Irregularity detection on low tension electric installations by neural network ensembles

Abstract

The volume of energy loss that Brazilian electric utilities have to deal with has been ever increasing. The electricity concessionaries are suffering significant and increasing loss in the last years, due to theft, measurement errors and many other kinds of irregularities. Therefore, there is a great concern from those companies to identify the profile of irregular customers, in order to reduce the volume of such losses. This paper presents the proposal of an intelligent system, composed of two neural networks ensembles, which intends to increase the level of accuracy in the identification of irregularities among low tension consumers. The data used to test the proposed system are from Light S.A. Company, the Rio de Janeiro concessionary. The results obtained presented a significant increase in the identification of irregular customers when compared to the current methodology employed by the company.

Detection of Electricity Theft Behavior Based on Improved Synthetic Minority Oversampling Technique and Random Forest Classifier

“…In [28], the extreme learning machine (ELM) was used to identify the weight between the hidden and output layer, and electricity theft was detected through the measured data of the meter. In [29], a five-joint neural network was trained with power data comprising 20,000 customers and achieved considerable accuracy. SVM-FIS method was proposed in [30], which could reduce the calculation complexity and improve the detection accuracy by combining the fuzzy inference system with the SVM.…”

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

“…However, the proposed scheme was not very effective due to the uneven datasets and hence low precision was obtained which finally resulted in huge false positives. Muniz et al [16] also used the ANN-based approach for training the NTLs detection model. In order to further improve the performance of the ANN model, the fuzzy classification was employed, however, that model also suffers from lower accuracy.…”

Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

“…To begin with, several contributions have gravitated on the use of machine learning models over supervised datasets, such as Support Vector Machines [13,14,15,16,17,18], Neural Networks [19,20,21], Extreme Learning Machines [22], Path Forests [23,24], Decision Trees [25,26,27], model ensembles [28], and statistical methods [29,30]. However, all such previous work builds upon the assumption that supervised datasets capture the entire casuistry of symptomatic anomalies of interest for fraud detection and/or electricity theft, which not only unrealistic in practice but also yields highly imbalanced datasets that subsequently jeopardize the model learning process.…”