Abstract
The electricity sector has been subjected to major changes in the last few years. Previously, there existed a regulated system where electric companies could know beforehand the amount of energy each generator would produce, hence basing their largely operational strategy on cost minimization in order to increase their profits. In Spain, from 1988 till 1997, electricity prices were established by the 'Marco Legal Estable' Stable Legal Framework, where the Ministry of Industry and Energy acknowledged the existence of certain generation costs related to each type of technology. It was an industrial sector with no actual competition and therefore, with very few controllable risks. In the aftermath of the electricity market liberalization competition and uncertainty arose. Electricity spot prices became highly volatile due to the specific characteristics of electricity as a commodity. Long-term contracts allowed for hedge funds to act against price fluctuation in the electricity market. As a consequence, developing an accurate electricity price forecasting model is an extremely difficult task for electricity market agents. This work aims to propose a methodology to improve the limitations of those methodologies just using historical data to forecast electricity prices. In this manner, and in order to gain access to more recent data, instead of using natural gas prices and electricity load historical data, a regression model to forecast the evolution of natural gas prices, and a model based on artificial neural networks (ANN) to forecast electricity loads, are proposed. The results of these models are used as input for an electricity price forecast model. Finally, and to demonstrate the effectiveness of the proposed methodology, several study cases applied to the Spanish market, using real price data, are presented.

Abstract
Physical disability can, in certain cases, be a barrier for traditional human-computer interaction based on keyboard and mouse devices. Alternative ways of interaction based on computer vision may be successfully adapted in particular cases of disability. This paper purposes a vision-based assistive technology to help a child with a degenerative neuromuscular disease to interact with the computer through facial expression recognition. The proposed algorithm was evaluated in images extracted from videos of the child and the preliminary results indicate that computer-interaction via facial expression recognition can break down barriers for people with reduced mobility regarding their relation with computers.

Abstract

Abstract
We describe a data mining workflow for predictive maintenance of the Air Pressure System in heavy trucks. Our approach is composed by four steps: (i) a filter that excludes a subset of features and examples based on the number of missing values (ii) a metafeatures engineering procedure used to create a meta-level features set with the goal of increasing the information on the original data; (iii) a biased sampling method to deal with the class imbalance problem; and (iv) boosted trees to learn the target concept. Results show that the metafeatures engineering and the biased sampling method are critical for improving the performance of the classifier.

Abstract
In this paper, a multi energy system (MES) model incorporating the traffic behavior of plug-in electric vehicles (PEVs) is proposed. It is assumed that in a micro MES two charging options are available for the PEVs: the home charging (HC) stations and the PEV parking lot (PL). The operation of these elements within the micro MES concept is studied. The matrix model of the micro MES is adapted to enable the integration of PL and HC. Moreover, the traffic flow of the PEVs is added to the model as an input to the micro MES. The model is tested for various case studies and possible traffic behavior between the PL and HC. The results show that the presence of these two elements leads to effective integration of reduced system operation costs.

Abstract
The aim of this work is to develop an application for Android able to classifying urban sounds in a real life context. It also enables the collection and classification of new sounds. To train our classifier we use the UrbanSound8K data set available online. We have used a hybrid approach to obtain features, by combining SAX-based multiresolution motif discovery with Mel-Frequency Cepstral Coefficients (MFCC). We also describe different configurations of motif discovery for defining attributes and compare the use of Random Forest and SVM algorithms on this kind of data.