Abstract
For a long time, controlling AC motors in an efficient way, was a task only in the reach of some very specific electronic design Engineers. Now, with the up rise of Integrated Platforms it is possible to implement, with almost no effort, the desired application, customizing almost everything at the reach of a simple and user friendly software interface. However, the simplicity in control and monitoring has a major settle back. As expected, such products, very desirable by the market, are only sold in conjunction with an evaluation platform. Sometimes this platform does not fit the consumer needs and buying the full package is not a satisfactory solution, especially if it is just to get the control and monitoring software. In this paper, it will be shown that low cost alternatives are possible. Using free tools and manufacturer's documentation, it is simple to implement Motor Control with the desired IC by means of a very intuitive and complete Graphical Unit Interface (GUI), built attending our special needs.

Abstract
Factors such as uncertainty associated to fuel prices, energy demand and generation availability, are on the basis of the agents major concerns in electricity markets. Facing that reality, price forecasting has an increasing impact in agents' activity. The success on bidding strategies or on price negotiation for bilateral contracts is directly dependent on the accuracy of the price forecast. However, taking decisions based only on a single forecasted value is not a good practice in risk management. The work presented in this paper makes use of artificial neural networks to find the market price for a given period, with a certain confidence level. Historical information was used to train the neural networks and the number of neural networks used is dependent of the number of clusters found on that data. K-Means clustering method is used to find clusters. A study case with real data is presented and discussed in detail.

Abstract

Abstract
It is well known that optimization problems for the decision-making process in real environments should consider uncertainty to attain robust solutions. Although this uncertainty has been usually modelled using probability theory, assuming a random origin, possibility theory has emerged as an alternative uncertainty model when statistical information is not available, or when imprecision and vagueness have to be considered. This article proposes two different criteria to obtain robust solutions for linear optimization problems when the objective function coefficients are modelled with possibility distributions. To do so, chance constrained programming is used, leading to equivalent crisp optimization problems, which can be solved by commercial optimization software. A simple case example is presented to illustrate the use of the proposed methodology.

Abstract
An artificial neural network concept has been developed for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA). A new methodology for mapping the neural network into a rule-based inference system is described. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a Fuzzy Inference System. Some studies are reported, illustrating the good results obtained.

Abstract
This paper provides evidence that Evolutionary Particle Swarm Algorithms outperform Genetic Algorithms in deriving optimal strategic decisions for an Energy Retailer, in the framework of a complex simulation of a multiple energy market, based on an Intelligent Agent FIPA-compliant open source platform.