Energy Theft Detection in Power System Network: Reviews of Studies on Machine Learning Based Solutions

Energy theft is a major issue in the energy industry, causing significant financial losses and reducing the reliability of the energy supply. With the advent of advanced technologies such as machine learning, energy theft detection is becoming more efficient and effective. Machine learning algorithms can analyze large amounts of energy consumption data to identify unusual patterns that may indicate energy theft while highlighting emerging opportunities and challenges in adopting the machine learning base solution. The study highlighted the advantages of machine learning solutions for detecting energy theft and the likely threats associated with its use. Machine Learning models can handle complex and non-linear relationships between energy consumption readings over time and identify the readings that deviate from the modeled relationships as anomalies. This study seeks to present an overview of electricity theft detection solutions and the application of machine learning techniques to tackle the challenge of energy theft. The review revealed that Time-Series and Deep Reinforcement Machine Learning Models were the least used ML Models due to the volume of data and amount of computational resources they required to train the model.