Energy Theft Detection in an Edge Data Center Using Deep Learning

Abstract

With the development of smart grid information physical systems, some of the data processing functions gradually approach the edge layer of end-users. To better realize the energy theft detection function at the edge, we proposed an energy theft detection method based on the power consumption information acquisition system of power enterprises. The method involves the following steps. In the centralized data center, K-means is used to decompose a large amount of data into small data and then input and train neural network parameters to realize feature extraction. We design a neural network named DWMCNN, which can extract features from the day, week, and month and can extract more accurate features. In the edge data center, the random forest (RF) algorithm is used to classify the extracted features. The experimental results show that the clustering method accords with the idea of edge computing-distributed processing and improves the operation speed and that the feature extractor has good convergence performance. In addition, compared with the methods based on various classifiers, this method has higher accuracy and lower computational complexity, which is suitable for the deployment of edge data centers.

Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting

“…Cluster-analysis-based data-mining techniques, such as [11][12][13] High requirements on the quantity of the data; High computational cost Machine learning, such as [14][15][16][17][18][19][20][21][22][23] Strong non-linear mapping ability; Influence of super parameters on prediction stability Artificial intelligence algorithms, such as [24][25][26] Strong convergence; Easy to fall into local extreme value Data envelopment analysis, such as [27][28][29] Strong applicability; Wide application range This study presents a power load forecasting-based abnormal data detection method to improve the economy of electricity inspection and promote the sustainable development of electric power firms. First, an intelligent algorithm is used to optimize the parameters of ELM to improve the forecasting accuracy for the power load.…”

“…For example, Viegas et al [11] proposed a data-based method to detect sources of theft and other power losses, using the Gustafson-Kessel fuzzy clustering algorithm to cluster the data collected by smart meters to identify prototypes of typical consumption behavior and locate abnormal data points by comparing new data samples with consumption prototypes. Cheng et al [12] proposed a power consumption detection method based on the power consumption information acquisition system of power firms. The K-means clustering algorithm was used to extract features and the random forest (RF) algorithm was used to classify the extracted features, being suitable for the processing of edge data.…”

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

“…However, they achieved low true positive rate (TPR) and high FPR, which are 63.6% and 24.3%, respectively. Lastly, the authors of [ 35 ] describe an energy-theft-detection method using data about power provider system consumption at the edge. Centralized data centers employ K-means clustering and DNN to extract features.…”

Novel FDIs-based data manipulation and its detection in smart meters’ electricity theft scenarios

“…The extracted data is inputted into catboost classifier for classification and a tree-SHAP algorithm is used as a decision-maker for theft identification. Study in (Cheng et al, 2021) proposes RF based classifier for the detection of an anomaly in a time series data. To reduce heavily dense time series data K-means method is used, whereas, a neural network of day, week, and month convolutional neural network (DWMCNN) is used to analyse the SMs' consumption data and to extract key features.…”