Abstract
Among an electricity provider’s non-technical losses, electricity theft has the most severe and dangerous effects. Fraudulent electricity consumption decreases the supply quality, increases generation load, causes legitimate consumers to pay excessive electricity bills, and affects the overall economy. The adaptation of smart grids can significantly reduce this loss through data analysis techniques. The smart grid infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning and deep learning techniques can accurately identify electricity theft users. In this paper, an electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture. CNN is a widely used technique that automates feature extraction and the classification process. Since the power consumption signature is time-series data, we were led to build a CNN-based LSTM (CNN-LSTM) model for smart grid data classification. In this work, a novel data pre-processing algorithm was also implemented to compute the missing instances in the dataset, based on the local values relative to the missing data point. Furthermore, in this dataset, the count of electricity theft users was relatively low, which could have made the model inefficient at identifying theft users. This class imbalance scenario was addressed through synthetic data generation. Finally, the results obtained indicate the proposed scheme can classify both the majority class (normal users) and the minority class (electricity theft users) with good accuracy.
Highlights
Because of the high cost of acquiring energy, as well as the limited amount of energy resources, efficient and operative use of energy resources is a very important aspect of social and economic development for any country
The smart grid system can be described as an entire electricity network consisting of the power system infrastructure and computers to manage and monitor the energy usage, along with an intelligent monitoring system that tracks the usage pattern and mode of action of all consumers connected with the system [1]
We verify the efficacy of the proposed convolutional neural network (CNN)-long short-term memory (LSTM), with novel missing data
Summary
Because of the high cost of acquiring energy, as well as the limited amount of energy resources, efficient and operative use of energy resources is a very important aspect of social and economic development for any country. The smart grid provides the utilities’ and customers’ facility to monitor, control, and predict energy use by integrating modern digital equipment with the existing electrical system. In this system, the collector device delivers usage readings to the operational center using the internet, and the power transmission company performs the billing process depending on these readings. The operation center collects user readings from neighborhood customers’ periodic updates through a wireless network. The main target is to reduce losses due to energy wastage and provide viable, cost-effective, and secure electricity supplies [2].
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