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In recent years, the population growth in urban areas of Latin American cities has resulted in an increase in demand for electricity in a dispersed manner, bringing challenges to the planning of distribution systems to supply this demand. In addition, incentives for the installation of distributed generation make it necessary to carry out analyzes with a spatial perspective to determine the places of impact in the electricity distribution networks. Geographic information systems are computational tools that allow the processing of data with geographic reference. These systems can collaborate in the visualization of the socioeconomic characteristics and the variables distributed in the zone of study, being able to provide information to the distribution planners. This work shows computational tools that will help distribution utilities, using techniques available in geographic information systems to characterize the local factors in concession zone of the distribution utilities.

Survey calibration is a widely used method to estimate the population mean or total score of a target variable, particularly in medical research. In this procedure, auxiliary information related to the variable of interest is used to recalibrate the estimation weights. However, when the auxiliary information includes qualitative variables, traditional calibration techniques may be not feasible or the optimisation procedure may fail. In this article, we propose the use of linear calibration in conjunction with a multidimensional scaling-based set of continuous, uncorrelated auxiliary variables along with a suitable metric in a distance-based regression framework. The calibration weights are estimated using a projection of the auxiliary information on a low-dimensional Euclidean space. The approach becomes one of the linear calibration with quantitative variables avoiding the usual computational problems in the presence of qualitative auxiliary information. The new variables preserve the underlying assumption in linear calibration of a linear relationship between the auxiliary and target variables, and therefore the optimal properties of the linear calibration method remain true. The behaviour of this approach is examined using a Monte Carlo procedure and its value is illustrated by analysing real data sets and by comparing its performance with that of traditional calibration procedures.

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Artículo resultado de investigación derivado del proyecto: órdenes estocásticos, medidas de riesgo y programación estocástica,