Abstract

Abstract
This paper analyzes the implementation of new technologies in network industries through the development of a suitable regulatory scheme. The analysis focuses on Smart Grid (SG) technologies which, among others benefits, could save operational costs and reduce the need for further conventional investments in the grid. In spite of the benefits that may result from their implementation, the adoption of SGs by network operators can be hampered by the uncertainties surrounding actual performances. A decision model has been developed to assess the firms' incentives to invest in "smart" technologies under different regulatory schemes. The model also enables testing the impact of uncertainties on the reduction of operational costs, and of conventional investments. Under certain circumstances, it may be justified to support the development and early deployment of emerging innovations that have a high potential to ameliorate the efficiency of the electricity system, but whose adoption faces many uncertainties.

Abstract
With the advent of restructuring electricity sector and smart grids, combined with the increased variability and uncertainty associated with electricity market prices (EMP) signals and players' behavior, together with the growing integration of renewable energy sources, enhancing prediction tools are required for players and different regulators agents to face the non-stationarity and stochastic nature of such time series, which must be capable of supporting decisions in a competitive environment with low prediction error, acceptable computational time and low computational complexity. Hybrid and evolutionary approaches are good candidates to surpass most of the previous concern considering time series prediction. In this sense, this work proposes a hybrid model composed by a novel combination of differential evolutionary particle swarm optimization (DEEPSO) and adaptive neuro-fuzzy inference system (ANFIS) to predict, in the short-term, the wind power and EMP, testing its results with real and published case studies, proving its superior performance within a robust prediction software tool. © 2016 IEEE.

Abstract

Abstract
Knowledge of walls and other structural elements construction characteristics and techniques is mandatory in the maintenance and restoration of historical buildings. Such information is obtained from documents or by comparing with monuments of the same period. Documents are difficult to obtain and comparison with other buildings can be inaccurate. The use of direct invasive techniques may be considered but they are likely to damage structures or compromise buildings stability. Thus, indirect high-resolution methods are adapted to these investigations. This work describes a GPR and a Seismic Transmission Tomography survey to investigate the walls and columns of the 14th century Batalha Abbey (UNESCO heritage site). High-resolution GRP data revealed the dimensions and thicknesses of different walls and the structure and nature of materials inside them. The Seismic Transmission Tomography survey, supported by high-resolution photogrammetry scanning for accurate positioning of sources and geophones, was carried out to investigate the Abbey columns. Seismic velocities distribution inside the columns characterized their interior and clarified the nature of the materials used. Columns construction followed the same technique and used the same materials. However, each column depicts a different velocity zonation that could correspond to different stages of deterioration for each one.

Abstract