Estimation of non-technical loss rates by regions

Abstract

Identifying vulnerable regions to non-technical losses allows more assertive combat against them. In this context, this paper presents a spatiotemporal methodology composed of two modules, spatial and temporal, to assist distribution companies in action planning to decrease the rates of non-technical losses by region. The spatial module contains a neighborhood structure based on the similarity among small regions named ‘‘neighborhood by the similarity of attributes’’, which improves the characterization of non-technical losses actions performed by end-consumers. That neighborhood structure is incorporated as an input parameter into a hierarchical spatial autoregressive regression model to represent the relationships between inhabitants. On the other hand, the temporal module uses a linear mixed-effects model to consider future values that are subject to the actions of consumers or distribution companies. The proposed methodology is applied to a medium-sized city with approximately 200,000 inhabitants, considering the inspections carried out by a Brazilian distribution utility. The proposal identified the future non-technical loss state in all the city’s regions with values greater than 69% of the success rate in identifying NTL to residential, commercial, and industrial consumer classes.