Electricity Theft Detection Using Machine Learning. Smart grids produce vast quantities of data, including consumer usa
Smart grids produce vast quantities of data, including consumer usage data which is crucial for identifying instances of energy theft. Ensemble ML models are The machine learning and deep learning approaches can be used for mining the hidden theft detection information in the smart meter data. This study suggests ensemble machine learning (ML) models for the detection of energy theft in smart grids using customers’ consumption patterns. Therefore, intensified research is needed to accurately detect the electricity thieves and to recover a huge revenue loss for utility PDF | On Jan 1, 2022, Ivan Petrlik and others published Electricity Theft Detection using Machine Learning | Find, read and cite all the research Nowadays, electricity theft is a major issue in many countries and poses a significant financial loss for global power utilities. Machine-learning techniques are extensively used in fraud detection and have a huge potential application in electricity theft detection. Electricity theft occurs when consumers Conventional Electricity Theft Detection (ETD) models face challenges such as the curse of dimensionality and highly imbalanced electricity consumption data distribution. Thus, it is important to develop effective Machine learning-based electricity theft detection using support vector machines January 2024 International Journal of Electrical Electricity Theft Detection using Machine Learning - written by Reshma Ravindran, Josephin Shajan, Suryalakshmi S R published on 2022/05/23 download full article with . Machine learning and deep learning To address these concerns at scale, this study proposes a cloud-based machine learning framework for intelligent electricity theft detection in residential sectors. The source of the information on the electricity consumption of 42372 The research focused on recommending the best prediction model using Machine Learning in electrical energy theft, and concluded that the best performance, with an accuracy of 81%, is To this end, stopping these energy theft practices is as important as energy production. The system accurately detects energy theft and false The existing theft detection methods using machine learning methods to detect various theft attacks are not efficient and have high To maintain the effectiveness, dependability, and security of modern energy systems, analyzing and detecting anomalies in energy usage, such as electricity theft and This research work dealt with the indiscriminate theft of electric power, reported as a non-technical loss, affecting electric distribution companies and customers, triggering serious Electricity theft not only results in higher electricity costs for regular paying customers but is also a safety threat to the public due to illegal power connections made for To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers’ electricity consumption The research focused on recommending the best prediction model using Machine Learning in electrical energy theft. Currently, huge amount of the historical electricity consumption (EC) data of electricity consumers collected by advanced Jain et al. [29] introduced a machine learning-based system for detecting energy theft and security threats in smart grids. However, it needs effective data Machine learning and deep learning algorithms may use this data to identify instances of energy theft.