DENTELNet: Teknik olmayan elektrik kayıplarının tespiti için yeni bir hibrit derin öğrenme yöntemi

Abstract

Elektrik enerjisi, modern toplumların temel ihtiyaçlarından biri olup, ekonomik büyüme ve toplumsal refah açısından kritik bir rol oynamaktadır. Ancak enerji üretiminden tüketime kadar geçen süreçte meydana gelen kayıplar, kaynakların verimsiz kullanımına ve ekonomik zararlara yol açmaktadır. Teknik olmayan kayıplar, özellikle usulsüz elektrik kullanımı, sayaç manipülasyonu ve kaçak bağlantılar gibi nedenlerle enerji sektörünün karşılaştığı en büyük zorluklardan biridir. Bu çalışmada, teknik olmayan kayıpların tespiti amacıyla yenilikçi bir hibrit derin öğrenme modeli olan DENTELNet önerilmiştir. Model, Evrişimli Sinir Ağları (Convolutional Neural Networks – CNN), İki Yönlü Uzun Kısa Vadeli Bellek (Bidirectional Long Short-Term Memory – BiLSTM) ve Sıvı Sinir Ağı (Liquid Neural Network – LNN) bileşenlerinden oluşmaktadır. Öznitelik indirgeme amacıyla Zaman Serisi Ayrışımı tabanlı Temel Bileşen Analizi yöntemi kullanılmış ve veri dengesizliği, Sınırda Sentetik Azınlık Aşırı Örnekleme Tekniği (Borderline SMOTE – BSMOTE) ve Tomek Bağlantısı Yöntemi (Tomek Link – TLink) yaklaşımı ile giderilmiştir. Yapılan deneysel çalışmalar sonucunda, model %92.153 doğruluk, %90.402 kesinlik, %92.153 duyarlılık ve %90.397 F1-skoru elde etmiştir. ROC eğrisi analizi, yüksek AUC değerleri ile modelin ayırt edici gücünü ve dengeli performansını göstermektedir.

Keywords

• Teknik olmayan kayıplar
• elektrik sahtekârlığı tespiti
• derin öğrenme
• öznitelik indirgeme
• veri dengeleme

DENTELNet: A Novel Hybrid Deep Learning Method for Detecting Non-Technical Electricity Losses

Abstract

Electricity is one of the essential needs of modern societies, playing a critical role in economic growth and social welfare. However, losses occurring throughout the energy production and consumption process lead to inefficient use of resources and economic damages. Non-technical losses, particularly due to unauthorized electricity consumption, meter tampering, and illegal connections, are among the most significant challenges faced by the energy sector. In this study, an innovative hybrid deep learning model named DENTELNet is proposed for detecting non-technical losses. The model consists of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Liquid Neural Networks (LNN). To achieve feature reduction, a Time-Series Decomposition-based Principal Component Analysis method is used, and data imbalance is addressed using the Borderline Synthetic Minority Over-sampling Technique (BSMOTE) and Tomek Link (TLink) approach. Experimental results demonstrate an accuracy of 92.153%, precision of 90.402%, recall of 92.153%, and an F1-score of 90.397%. The ROC curve analysis, with high AUC values, reveals the model's discriminative power and balanced performance.

Keywords

• Non-technical losses
• electricity fraud detection
• deep learning
• feature reduction
• data balancing

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