Improving Smart Grids Security: An Active Learning Approach for Smart Grid-Based Energy Theft Detection

Abstract

Energy providers and the power grid are severely harmed by electricity theft, which also causes economic and non-technical losses. Energy theft causes a decline in power quality and overall profitability. Smart grids may address the problem of power theft by merging data and energy flow. The analysis of smart grid data helps to find power theft. The prior methods, however, could have done a better job of identifying energy theft. In this research, we presented an active learning-based machine learning model for energy theft identification and classification of a smart grid. The suggested approach is based on the following steps. We use a dataset from the Open Energy Data Initiative (OEDI), an energy research database that gets information from numerous OEDI offices and labs. Next, we pre-process the data and employ machine learning methods like Active Learning (AL) based Random Forests (RFAL), eXtreme Gradient Boosting (XGboostAL), Decision Tree (DTAL), Gradient Boosting (GBAL), K-Nearest Neighbors (KNNAL), Categorical Boosting (CatboostAL) and Light Gradient Boosting Machine (LGBMAL) classifier. Using the smart grid-based energy theft detection dataset, the proposed RFAL model outperforms competing models and obtains an accuracy of 70.61%. The principles of smart grid tasks streamline decisions and enhance interaction between humans and machines by combining AL with machine learning. The application of this technology in this area has the potential to enhance the accuracy of energy theft detection and electricity-related problems and consequences.

Keywords

• Active learning
• energy theft detection
• machine learning
• privacy
• smart grid
• security