Using GA for the stratified sampling of electricity consumers

Non-technical energy losses mostly arise from illegal use of energy and force energy distribution companies to inspect large batches of clients in order to make decisions on actions for reducing these losses. Since an exhaustive inspection is impractical due to the high inspection cost and the very large number of clients, a carefully designed sampling procedure is needed. A useful strategy is offered by stratified sampling based on a division of the clients into homogeneous subgroups (strata). In this work we formulate the stratification task as a non-linear restricted optimization problem, in which the variance of overall energy loss due to the fraudulent activities is minimized. Solving this problem analytically is difficult and an exhaustive algorithm is intractable even for small problem instances. Therefore, we propose a Genetic Algorithm for finding practical solutions for the problem. Numerical experiments and a comparison with Simulated Annealing algorithm and a proportional allocation scheme are presented.