Abstract
This paper is written with the didactic purpose of exploring and indicating possibilities to power companies in the Balkan region for the application of adaptive neuro-fuzzy inference system (ANFIS) models in load prediction with real load data set. ANFIS models were trained and tested using 15-minute load data collected in Portugal by the electric power company EDP during a 42 day period. Simulation results gave promising results especially considering small size of used data set. Although the objective of the paper is to demonstrate possibilities for practical implementation, further research and improvement including the contributions of similar approaches in the world must he done.

Abstract
The text explains that the fuzzy approaches have the objective to bring the decision process in planning closer to the decision maker, by allowing him to understand better the diversity of aspects that must be considered in planning decisions and helping the decision process while keeping, as much information as possible, represented in the definition of fuzzy sets. The paper shows that the qualitative aspects of uncertainty, risk and decision making may be adequately modeled with a fuzzy set approach. It could help the decision maker guiding him towards a decision that takes in account uncertainty in the future, the multiple criteria evaluation of plans, as well as hedging policies.

Abstract
This work presents a model developed to evaluate the Dynamic Adaptation of an Electric Energy Distribution System (EEDS) respect to its planning for a given period of Tariff Control. The model is based on a two-stage strategy that deals with the mid/short-term and long-term planning, respectively. The starting point for modeling is brought about by the results from a multi-attribute method based on Fuzzy Dynamic Programming and on Analytic Hierarchy Processes (FDP + AHP) for a mid/short-term horizon. Such a method produces a set of possible evolution trajectories which can be defined as satisfactory when they evolve above a given risk threshold that the planner is willing to accept. Then, the decision-making activities within the framework of the Analytical Hierarchy Processes are those tasks that allow defining a vector for dynamic adaptation of the system, which is directly associated to an eventual series of imbalances that take place during its evolution.

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
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
This paper discusses some issues related with the growing importance of wind power and in modern power systems and some challenges raised by the emergence of distributed generation, and how computational intelligence and other modern techniques have been able to provide valuable results in solving the new problems. It presents some solutions obtained with a number of computational intelligence techniques and their application to real cases. © TÜBITAK.