Abstract
Electricity is widely used around 80% of the world. Electricity theft has dangerous effects on utilities in terms of power efficiency and costs billions of dollars per annum. The enhancement of the traditional grids gave rise to smart grids that enable one to resolve the dilemma of electricity theft detection (ETD) using an extensive amount of data formulated by smart meters. This data are used by power utilities to examine the consumption behaviors of consumers and to decide whether the consumer is an electricity thief or benign. However, the traditional data-driven methods for ETD have poor detection performances due to the high-dimensional imbalanced data and their limited ETD capability. In this paper, we present a new class balancing mechanism based on the interquartile minority oversampling technique and a combined ETD model to overcome the shortcomings of conventional approaches. The combined ETD model is composed of long short-term memory (LSTM), UNet and adaptive boosting (Adaboost), and termed LSTM–UNet–Adaboost. In this regard, LSTM–UNet–Adaboost combines the advantages of deep learning (LSTM-UNet) along with ensemble learning (Adaboost) for ETD. Moreover, the performance of the proposed LSTM–UNet–Adaboost scheme was simulated and evaluated over the real-time smart meter dataset given by the State Grid Corporation of China. The simulations were conducted using the most appropriate performance indicators, such as area under the curve, precision, recall and F1 measure. The proposed solution obtained the highest results as compared to the existing benchmark schemes in terms of selected performance measures. More specifically, it achieved the detection rate of 0.92, which was the highest among existing benchmark schemes, such as logistic regression, support vector machine and random under-sampling boosting technique. Therefore, the simulation outcomes validate that the proposed LSTM–UNet–Adaboost model surpasses other traditional methods in terms of ETD and is more acceptable for real-time practices.
Highlights
The secure and efficient use of electricity represents a major aspect of the social and economic development of a country
To validate the long short-term memory (LSTM)–UNet–Adaboost model’s performance and robustness, seven performance evaluation metrics are used to show the superiority of our proposed model over benchmark schemes for theft detection
This section assesses the performance of our proposed LSTM–UNet–Adaboost model for electricity theft detection (ETD)
Summary
The secure and efficient use of electricity represents a major aspect of the social and economic development of a country. Electricity losses happen during power generation, transmission and delivery to consumers. Power transmission and delivery have a couple of losses, Energies 2020, 13, 5599; doi:10.3390/en13215599 www.mdpi.com/journal/energies. Energies 2020, 13, 5599 namely, technical losses (TL) and nontechnical losses (NTL) [1]. TL occur due to the line losses, transformer losses and other power system elements. NTL occur due to the electricity stealing, defective meters, overdue bills and billing mistakes [1]. NTL are the difference between total losses and TL. NTL raise electricity prices, increase load-shedding, decrease revenue and decrease energy efficiency. NTL badly affect both the utilities and a country’s financial state [2,3]
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