Abstract
This paper describes a research where the main goal was to predict the future values of a time series of the hourly demand of Portugal global electricity consumption in the following day. In a preliminary phase several regression techniques were experimented: K Nearest Neighbors, Multiple Linear Regression, Projection Pursuit Regression, Regression Trees, Multivariate Adaptive Regression Splines and Artificial Neural Networks (ANN). Having the best results been achieved with ANN, this technique was selected as the primary tool for the load forecasting process. The prediction for holidays and days following holidays is analyzed and dealt with. Temperature significance on consumption level is also studied. Results attained support the adopted approach.

Abstract
Sensor networks act in dynamic environments with distributed sources of continuous data and computing with resource constraints. Learning in these environments is faced with new challenges: the need to continuously maintain a decision model consistent with the most recent data. Desirable properties of learning algorithms include: the ability to maintain an any time model; the ability to modify the decision model whenever new information is available; the ability to forget outdated information; and the ability to detect and react to changes in the underlying process generating data, monitoring the learning process and managing the trade-off between the cost of updating a model and the benefits in performance gains. In this chapter we illustrate these ideas in two learning scenarios - centralized and distributed - and present illustrative algorithms for these contexts. © 2007 Springer-Verlag Berlin Heidelberg.

Abstract
The evaluation of the urban quality of life has been an important aspect of the research concerning the contemporary city and an increasingly support to urban planning and management. As part of a project to monitor the quality of life in the city of Porto, a survey of the resident population was conducted in order to study the citizens' perceptions of their local quality of life and its evolution in recent years. The opinions of individuals on their level of satisfaction with various fields of the urban quality of life are systematised, as well as their integrated assessment. This analysis is complemented by a multivariate analysis that allows the grouping of the interviewees in large homogenous groups and their social and economic characterisation. Based on the results achieved, we try to highlight the usefulness of the qualitative analysis of the quality of life to support the definition of urban policies. © 2007 Springer Science + Business Media BV/The International Society for Quality-of-Life Studies (ISQOLS).

Abstract
Wind energy experiences in Portugal an increasing interest. Slightly more than 1700 MW were operating by the end of 2006, in a system with a global capacity of about 12 GW (8,5 GW peak demand). Several new wind farms are under construction and a considerable amount of connection points are or will be granted in the coming years. More than 5000 MW are expected to be connected to the grid around 2012, the global generating capacity being then about 16 GW. Clearly, a wind power forecasting system must be implemented that will help to deal with the significant penetration of the technology in the electrical system. A group of wind farm promoters, owning the majority of the capacity installed so far, ordered to a consortium of universities and research institutes the development of a forecasting tool, giving rise of the EPREV project, wholly financed by them. The system will have the following main characteristics: Wind speed and active power forecasting up to 72 hours; Evaluation of the forecasting uncertainty; Possibility of using the predictions of physical models and the information from the wind farm Supervisory Control And Data Acquisition (SCADA); Capacity of predicting only with SCADA information for very short term. The main components of the system are: A human-machine-interface, allowing the control of the system, the selection and aggregation of forecasting models and the visualization of results; A power forecasting model for individual wind turbines and for wind farms. A cascade of models is used, starting in the mesoscale simulation, with up to 2 km resolution. The outputs of the mesoscale models are corrected and statistically adapted to the fine scale conditions. Two models and different boundary conditions are run, in three nested domains (54x54, 18x18 and 6x6 km). The advantage of using a 2x2 km resolution is also tested. The statistical models are fed with recent information from the wind farms, after a learning process that made use of the historical information of its operation. Three different types of statistical models are employed: Power Curve Model (PCM), Auto Regressive (AR) and Neural Network Assembling Model (NNAM). The wind simulation at the wind farm scale is done both by linearized physical models and Computational Fluid Dynamics (CFD) models, namely using VENTOS®, a code developed at the University of Porto. The duration of the project is planned to be 1 year, including off-line tests of the complete system for 3 wind farms, for performance evaluation purposes.

Abstract
This paper presents a new and efficient methodology for network reconfiguration with optimal power flow based on Benders Decomposition approach. The objective minimizes the power losses, balancing load among the feeders and subject to the constraints: capacity limit of the branches, minimal and maximal limits of the substation or generator, minimum deviation of the nodes voltages and radial operation of the networks. A variant of the generalized Benders decomposition algorithm is applied for solving the problem, since the formulation can be embedded under two stages. The first one is the Master problem and Is formulated as Mixed Integer non-Linear Programming. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-Linear Programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an Optimal Power Flow and provides information to formulate the linear Benders cuts. The model is programmed in GAMS mathematical modeling language. The effectiveness of the proposal is demonstrated through an example extracted from the specialized literature.

Abstract
This paper presents the results of a CMOS-VLSI implementation of a realistic computational model proposed by Walter Freeman for the olfactory system. This model, in later years, has been studied for engineering applications such as auto-association and classification. The analogue nature of the model motivates analogue VLSI implementations. However, the dimension and complexity of such system poses many obstacles to an analogue electronic implementation; one such is the massive interconnectivity which size increases with the square of the number of inputs (channels). We suggest a multiplexing procedure that puts the burden of interconnectivity over a digital system that is simpler to design and makes the analogue system more treatable. The procedure naturally samples the signals. To avoid smoothing filters, a discrete-time solution was also employed. Although with such approach the time resolution is reduced, the advantages overcome the detriments. Previous work has shown that the model can be efficiently discretized using DSP techniques, resulting on a system that is able to predict, on sample-by-sample basis, the behaviour of the VLSI circuit, allowing for a simple and flexible way to adjust the circuit parameters. We present the measured circuit results that are further confronted with the digital implementation.