Artificial intelligence-based efficient model for the detection of non-technical losses in smart grids
TL;DR
A novel transformer-based wide and deep convolutional neural network (TWiDeCNN) is proposed to efficiently identify electric energy theft in a scenario based on SGs, demonstrating its stability and effectiveness for electric energy theft detection.
Abstract
Electric energy theft is a major issue for the sustained use of modern smart grids (SGs). It affects the electric system’s overall long-term reliability and affordability. Even though advanced metering infrastructure can gather a large amount of data regarding electric energy utilization, it is quite difficult to address non-technical losses (NTLs). This is because electric energy consumption patterns exhibit high-dimensional characteristics, time-varying features, and are not invariably steady. Adapting the simple machine learning and artificial intelligence approach generally requires a single multidimensional electric energy consumption estimation and topical feature extraction mechanism, a limitation that prevents it from characterizing long-range temporal dependencies and intermittent behavior. Consequently, such procedures tend to have lower detection and elevated rates of false positives. In this regard, in this research, a novel transformer-based wide and deep convolutional neural network (TWiDeCNN) is proposed to efficiently identify electric energy theft in a scenario based on SGs. The suggested TWiDeCNN model is trained and tested on a real-world dataset of electric energy use collected from working SG environments, which makes it useful in the real world. Experimental results show that the suggested model works better than modern advanced methods that use binary classification metrics. These results show that the model performs well, is stable, and can be used at a large scale. They also show that it could be used in cutting-edge ways to improve energy management and find more electric energy theft. In-depth simulation results on the State Grid Corporation of China (SGCC) dataset demonstrate that TWiDeCNN outperforms the original wide model. In addition, it outperforms deep convolutional neural network (CNN) and other benchmarks in terms of ranking capability and detection performance with MAP@100 of 96.00 % and MAP@200 of 93.58 % at a 70% split. Moreover, parameter sensitivity analysis shows that proper tuning of parameters α , β , γ , and R would help the model build a deep feature-based, powerful representation and a better adaptation ability. Our proposed model maintains robust performance, demonstrating its stability and effectiveness for electric energy theft detection.