Wind Energy Forecasting at Different Time Horizons with Individual and Global Models

This paper has been presented at: 14th IFIP International Conference on Artificial Intelligence Applications and InnovationsIn this work two different machine learning approaches have been studied to predict wind power for different time horizons: individual and global models. The individual approach constructs a model for each horizon while the global approach obtains a single model that can be used for all horizons. Both approaches have advantages and disadvantages. Each individual model is trained with data pertaining to a single horizon, thus it can be specific for that horizon, but can use fewer data for training than the global model, which is constructed with data belonging to all horizons. Support Vector Machines have been used for constructing the individual and global models. This study has been tested on energy production data obtained from the Sotavento wind farm and meteorological data from the European Centre for Medium-Range Weather Forecasts, for a 5 × 5 grid around Sotavento. Also, given the large amount of variables involved, a feature selection algorithm (Sequential Forward Selection) has been used in order to improve the performance of the models. Experimental results show that the global model is more accurate than the individual ones, specially when feature selection is used.The authors acknowledge financial support granted by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R

DRIMPAC—Unified Demand Response Interoperability Framework Enabling Market Participation of Active Energy Consumers

Residential and tertiary buildings are responsible for 44% of final energy consumption in Europe. However, they are currently not engaged in Demand Response (DR) activities due to technology-related and consumer-related roadblocks. One of the main technological roadblocks is the extreme fragmentation of protocols, data models and standards for Building Energy Management (BEM) systems and Building to Grid (B2G) communications. DRIMPAC is an EU-funded Innovation Action that aims to address the interoperability gaps and standards fragmentation in the residential and commercial buildings Demand Response domain aiming to reduce costs for all involved actors and make DR more attractive for the end prosumer

Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study

We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs), Artificial Neural Networks (ANNs), Fuzzy Expert Systems (FESs), and Support Vector Machines (SVMs). The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns from a well-known benchmark biometrics database, and the validation/testing took place with patterns from the same database which were not included in the training dataset. The comparison of the algorithms reveals that the biometrics fusion system is superior to the original unimodal systems and also other fusion schemes found in the literature

Unobtrusive Multimodal Biometric Authentication: The HUMABIO Project Concept

Human Monitoring and Authentication using Biodynamic Indicators and Behavioural Analysis (HUMABIO) (2007) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state of the art sensorial technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system which utilizes a biodynamic physiological profile, unique for each individual, and advancements of the state-of-the art in behavioural and other biometrics, such as face, speech, gait recognition, and seat-based anthropometrics. Several shortcomings in biometric authentication will be addressed in the course of HUMABIO which will provide the basis for improving existing sensors, develop new algorithms, and design applications, towards creating new, unobtrusive biometric authentication procedures in security sensitive, controlled environments. This paper presents the concept of this project, describes its unobtrusive authentication demonstrator, and reports some preliminary results

Dynamic Wireless Charging for More Efficient FEVs: The Fabric Project Concept

This paper describes the technology avenues that will be explored in FABRIC, an FP7 Integrated Project, for accomplishing and evaluating the dynamic, wireless charging of future fully electric vehicles. Initially, the charging modes supported by FABRIC are specified in detail. The preliminary high-level conceptual system architecture follows, and afterwards the wireless charging technologies that will be developed and tested are presented. Finally there is a discussion on the challenges that are anticipated during the design, prototyping and testing phases