Abstract
In recent years there has been a series of documents such as the European Strategy 20-20-20 to address the issue of energy efficiency in various sectors of activity. Your objective is to reduce 20% of energy consumption, 20% of GHG emissions (Greenhouse Gases) and get 20% of the energy consumed is from renewable sources [1]. The reduction of losses in the conductors of a sustainable street lighting installation as a technique for managing energy consumption, also allows the reduction of emissions of greenhouse gases, accounted for in this paper. Reduction of losses in the conductors of a sustainable street lighting, allowing a better use of the installed power, which can be an important issue, particularly allows increasing the weight of the renewable energies.

Abstract
During the last years, several plans for offshore grid development in Europe have been under discussion as a result of the need for the integration of large amounts of offshore wind power, providing additional transmission capacity and building adequate conditions for the single European electricity market. From the system operation viewpoint, technical constraints justify the need for adopting High Voltage Direct Current (HVDC) technology with Voltage Source Converters (VSC) for offshore grid development rather than conventional AC connections. Additionally, reliability and flexibility of operation is pushing for the development of the Multi-Terminal DC (MTDC) grid concept, by which a set of offshore wind farms is connected to a set of onshore AC nodes via a (possibly meshed) DC grid structure. This concept is in line with the envisioned plans for the development of the pan-European Transmission System. A broad range of aspects related to MTDC grids planning, operation, and technology was dealt within the TWENTIES European research project (2009-2013). In particular, this paper presents some results regarding the impact of MTDC systems on the AC systems they are connected to: specific focus is devoted to provision of advanced ancillary services (primary frequency controls, Fault Ride Through capability to AC faults, reactive power support), to the possible contribution of the MTDC grid in enhancing the operational security of AC systems and in restarting them after partial or complete blackouts. To emphasize the advanced functionalities that can be provided for overall system operation, this MTDC grid is referred to in the paper as "DC Grid" (DCG). Simulation results illustrate the potential benefits for the AC systems deriving from the described advanced control functionalities of DCGs. The work has been carried out within TWENTIES Working Package 5.

Abstract
Incremental learning from data streams is increasingly attracting research focus due to many real streaming problems (such as learning from transactions, sensors or other sequential observations) that require processing and forecasting in the real time. In this paper we deal with two issues related to incremental learning - prediction accuracy and prediction explanation - and demonstrate their applicability on several streaming problems for predicting electricity load in the future. For improving prediction accuracy we propose and evaluate the use of two reliability estimators that allow us to estimate prediction error and correct predictions. For improving interpretability of the incremental model and its predictions we propose an adaptation of the existing prediction explanation methodology, which was originally developed for batch learning from stationary data. The explanation methodology is combined with a state-of-the-art concept drift detector and a visualization technique to enhance the explanation in dynamic streaming settings. The results show that the proposed approaches can improve prediction accuracy and allow transparent insight into the modeled concept.

Abstract
A dual-core fiber in which one of the cores is doped with germanium and the other with phosphorus is used as an in-line Mach-Zehnder dispersive interferometer. By ensuring an equal length but with different dispersion dependencies in the interferometer arms (the two cores), high-sensitivity strain and temperature sensing are achieved. Opposite sensitivities for high and low wavelength peaks were also demonstrated when strain and temperature was applied. To our knowledge this is the first time that such behavior is demonstrated using this type of in-line interferometer based on a dual-core fiber. A sensitivity of (0.102 +/- 0.0020 nm/mu epsilon, between 0 and 800 mu epsilon) and (-4.2 +/- 0.2 nm/degrees C between 47 degrees C and 62 degrees C) is demonstrated. (C) 2014 Optical Society of America

Abstract
There are several new emerging environments, generating data spatially spread and interrelated. These applications reinforce the importance of the development of analytical systems capable to sense the environment and receive data from different locations. In this study we explore collaborative methodologies in a real-world problem: wind power prediction. Wind power is considered one of the most rapidly growing sources of electricity generation all over the world. The problem consists of monitoring a network of wind farms that collaborate by sharing information in a very short-term forecasting problem. We use an auto-regressive integrated moving average (ARIMA) model. The Symbolic Aggregate Approximation (SAX) is used in the selection of the set of neighbours. We propose two collaborative methods. The first one, based on a centralized management, exchange data-points between nodes. In the second approach, correlated wind farms share their own ARIMA models. In the experimental work we use 1 year data from 16 wind farms. The goal is to predict the energy produced at each farm every hour in the next 6 hours. We compare the proposed methods against ARIMA models trained with data of each one of the farms and with the persistence model at each farm. We observe a small but consistent reduction of the root mean square error (RMSE) of the predictions.

Abstract
Inductive Logic Programming (ILP) is a well known approach to Multi-Relational Data Mining. ILP systems may take a long time for analyzing the data mainly because the search (hypotheses) spaces are often very large and the evaluation of each hypothesis, which involves theorem proving, may be quite time consuming in some domains. To address these efficiency issues of ILP systems we propose the APIS (And ParallelISm for ILP) system that uses results from Logic Programming AND-parallelism. The approach enables the partition of the search space into sub-spaces of two kinds: sub-spaces where clause evaluation requires theorem proving; and sub-spaces where clause evaluation is performed quite efficiently without resorting to a theorem prover. We have also defined a new type of redundancy (Coverage-equivalent redundancy) that enables the prune of significant parts of the search space. The new type of pruning together with the partition of the hypothesis space considerably improved the performance of the APIS system. An empirical evaluation of the APIS system in standard ILP data sets shows considerable speedups without a lost of accuracy of the models constructed.