Assessing Severity of Non-technical Losses in Power using Clustering Algorithms

Electricity or power has become an essential service which is instrumental to many social, economic and technological developments worldwide. However, power utilities competing in emerging markets are confronted with challenges of shortfall in revenue due to collection losses known as Non-Technical losses (NTLs). Assessing and plummeting NTL to increase revenue, and reliability of the distribution of power remains a top priority for the utilities and regulators. The amount of data generated by power utilities offers exceptional opportunities owing to Machine Learning (ML) techniques to better understand the payment and consumption patterns of consumers. Consumers on the same profile can be segmented into clusters of similar behaviour through Customer segmentation. This paper applies k-means and DBSCAN clustering algorithms to segment customers by a measure of their bill payments, consumption and tariff plans then grouped into clusters. The clusters encountered, shows different categories of consumers in relation to their obligation profiles and tariff plans. K-means gives a better visualization in terms of cluster assignment. Dense clusters and outliers were viewed in DBSCAN.