Stacked machine learning models for non-technical loss detection in smart grid: A comparative analysis

The growing prominence and emphasis of renewable energy to decrease carbonization in the power system and reduce the dependability of fossil fuel for energy needs play an important role in the development of smart grids. Many technological advancements are integrated into smart grid to optimize the power system and renewable energy sources. Smart grid leverages electricity and energy consumption data exchange to establish a significantly advanced, automated, and decentralized electricity network. However, this brings numerous vulnerabilities to the power system, including cyber-attacks, grid blackouts, and electricity theft. While the most significant concern is energy theft, where some culprit's consumers manipulate their energy meters to reduce their readings. This destabilizes the country's electricity utility and economic development and causes a high tariff on energy for consumers who pay the bill. Therefore, developing an advanced framework for electricity theft detection is necessary. To address this problem, we propose a machine learning-based stacked framework to detect malicious activity in the smart grid. The proposed data-based stacked ensemble model detects honest and anomalous consumers in two stages. In the first stage, the model employs four individual classifiers at the base level to analyze data and a single classifier at the meta-level to classify the results of the base learners for the second stage classification. Furthermore, the Borderline SMOTE and Principle Component Analysis techniques are employed to address the class imbalance and curse of dimensionality issues respectively. Through experimental analysis, we proved the effectiveness of the proposed framework in detecting suspicious activity in four different experiments, including preprocessed data, feature extracted data, balanced data, and lastly, both feature engineering and data balancing. The simulation outcomes demonstrate that our proposed framework enhanced energy security and overcomes the impact of theft attacks on the smart grid.