Wind Power Prediction Based on Polynomial Regression Method

Abstract

Accurate prediction of wind energy output from site-specific wind velocity data is essential for evaluating the feasibility and energy yield of wind farm installations. This study presents a predictive framework for wind power assessment based on daily wind velocity datasets from eight Iraqi cities: Duhok, Mosul, Kirkuk, Baghdad, Najaf, Wasit, Qadisiyyah, and Basra. The proposed method employs polynomial regression (POR) as a nonlinear estimation technique to correlate daily wind speed data with turbine power output. POR is selected for its computational simplicity and adequacy in capturing the nonlinearity inherent in wind power curves. The predictive models are calibrated individually for each city using historical wind data and the manufacturer's specified power curve for the turbine. The results indicate that the POR-based predictions closely align with the actual turbine power curve across all sites, demonstrating low prediction error and strong curve-fitting behavior. Notably, Wasit, Qadisiyyah, and Basra exhibited the highest potential for wind energy generation, with annual predicted outputs exceeding 2000 kWh per turbine, indicating promising conditions for small-scale wind energy exploitation. In contrast, northern cities such as Duhok and Mosul yielded significantly lower outputs (<1000 kWh annually), suggesting limited economic viability under the studied configuration.

Keywords

• Artificial intelligence
• Machine learning
• Wind power
• Wind velocity

Polinom Regresyon Yöntemine Dayalı Rüzgar Gücü Tahmini.

Abstract

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.

Keywords

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

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