Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan

Abstract

Non-technical losses (NTLs) have been a major concern for power distribution companies (PDCs). Billions of dollars are lost each year due to fraud in billing, metering, and illegal consumer activities. Various studies have explored different methodologies for efficiently identifying fraudster consumers. This study proposes a new approach for NTL detection in PDCs by using the ensemble bagged tree (EBT) algorithm. The bagged tree is an ensemble of many decision trees which considerably improves the classification performance of many individual decision trees by combining their predictions to reach a final decision. This approach relies on consumer energy usage data to identify any abnormality in consumption which could be associated with NTL behavior. The key motive of the current study is to provide assistance to the Multan Electric Power Company (MEPCO) in Punjab, Pakistan for its campaign against energy stealers. The model developed in this study generates the list of suspicious consumers with irregularities in consumption data to be further examined on-site. The accuracy of the EBT algorithm for NTL detection is found to be 93.1%, which is considerably higher compared to conventional techniques such as support vector machine (SVM), k-th nearest neighbor (KNN), decision trees (DT), and random forest (RF) algorithm.

A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

“…Specifically, for the binary classification problem, the confusion matrix returns two rows and two columns, i.e., four possible outcomes. These four possible outcomes are described as follows: The following are the performance metrics given in Equations (11)- (16), as defined in [20,21,24]:…”

“…Table 5 demonstrates the selection of the RUSBoost's hyperparameters using the grid-search technique. The authors in [21] use a bagged tree for NTL detection. A bagged tree is an ensemble learning technique, in which a number of training subsets are generated with replacements and different classifiers are trained on these subsets.…”

“…In contrast to the manual methods, supervised machine learning solutions have gained the interest of utilities and academia for performing ETD. Studies based on the supervised learning techniques [20][21][22][23][24][25] focus on ETD using the large and imbalanced datasets obtained through the smart meters. However, the performances of these techniques are still not sufficient for the practical applications in utilities.…”

“…A theft detector is also used for feature engineering-based preprocessing through the GBTD's feature importance function. Another solution based on the ensemble bagged tree is presented in [21] for the NTL detection, in which an ensemble of individual decision trees is applied to improve the theft detection by aggregating their performances. Buzau et al [23] submit a hybrid of LSTM and multilayer perceptron (MLP), termed LSTM-MLP, for the NTL detection in the smart grid.…”

A novel unsupervised feature‐based approach for electricity theft detection using robust PCA and outlier removal clustering algorithm

“…A hybrid approach consisting of one‐step‐ahead load predictor and a rule‐engine‐based load anomaly detector is proposed for a residential electrical load anomaly detection framework in Reference 14. Most recently, a supervised ML algorithms such as Ensemble bagged tree and C.50 decision tree algorithms are proposed to train a classification model in a conventional meter data set in references, 15,16 respectively.…”