PACF). The multi-step wind speed estimation process has been carried out by applying the obtained features to the feedback neural network (Back propagation neural networks, BPNN). It has been determined that the proposed model gives much more accurate and reliable results than the singular discretization using independent techniques. The data used in the study were collected from the measurement station established in Tokat Gaziosmanpaşa University Taşlıçiftlik Campus. The proposed hybrid model is reliable, powerful and effective for high precision wind speed predictions, as well as easily used in data mining applications. The overall prediction accuracy of the prediction performance was achieved with the three commonly used general error rating indices: determination coefficient (R2), mean absolute percent error (MAPE) and root mean square error (RMSE).
Alan : Fen Bilimleri ve Matematik; Mühendislik
Dergi Türü : Uluslararası
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