Wind Power Prediction Based on Wind Velocity Variable and Polynomial Regression Method.

Yıl 2025, Cilt: 12 Sayı: 3, 274 - 282, 30.09.2025

Hussein Saleh

https://doi.org/10.31202/ecjse.1722153

Öz

Predicting the output energy obtained from wind velocity is required to investigate the wind power characteristics at potential locations and accurate evaluation of the power fluxes generating into the wind farms. This study provides a proposed approach to predict and assess wind power, using wind turbine parameters as a learning factor and daily wind velocity dataset of 8 cities in Iraq, namely Duhok, Mosul, Kirkuk, Baghdad, Najaf, Wasit, Qadisiyyah, and Basra. The nonlinear predictive models are established via the polynomial regression technique (POR) as a less complex machine learning method to extract potential wind energy from the wind turbine model (GhrepowerFD21-50_61.2kW_21.5). According to the outcomes, the scatter points of the wind power prediction values at all locations can follow the power curve output of the suggested turbine model with high accuracy and minimal error. Also, the 3 cities of Wasit, Qadisiyyah, and Basra have best total annual wind power quantities, with more than 2000 Kilowatt (kW) for each turbine unit, outperforming Duhok and Mosul that were less than 1000 kW. In addition, the POR technique was useful in extracting annual wind energy and estimation procedures for the regression method.

Anahtar Kelimeler

Artificial intelligence , machine learning , wind power

Polinom Regresyon Yöntemine Dayalı Rüzgar Gücü Tahmini.

Öz

Rüzgar hızından elde edilen çıkış enerjisini tahmin etmek, potansiyel lokasyonlardaki rüzgar gücü karakteristiklerini araştırmak ve rüzgar çiftliklerine üretilen güç akılarının doğru bir şekilde değerlendirilmesi için gereklidir. Bu çalışma, öğrenme faktörü olarak rüzgar türbini parametrelerini ve Irak'taki 8 şehrin (Duhok, Musul, Kerkük, Bağdat, Necef, Vasit, Kadisiye ve Basra) günlük rüzgar hızı veri setini kullanarak rüzgar gücünü tahmin etmek ve değerlendirmek için önerilen bir yaklaşım sunmaktadır. Doğrusal olmayan tahmini modeller, rüzgar türbini modelinden (GhrepowerFD21-50_61.2kW_21.5) potansiyel rüzgar enerjisini çıkarmak için daha az karmaşık bir makine öğrenme yöntemi olarak polinom regresyon tekniği (POR) aracılığıyla kurulmuştur. Sonuçlara göre, tüm lokasyonlardaki rüzgar gücü tahmin değerlerinin dağılım noktaları, önerilen türbin modelinin güç eğrisi çıktısını yüksek doğruluk ve minimum hata ile takip edebilir. Ayrıca, Wasit, Qadisiyyah ve Basra'nın 3 şehri, her türbin ünitesi için 2000 Kilowatt'tan (kW) fazla olan en iyi toplam yıllık rüzgar gücü miktarlarına sahiptir ve 1000 kW'tan az olan Duhok ve Musul'u geride bırakmıştır. Ek olarak, POR tekniği, yıllık rüzgar enerjisini çıkarmada ve regresyon yöntemi için tahmin prosedürlerinde yararlıydı.

Anahtar Kelimeler

Yapay zeka , makine öğrenmesi , rüzgar enerjisi , rüzgar hızı.

Kaynakça

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