Empowered by the emergence of novel information and communication technologies (ICTs) such as sensors and high-performance digital communication systems, Europe has adapted its electricity distribution network into a modern infrastructure known as a smart grid (SG). The benefits of this new infrastructure include precise and real-time capacity for measuring and monitoring the different energy-relevant parameters on the various points of the grid and for the remote operation and optimization of distribution. Furthermore, a new user profile is derived from this novel infrastructure, known as a prosumer (a user that can produce and consume energy to/from the grid), who can benefit from the features derived from applying advanced analytics and semantic technologies in the rich amount of big data generated by the different subsystems. However, this novel, highly interconnected infrastructure also presents some significant drawbacks, like those related to information security (IS). We provide a systematic literature survey of the ICT-empowered environments that comprise SGs and homes, and the application of modern artificial intelligence (AI) related technologies with sensor fusion systems and actuators, ensuring energy efficiency in such systems. Furthermore, we outline the current challenges and outlook for this field. These address new developments on microgrids, and data-driven energy efficiency that leads to better knowledge representation and decision-making for smart homes and SGs.

This paper presents an architecture which applies document similarity measures to the documentation produced during the phases of software development in order to generate recommendations of process and people metrics for similar projects. The application makes a judgment of similarity of the Service Provision Offer (SPO) document of a new proposed project to a collection of Project History Documents (PHD), stored in a repository of unstructured texts. The process is carried out in three stages: firstly, clustering of the Offer document with the set of PHDs which are most similar to it; this provides the initial indication of whether similar previous projects exist, and signifies similarity. Secondly, determination of which PHD in the set is most comparable with the Offer document, based on various parameters: project effort, project duration (time), project resources (members/size of team), costs, and sector(s) involved, indicating comparability of projects. The comparable parameters are extracted using the GATE Natural Language Processing architecture. Lastly, a recommendation of metrics for the new project is made, which is based on the transferability of the metrics of the most similar and comparable PHD extracted, here referred to as recommendation.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project SONAR (TSI-340000-2007-212), GODO2 (TSI- 020100-2008-564) and SONAR2 (TSI-020100-2008- 665) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140- C03-02).Publicad