Open AccessAdaptive data balancing method using stacking ensemble model and its application to non-technical loss detection in smart grids
Author(s) -
Ashraf Ullah,
Nadeem Javaid,
Muhammad Umar Javed,
Pamir,
Byung-Seo Kim,
Saeed Ali Bahaj
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3230952
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
A stacking ensemble model (SEM) is proposed in this paper to identify non-technical losses. Three layers make up the proposed model. Data pre-processing is performed at the first layer, where issues of data imbalance, missing values, and data normalization are dealt with. Min-max and a simple imputer are used to handle data normalization and missing values, respectively. Besides, ADASYN and TomekLink are used in a combined form to address the problem of data imbalance. The second layer employs three different machine learning models. The models, also referred to as base classifiers, used at the second layer in the proposed SEM include the following classifiers: random forest (RF), extra tree (ET), and extreme gradient boosting (XGBoost). To accomplish the final classification using the ridge classifier, the output of the basic classifiers is ensembled at the third layer. The ridge classifier is also regarded as the meta classifier. Furthermore, the training and testing of the suggested model is aided by real-time data from the smart grid corporation of China (SGCC). The proposed model’s performance is validated by multiple simulations using various performance indicators and is found to surpass the standalone classifiers in terms of ETD.