Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.1887617 Software Engineering (Other) Yazılım Mühendisliği (Diğer) Deep Learning Methods in Energy Systems: A Renewable Energy Perspective Enerji Sistemlerinde Derin Öğrenme Yöntemleri: Yenilenebilir Enerji Odaklı Bir İnceleme https://orcid.org/0000-0002-2203-0177 Budak Ziyadanoğulları Neşe BATMAN ÜNİVERSİTESİ 03 28 2026 14 50 62 02 12 2026 03 27 2026 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

This paper presents a comprehensive review of deep learning applications in energy systems with a particular focus on renewable-energy-based power systems. The rapid deployment of photovoltaic (PV) and wind generation introduces significant uncertainty into power system operation and planning. Accurate forecasting of renewable generation and load, advanced energy management strategies for renewable-rich microgrids, and reliable fault detection and predictive maintenance schemes for PV plants and wind turbines are essential to guarantee secure and economic operation. In recent years, deep neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN) such as long short-term memory (LSTM) and gated recurrent units (GRU), and deep reinforcement learning (DRL) algorithms have achieved state-of-the-art performance in these tasks. This review first outlines the main deep learning architectures and the characteristics of data in energy and renewable energy systems. It then surveys applications in PV and wind power forecasting, load forecasting in smart grids, DRL-based energy management in renewable-rich microgrids, and fault detection and predictive maintenance in PV and wind plants. Emerging trends such as generative models for data augmentation, physics-informed learning and explainable artificial intelligence (XAI) are also discussed. The paper concludes by highlighting open challenges related to data quality, generalization, computational cost and model interpretability, and by outlining promising directions for future research.

Bu makale, özellikle yenilenebilir enerjiye dayalı güç sistemlerine odaklanarak, enerji sistemlerinde derin öğrenme uygulamalarının kapsamlı bir incelemesini sunmaktadır. Fotovoltaik (PV) ve rüzgar enerjisi üretiminin hızlı bir şekilde yaygınlaşması, güç sistemi işletimi ve planlamasına önemli belirsizlikler getirmektedir. Yenilenebilir enerji üretimi ve yükünün doğru tahmin edilmesi, yenilenebilir enerji açısından zengin mikro şebekeler için gelişmiş enerji yönetim stratejileri ve PV santralleri ve rüzgar türbinleri için güvenilir arıza tespiti ve öngörücü bakım şemaları, güvenli ve ekonomik işletimi garanti etmek için gereklidir. Son yıllarda, derin sinir ağları, evrişimsel sinir ağları (CNN), uzun kısa süreli bellek (LSTM) ve geçitli tekrarlayan birimler (GRU) gibi tekrarlayan sinir ağları (RNN) ve derin pekiştirmeli öğrenme (DRL) algoritmaları bu görevlerde en iyi performansı elde etmiştir. Bu inceleme öncelikle temel derin öğrenme mimarilerini ve enerji ve yenilenebilir enerji sistemlerindeki verilerin özelliklerini özetlemektedir. Ardından, fotovoltaik ve rüzgar enerjisi tahminleri, akıllı şebekelerde yük tahminleri, yenilenebilir enerji açısından zengin mikro şebekelerde DRL tabanlı enerji yönetimi ve fotovoltaik ve rüzgar santrallerinde arıza tespiti ve öngörücü bakım uygulamaları incelenmektedir. Veri artırma için üretken modeller, fiziksel bilgiye dayalı öğrenme ve açıklanabilir yapay zeka (XAI) gibi ortaya çıkan trendler de ele alınmaktadır. Makale, veri kalitesi, genelleme, hesaplama maliyeti ve model ile ilgili açık zorlukları vurgulayarak sona ermektedir.

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