Machine learning-based anomaly detection in residential electricity usage patterns using meter data - Case study (Msunduzi Municipality)

One of the key methods for identifying anomalous activities, such as electricity theft, metering errors, cyber-attacks, and technical losses by distribution network operators (DNOs), is the detection of anomalies in residential energy consumption. In this paper, a machine learning-based method is utilized based on irregularities in residential electricity consumption data is presented to detect and predict electricity theft. The paper uses meter data energy usage for over 20000 customers in Msunduzi Municipality for the prediction. The study assesses the method's efficacy and looks at ways to incorporate it into the current utility infrastructure to provide a proactive means of spotting possible theft occurrences and boost the dependability and efficiency of energy distribution networks. Through empirical validation and testing, this study advances methods for identifying electricity and promoting integrity and sustainability in the energy industry. The results show that Machine learning is effective in detecting cases of electricity theft based on purchase records.