Açıklanabilir Yapay Zekâ Destekli Güneş Enerjisi Tahmini: Histogram Tabanlı Gradyan Artırma Modeli ile Bir Uygulama

Abstract

Güneş fotovoltaik (PV) sistemlerinin enerji şebekelerine entegrasyonu, üretim-tüketim dengesinin korunabilmesi açısından doğru ve güvenilir üretim tahminlerini zorunlu kılmaktadır. Ancak, geleneksel tahmin modellerinin yorumlanabilirlik eksikliği, bu modellerin karar verme süreçlerine duyulan güveni sınırlamaktadır. Bu çalışmada, saatlik güneş enerjisi üretimi tahmini için Histogram Tabanlı Gradyan Artırma algoritması (HistGradientBoosting) kullanılmış ve modelin şeffaflığını artırmak amacıyla Açıklanabilir Yapay Zekâ (XAI) tekniklerinden SHAP (Shapley Additive Explanations) yöntemi entegre edilmiştir. Çalışmada, açık erişimli GEFCom 2014 veri seti kullanılmış olup toplam 59.112 örneklem ve güneş ışınımı, sıcaklık, nem, bulutluluk ve rüzgâr hızı gibi 12 farklı meteorolojik öznitelik içermektedir. Model performansı, Ortalama Karesel Hata (MSE), Hatanın Karesel Ortalamasının Karekökü (RMSE), Ortalama Mutlak Hata (MAE) ve Belirleme Katsayısı (R²) metrikleriyle değerlendirilmiştir. Gerçekleştirilen analizler sonucunda, önerilen HistGradientBoosting modeli 0.0836 RMSE ve 0.8768 R² değerlerine ulaşarak yüksek bir tahmin doğruluğu sergilemiştir. SHAP analizleri, güneş ışınımının en etkili özellik olduğunu ortaya koymuştur. Çalışma, güneş enerjisi üretiminde XAI destekli tahmin modellerinin, enerji yönetimi ve akıllı şebeke uygulamalarında güvenilir karar destek sistemleri geliştirilmesine katkı sağlayabileceğini göstermektedir.

Keywords

• Solar PV
• Güç Üretimi Tahmini
• Açıklanabilir Yapay Zeka
• Makine Öğrenmesi
• Yenilenebilir Enerji
• Akıllı Şebeke

Supporting Institution

TÜBİTAK

Thanks

Bu araştırma TÜBİTAK-Türkiye Bilimsel ve Teknolojik Araştırma Kurumu tarafından '2219-Uluslararası Doktora Sonrası Araştırma Burs Programı' kapsamında kısmen desteklenmiştir.

Explainable Artificial Intelligence Assisted Solar Energy Forecasting: An Application with Histogram-Based Gradient Boosting Model

Abstract

The integration of solar photovoltaic (PV) energy into power grids necessitates accurate and reliable forecasting to ensure stability and efficiency. Traditional forecasting models often lack transparency, complicating the interpretation of predictions and the optimization of energy management strategies. In this study, a HistGradientBoosting algorithm was employed for hourly solar power forecasting, and the SHAP (Shapley Additive Explanations) method was integrated to enhance model transparency within an Explainable Artificial Intelligence (XAI) framework. The study used the GEFCom 2014, which is an open-access dataset containing 12 different meteorological features with a total of 59,112 samples, such as solar radiation, temperature, humidity, wind speed, cloud cover, etc. The model's performance is evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination. The results of the analyses show that the proposed HistGradientBoosting model achieved high prediction accuracy, with an RMSE of 0.0836 and an R² value of 0.8768. The study discusses the practical implications of using XAI-enhanced forecasting models in energy management and smart grid applications, highlighting improvements in trust, decision-making, and operational efficiency.

Keywords

• Solar PV
• Power Generation Forecasting
• Explainable Artificial Intelligence
• Machine Learning
• Renewable Energy
• Smart Grid

Supporting Institution

TUBITAK

Thanks

This research was partially supported by TÜBİTAK-The Scientific and Technological Research Council of Türkiye within the scope of '2219-International Postdoctoral Research Fellowship Program'.

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