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.

Abstract
Expansion planning of electric power or natural gas networks has become a consuming time engineering task due to the multiple factors that must be taken into account: technical, economic, environmental or social factors. This paper presents an advanced model of natural gas distribution networks based on Geographic Information Systems (GIS) methodologies, to evaluate the cost associated to the expansion of these networks in order to meet a demand imposed by the user in any location of a region. The experimental results show that this approach produces visual and useful information for planning the expansion of natural gas distribution networks.

Abstract

Abstract
This paper presents a method used to validate a spatial load forecasting model based on fuzzy systems implemented in a Geographical Information System. The validation process confirms the adequacy of the rule base, and also it is strictly necessary to define the confidence intervals associated to the predicted spatial demand.