Madalina-Mihaela Buzau's scientific contributions
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Publications (2)
Non-technical losses in electricity utilities are responsible for major revenue losses. In this paper, we propose a novel end-to-end solution to self-learn the features for detecting anomalies and frauds in smart meters using a hybrid deep neural network. The network is fed with simple raw data, removing the need of handcrafted feature engineering....
Non-technical electricity losses due to anomalies or frauds are accountable for important revenue losses in power utilities. Recent advances have been made in this area, fostered by the roll-out of smart meters. In this paper, we propose a methodology for non-technical loss detection using supervised learning. The methodology has been developed and...
Citations
... Further, a method based on principal component analysis (PCA) has been presented for electricity theft detection [3]. Besides, deep learning has also been used for electricity fraud detection [7,18,28,31,52,61]. Most existing machine learning methods employ SMOTE to reduce the class imbalance problem. ...
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... Utilities face a significant challenge from non-technical electricity losses (NTL), which can result from a wide variety of causes such as human error during installation, tampering with meter readings through unauthorized database access, incorrect calculations of technical losses, meter fraud, a faulty meter, electricity theft via distribution lines, nonpayment by customers, billing errors, and so on. Not only can they result in significant revenue losses, but also, since they introduce uncertainty into the real consumption, they might have an impact on the operation of the power system [9]. ...
