Güneş enerji sistemlerinde veri analizi için kullanılan algoritmaların performanslarının karşılaştırılması

Abstract

Güneş enerjisi hayati bir sürdürülebilir çözümdür, ancak verimliliği çevresel değişkenlikten etkilenir ve bu durum doğru performans tahminini zorunlu kılar. Bu çalışma, sistem optimizasyonunu artırmak amacıyla güneş enerjisi tahmini için öne çıkan algoritmaları değerlendirmektedir. Yapay Sinir Ağları (YSA), Uzun Kısa Süreli Bellek (LSTM), Rastgele Orman (RF), Destek Vektör Makineleri (DVM), K-En Yakın Komşu (KNN) ve Çoklu Doğrusal Regresyon (MLR) arasında sistematik bir karşılaştırma yapılmıştır. Bu teknikler tahmin doğruluğu, hesaplama hızı ve uygulama kolaylığına göre analiz edilmiştir. Sonuçlar, geleneksel modellerin basitlik sunduğunu, ancak LSTM gibi gelişmiş derin öğrenme mimarilerinin güneş verilerinin doğrusal olmayan ve zamansal bağımlılıklarını yönetmede üstün yetenekler sağladığını göstermektedir. Bu araştırma, model karmaşıklığı ile tahmin hassasiyeti arasındaki ödünleşimleri dengeleyerek, belirli güneş enerjisi uygulamaları için en uygun algoritmayı seçmede araştırmacılara ve mühendislere stratejik bir çerçeve sunar.

Keywords

• LSTM
• YSA
• Güneş enerjisi
• Makine öğrenimi
• Performans tahmini.

Comparing The Performance of Algorithms Used for Data Analysis in Solar Energy Systems

Abstract

Solar energy is a vital sustainable solution, but its efficiency is heavily affected by environmental variability, making accurate performance prediction essential. This study evaluates several prominent algorithms for solar energy forecasting to enhance system optimization. A systematic comparison was conducted among Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Multiple Linear Regression (MLR). These techniques were analysed based on predictive accuracy, computational speed, and ease of application. Findings indicate that while traditional models offer simplicity, advanced deep learning architectures like LSTM provide superior capabilities in handling the non-linear and temporal dependencies of solar data. This research provides a strategic framework for researchers and engineers in selecting the most suitable algorithm for specific solar energy applications, effectively balancing the trade-offs between model complexity and forecasting precision.

Keywords

• ANN
• Solar energy
• Machine learning
• Performance prediction
• Data analysis.

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