Madalina-Mihaela Buzau's scientific contributions

Publications (2)

Article
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....
Article
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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

... Not only do users require service improvements, but providers constantly lose resources to non-technical losses (NTL). Therefore, in [9] they have implemented a control algorithm based on hybrid deep neural networks that allow the detection of operating problems and anomalies in smart meters. This proposal does not require data pre-processing, which simplifies the system that shows good results. ...
... The following techniques of machine learning can be used in real-time applications: (1) supervised learning Buzau et al., 2019;Kim, 2020;Mohammadi et al., 2018;Henri and Lu, 2019); (2) unsupervised learning Park et al., 2019;Bodnar et al., 2017); (3) reinforcement learning (Yan and Xu, 2020;Xu et al., 2020b;Wei et al., 2020;Munir et al., 2019;Cao et al., 2020;Yu et al., 2020); and (4) ensemble learning (Su et al., 2020;Du, 2019;Zhnag, 2020;Meng et al., 2020;Xu et al., 2019). Supervised learning: The method is used to learn the data when supplying the right responses or data labels. ...