Dört Farklı Metasezgisel Algoritma Kullanılarak Rüzgâr Hızı Olasılık Dağılımı Parametrelerinin Tahmini

Abstract

Güneş ve rüzgâr enerjisi gibi yenilenebilir enerji kaynaklarından (YEK) üretilen enerjinin mevcut enerji sistemlerine dahil edilmesi, karbon salınımlarını, hava kirliliğini ve iklim değişikliğini azaltmak ve sürdürülebilir bir kalkınmayı sağlamak için önemlidir. Ancak YEK’lerin enerji sistemine entegrasyonu, oldukça belirsiz ve kesintili yapıları nedeniyle hayli zordur. Bu çalışmada, İstanbul’da bulunan kentsel istasyonlardan elde edilen rüzgâr hızı verileri için, toplamda üç farklı olasılık yoğunluk fonksiyonu dikkate alınarak, Weibull olasılık yoğunluk fonksiyonunun ölçek ve şekil parametreleri, Rayleigh olasılık yoğunluk fonksiyonunun ölçek parametresi ve Gamma olasılık yoğunluk fonksiyonunun ölçek ve şekil parametreleri, Genetik Algoritma (GA), Diferansiyel Evrim (DE), Parçacık Sürü Optimizasyonu (PSO) ve Gri Kurt Optimizasyonu (GWO) algoritmaları olmak üzere dört farklı metasezgisel algoritma kullanılarak tahmin edilmiştir. Her istasyonda her bir olasılık yoğunluk fonksiyonu için ortalama mutlak hata (MAE), kök ortalama kare hata (RMSE) ve R2 değerleri hesaplanarak, rüzgâr hızı olasılık dağılımını en iyi karakterize eden olasılık yoğunluk fonksiyonu belirlenmiştir.

Keywords

• Rüzgâr hızı
• yenilenebilir enerji
• olasılık yoğunluk fonksiyonu
• parametre tahmini
• metasezgisel algoritma

Estimation of Wind Speed Probability Distribution Parameters by Using Four Different Metaheuristic Algorithms

Abstract

The inclusion of energy produced from renewable energy sources (RES) such as solar and wind energy into existing energy systems is important to reduce carbon emissions, air pollution and climate change, and to ensure sustainable development. However, the integration of RES into the energy system is quite difficult due to their highly uncertain and intermittent nature. In this study, considering three different probability density functions (PDFs) in total, the scale and shape parameters of the Weibull PDF, the scale parameter of the Rayleigh PDF, and the scale and shape parameters of the Gamma PDF were estimated for the wind speed data obtained from urban stations located in Istanbul by using the four different metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Calculating the mean absolute error (MAE), root mean squared error (RMSE), and R2 values for each PDF at each station, the PDF that characterizes the wind speed probability distribution the best was identified.

Keywords

• Wind speed
• renewable energy
• probability density function
• parameter estimation
• metaheuristic algorithm

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