Abstract
This paper presents a new methodology for auto- mated route selection for the construction of new power lines, based on geographic information systems (GIS). It uses a dynamic programming model for route optimization. Environmental restrictions are taken into account together with all of the operating, maintenance, and equipment installation costs, including a new approach to the costs associated with the slope of the terrain crossed by the power lines. The computing and visual representation capacities of GIS are exploited for the selection of economic corridors, keeping the total costs under a threshold imposed by the user. Intensive simulation examples illustrate the power and flexibility of the proposed methodology.

Abstract
Distributed Generation (DG) facilities require, like other energy projects, a sitting review process to acquire the permits and approval needs for construction and operation. In this process different groups and individuals with different roles, interests and priorities are involved. This paper presents a Spatial Decision Support System (SDSS) that helps to identify permissible areas to install DG facilities. Wind energy facilities are used in this paper to exemplify the use of the SDSS. © 2001 IEEE.

Abstract
The evaluation of high-potential areas for integrating distributed resources is mainly dependent on the geographical characterization of resources, consumptions, energy technologies and infrastructures. The effect of distributed resources on electric distribution could be studied by a spatial evaluation of incremental costs. This paper presents a Spatial Support System (SSS), based on GIS methodologies, to evaluate the spatial impact caused by integration of distributed power resources on the power distribution network costs. © 2001 IEEE.

Abstract
This paper summarizes efforts in understanding the possible application of Information Theoretic Learning Principles to Power Systems. It presents the application of Renyi's Entropy combined with Parzen windows as a measure of information content of the error distribution in model parameter estimation in supervised learning. It illustrates the concept with an application to the prediction of power generated in a wind park, made by Takagi-Sugeno Fuzzy Inference Systems, whose parameters are discovered with an EPSO-Evolutionary Particle Swarm Optimization algorithm.

Abstract
Decision problems cannot be fully represented without underlying assumptions about the Decision Maker motivations and behavior. This paper describes one technique to build a rule model representing the interaction of preferences of a Decision Maker, by training a Fuzzy Inference System based on examples. © 2005 ISAP.

Abstract
Prediction models for generation from renewables are needed in the context of a power system with a diversified portfolio. The presentation will discuss a new criterion and procedure to develop prediction models based on Renyils Entropy combined with Parzen windows (an approach named Information Theoretic Learning) that is applied to wind prediction and suggested as a better training paradigm for fuzzy or neural systems.