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Journals / İstatistik ve Uygulamalı Bilimler Dergisi / 2026 / Sayı: 11

Machine Learning-Based Wind Energy Forecasting Using Weather Parameters: The Example of Yalova

Hava Parametrelerini Kullanarak Makine Öğrenmesine Dayalı Rüzgar Enerjisi Tahmini: Yalova Örneği

Abdulkadir Atalan(CANAKKALE ONSEKIZ MART UNIVERSITY)0000-0003-0924-3685
Lütfi Alper Gündoğdu(CANAKKALE ONSEKIZ MART UNIVERSITY)0000-0002-2002-0952
Harun Kahyalık(CANAKKALE ONSEKIZ MART UNIVERSITY)0009-0008-9636-8008
Yasemin Ayaz Atalan(CANAKKALE ONSEKIZ MART UNIVERSITY)0000-0001-7767-0342
Pages
40–49
Publication Date
2025-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results, the best-performing algorithm was RF with an MSE value of 0.039, RMSE value of 0.197, MAE value of 0.081, MAPE value of 0.377, and R² score of 0.961.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, various machine learning algorithms were evaluated for estimating wind energy production using hourly meteorological data of Yalova province in 2018.
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Key Findings & Data:In the analysis performed on a total of 50530 data points, methods such as Gradient Boosting (GB), Random Forests (RF), k-nearest neighbor (kNN), and Stochastic gradient descent (GBD) were compared.
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Conclusion & Impact:According to the results, the best-performing algorithm was RF with an MSE value of 0.039, RMSE value of 0.197, MAE value of 0.081, MAPE value of 0.377, and R² score of 0.961.
🔬Design:Modeling & Simulation
📍Scope:2018 Verileri
⚙️Variables:Rüzgar enerjisi · hava parametreleri · makine öğrenimi

Abstract

In this study, various machine learning algorithms were evaluated for estimating wind energy production using hourly meteorological data of Yalova province in 2018. The input parameters were input parameters of weather parameters such as temperature, relative humidity, air pressure, wind direction, and wind speed. In the analysis performed on a total of 50530 data points, methods such as Gradient Boosting (GB), Random Forests (RF), k-nearest neighbor (kNN), and Stochastic gradient descent (GBD) were compared. Model performances were evaluated according to Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), MAPE, and R2 criteria. According to the results, the best-performing algorithm was RF with an MSE value of 0.039, RMSE value of 0.197, MAE value of 0.081, MAPE value of 0.377, and R² score of 0.961. On the other hand, the SGD model showed the lowest performance with an MSE value of 0.175, RMSE value of 0.418, MAE value of 0.303, MAPE value of 0.581, and R² score of 0.822. These findings show that machine learning models, supported by selecting the correct weather parameters, can provide high accuracy in estimating wind energy production and contribute to energy management policies in this direction.

Özet

Bu çalışmada, 2018 yılına ait Yalova ilinin saatlik meteorolojik verileri kullanılarak rüzgar enerjisi üretiminin tahmin edilmesinde çeşitli makine öğrenmesi algoritmaları değerlendirilmiştir. Girdi parametreleri; sıcaklık, bağıl nem, hava basıncı, rüzgar yönü ve rüzgar hızı gibi hava durumu parametreleridir. Toplam 50.530 veri noktası üzerinde yapılan analizde, Gradient Boosting (GB), Random Forests (RF), en yakın komşu (kNN) ve stokastik gradyan inişi (SGD) gibi yöntemler karşılaştırılmıştır. Model performansları Ortalama Mutlak Hata (MAE), Ortalama Kare Hata (MSE), Kök Ortalama Kare Hata (RMSE), Ortalama Mutlak Yüzde Hata (MAPE) ve R² kriterlerine göre değerlendirilmiştir. Sonuçlara göre, en iyi performansı gösteren algoritma; 0,039 MSE değeri, 0,197 RMSE değeri, 0,081 MAE değeri, 0,377 MAPE değeri ve 0,961 R² skoru ile RF olmuştur. Öte yandan, en düşük performansı gösteren model ise; 0,175 MSE değeri, 0,418 RMSE değeri, 0,303 MAE değeri, 0,581 MAPE değeri ve 0,822 R² skoru ile SGD modeli olmuştur. Bu bulgular, doğru hava durumu parametrelerinin seçimiyle desteklenen makine öğrenmesi modellerinin rüzgar enerjisi üretiminin tahmininde yüksek doğruluk sağlayabileceğini ve bu doğrultuda enerji yönetim politikalarına katkı sunabileceğini göstermektedir.

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Cite This Article

Atalan, A., Gündoğdu, L. A., Kahyalık, H., Atalan, Y. A. (2025). Machine Learning-Based Wind Energy Forecasting Using Weather Parameters: The Example of Yalova. İstatistik ve Uygulamalı Bilimler Dergisi, Sayı: 11, 40–49. https://doi.org/10.52693/jsas.1670486

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