Abstract
Micro grids represent an emergent vision to address the challenges imposed by recent trends in smart electrical grids, where the large-scale integration of distributed energy production units plays an important role. Nevertheless, the realization of this vision requires the use of advanced intelligent approaches to manage the micro grids elements, such as distributed renewable energy production units, loads and storage devices. Multi-agent systems provide a suitable framework to design and implement such systems, where autonomous agents are endowed with predictive data analytics capabilities, e.g., for the prediction of renewable energy production and consumption, taking advantage of the large amount of data produced in these environments. In this context, this paper presents an agent-based system for the management of micro grids, where predictive capabilities were embedded in agents to provide real-time data analytics. The proposed approach was applied to an experimental case study where a set of predictive models was tested for short and long term forecasting of the energy produced by photovoltaic units.

Abstract

Abstract

Abstract
The era of mass customization of goods forces manufacturing systems to promote agility, flexibility and responsiveness, leading to complex and unpredictable systems. Such challenges have an impact in terms of the system responsiveness and adaptation, production costs, product quality, etc. In order to improve those aspects, some flexible control manufacturing paradigms were proposed offering elasticity to change available skills and provide new services. However, the understanding of when and how to (self-) reconfigure the system aiming to perform a fast changeover, is a crucial issue. This work proposes a self-organizing multi-agent system approach for an efficient and on the fly reconfiguration of services in the manufacturing domain. Besides self-organizing techniques, other dimensions, e.g., "social-based" trust and QoS metrics, are used to ensure a constant QoS in an agile production system. The insertion of intelligent agents facilitates the improvement of strategies that perform the service reconfiguration, and in addition, permits to understand when and how self-reconfiguration takes place in order to allow a continuous improvement of the system performance. Additionally, this work addresses solutions for real industrial applications, being aligned with some characteristics of the Industrie 4.0 initiative, namely the distributed intelligence and self-* methods, e.g. self-adaptation, self-organization and self-configuration.

Abstract

Abstract
The Biblioteca Digital de Arte (BDArt) Digital Library hosted by the Thematic Repository at the University of Porto (Repositório Tem ático da U.Porto) aggregates documents from the library and the archive collections belonging to the Fine Arts School of the University of Porto (Faculdade de Belas Artes da U.Porto). This school has a museum collection containing a significant set of world-class ob- jects managed with distinct processes and tools from those currently used in libraries and archives elsewhere. Interoperability between the collections of the archive, the library, and the museum is necessary because many works allocated to different collections are closely related and can only be seen as a whole by cross-collection search functionalities. The goal of this work, the first of its kind to be developed at the University of Porto (U. Porto), is to integrate the museum collection with archives and library collections in the repository and to use an open-source technology (DSpace). Our experiment involved the selection of appropriate representations of the objects and the definition of a metadata crosswalk between the original metadata standards and qualified Dublin Core. As a result, we created the BDA Museum Collection as a BDArt subcom- munity using an XML export procedure that we expect to be helpful in future developments of other museum collections in the Thematic Repository at U.Porto. © Isabel Barroso, Nadia Hartmann, and Cristina Ribeiro.