2020
Autores
Novotná, L; Martins, I; Moreira, A;
Publicação
Foreign Direct Investments
Abstract
2020
Autores
Terras, JM; Simão, T; Rua, D; Coelho, F; Gouveia, C; Bessa, R; Baumeister, J; Prümm, RI; Genest, O; Siarheyeva, A; Laarakkers, J; Rivero, E; Bosco, E; Nemcek, P; Glennung, K;
Publicação
CIRED - Open Access Proceedings Journal
Abstract
This study offers an overview of the H2020 InterConnect project, which targets the relation between smart homes and distribution grids. The project vision is to produce a digital marketplace, using an interoperable marketplace toolbox and Smart appliances REference Ontology (SAREF) compliant Internet of Things (IoT) reference architecture as the main backbone, through which all SAREF-ized services, compliant devices, platform enablers and applications can be downloaded onto IoT and smart grid digital platforms. Energy users in buildings, either residential or non-residential, manufacturers, distribution grid operators and the energy retailers will work together towards the demonstration of the smart energy management solutions in seven connected large-scale test-sites in Portugal, Belgium, Germany, the Netherlands, Italy, Greece and France. This study depicts how InterConnect project will enhance the relation and the interconnectivity between smart buildings and grids safeguarding the definition of the role of each stakeholder in energy and non-energy services. © 2020 Institution of Engineering and Technology. All rights reserved.
2020
Autores
Traqueia, A; Nogueira, S; Barbosa, B; Costa, F; Dias, GP; Filipe, S; Melo, A; Rodrigues, C; Santos, CA;
Publicação
14TH INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT CONFERENCE (INTED2020)
Abstract
Despite the growing popularity of Vocational Education and Training (VET), which is mainly oriented towards labour market inclusion, literature shows that there is still stigmatization and association of those programmes with lower quality training offers when compared to the so-called general secondary education. The main aim of this article is to shed light on the differences in students' sociodemographic profiles between the two education alternatives. It adopts a quantitative approach, exploring secondary data collected by national (e.g., Ministry of Education) and international (e.g., OECD) organizations regarding secondary education students in Portugal. Results confirm that students in VET secondary education have a different sociodemographic profile, namely in terms of income, parents' academic qualifications and professional activities, thus presenting a clear lower social status than students in general secondary education. Indeed, VET is more common in Portuguese secondary schools with a student population originating from more disadvantaged socioeconomic backgrounds. This study also highlights the limitations of the available secondary data, suggesting a set of variables and hypotheses built on contributions from extant literature that may enable a better understanding of the reasons behind the differences in students' profiles. Implications for schools and decision makers, as well as suggestions for future research, are also presented.
2020
Autores
Meneghetti, R; Costa, AS; Miranda, V; Ascari, LB;
Publicação
ELECTRIC POWER SYSTEMS RESEARCH
Abstract
This paper introduces an Information Theoretic approach for Generalized State Estimation, aiming at achieving reliable topology and state variables co-estimation results, even in the presence of both topology errors and gross measurements. Attention is focused on the final bad data processing stage in which only relevant parts of the power network are represented at the bus-section level. The proposed generalized strategy applied at physical level relies on the superior outlier rejection properties of state estimators based on Maximum Correntropy, a concept borrowed from Information Theoretical Learning. A single objective function unifies the treatment of analog measurements and topology data, leading to an algorithm that does not require re-estimation runs for bad data suppression, and is simpler and more efficient than previously proposed co-estimation methods. Case studies conducted for distinct test-systems are presented, including various types of topology errors and simultaneous occurrence of topology and gross measurement errors. The results suggest that the proposed information-theoretic co-estimation algorithm is able to successfully provide bad data-free real-time network models even in the presence of multiple topology errors, simultaneous gross measurements and inaccurate topology information. Finally, additional tests confirm its superior computational performance as compared with other co-estimation algorithms.
2020
Autores
Nikoobakht, A; Aghaei, J; Shafie Khah, M; Catalao, JPS;
Publicação
IEEE TRANSACTIONS ON SMART GRID
Abstract
This paper studies the role of electricity demand response program (EDRP) in the co-operation of the electric power systems and the natural gas transmission system to facilitate integration of wind power generation. It is known that time-based uncertainty modeling has a critical role in co-operation of electricity and gas systems. Also, the major limitation of the hourly discrete time model (HDTM) is its inability to handle the fast sub-hourly variations of generation sources. Accordingly, in this paper, this limitation has been solved by the operation of both energy systems with a continuous time model (CTM). Also, a new fuzzy information gap decision theory (IGDT) approach has been proposed to model the uncertainties of the wind energy. Numerical results on the IEEE Reliability Test System (RTS) demonstrate the benefits of applying the continuous-time EDRP to improve the co-scheduling of both natural gas and electricity systems under wind power generation uncertainty.
2020
Autores
Ferreira, PJS; Cardoso, JMP; Moreira, JM;
Publicação
Comput.
Abstract
The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online than offline due to time and memory restrictions in the online mode. Some methods propose decreasing the high computational costs of kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-Sensitive Hashing (LSH). However, embedded kNN implementations also need to address the target device’s memory constraints, especially as the use of online classification needs to cope with those constraints to be practical. This paper discusses online approaches to reduce the number of training instances stored in the kNN search space. To address practical implementations of HAR systems using kNN, this paper presents simple, energy/computationally efficient, and real-time feasible schemes to maintain at runtime a maximum number of training instances stored by kNN. The proposed schemes include policies for substituting the training instances, maintaining the search space to a maximum size. Experiments in the context of HAR datasets show the efficiency of our best schemes. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.