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Mediterranean Electrotechnical Conference (MELECON)

作者： Mohamad El Iaali Reza Razi Antoine Bruyere Bruno Francois Joao Soares Centrale Lille Junia ULR 2697 - L2EP Univ. Lille Arts et Metiers Institute of Technology Lille France GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

The growing integration of renewable energy sources and the widespread adoption of electric vehicles add complexity to power flows in distribution systems. Understanding the system dynamics and implementing control actions are imperative for effective grid management. While measurements in specific nodes are facilitated by smart meters, accurate sensors, communication networks, and electric vehicles, it is costly and, in some cases, impossible to obtain measurements at all nodes. In response to these challenges, this paper introduces a machine learning-based state estimation method, utilizing available measurements and estimating others. By harnessing data-driven techniques, the proposed method aims to provide accurate estimations of voltages and currents in distribution networks, offering valuable insights into flexibilities management and control processes. Specific case study is considered, confirming the efficiency of the suggested state estimation approach and providing insights into its flexibility across a range of scenarios. In addition to the mentioned benefits, the results validate the proposed method, demonstrating a small margin of error. This approach holds significant promises in improving situational awareness for power system operators and enhancing their understanding of the evolving grid dynamics.

关键词： Accuracy System dynamics Current measurement Power system dynamics Distribution networks Electric vehicles Smart meters

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Distribution Network Digital Twin: a Basic Machine Learning based Voltage Estimator

Distribution Network Digital Twin: a Basic Machine Learning ...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Mohamad El Iaali Reza Razi Antoine Bruyere Bruno Francois Joao Soares Univ. Lille Arts et Metiers Institute of Technology Centrale Lille Junia ULR 2697 - L2EP Lille France GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate LAboratory Polytechnic of Porto Porto Portugal

The increasing integration of renewable energy sources, and the widespread use of electric vehicles have introduced greater complexity and dynamics in power flows in distribution systems. With bidirectional power flows enhancing the observability of the grid becomes essential to identify critical feeders. In this context, data-driven methods offer a promising approach to enhance situational awareness for power system operators and improve their understanding of the system behavior under variabilities. This paper proposes a voltage estimation method based on a Machine Learning algorithm to address this challenge. By leveraging data-driven techniques, the proposed method aims to provide accurate and efficient voltage estimations in distribution networks, contributing to the effective management and control of modern power systems.

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Renewable Energy Communities Classification

Renewable Energy Communities Classification

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IEEE General Meeting Power& Energy Society

作者： Bruno Canizes João Costa Zita Vale GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate LAboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto Portugal Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto Portugal

It is clear and inevitable that the current electricity sector will change to a new model. For everyone in this value chain, the future holds new projects and challenges that will get harder and harder. Technological advances have led to the creation of new ideas, such as renewable energy communities, decentralized renewable energy generation, and even the storage of renewable energy. These things have given consumers a more active role in the electricity sector and helped them reach their environmental goals in a big way. With the rise of communities that use renewable energy, there is a need for new management tools, mostly in generation and load management. So, this paper suggests a platform that can group consumers and prosumers together based on their energy use and where they live and also classify those groups (communities). Two algorithms, K-means, and DBSCAN, are thought to be able to do this. With this work, it will be possible to check how each algorithm works and how well it puts players into communities. Nine cities in the north of Portugal were considered to show how the proposed platform could be used. The findings provide evidence that the developed platform is adequate, versatile, and reliable regarding classifying communities that use renewable energy.

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Ekrucami architecture-applications in healthcare domain

Frontiers in Artificial Intelligence and Applications

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Frontiers in Artificial Intelligence and Applications 2017年 298卷 140-152页

作者： Ramos, Carlos Martinho, Diogo Marreiros, Goreti Villarrubia, Gabriel De Paz, Juan F. Corchado, Juan M. Ko, Hoon Kim, Haeng-Kon Yoe, Hyun GECAD-Research Group Intelligent Engineering and Computing for Advanced Innovation Development Institute of Engineering-Polytechnic Porto Portugal Departamento de Informática y Automática Universidad de Salamanca Plaza de la Merced s/n Salamanca37008 Spain Department of Computer Science and Engineering Sungkyunkwan University Korea Republic of Daegu Catholic University Korea Republic of Suncheon National University Korea Republic of

ISBN: (纸本)9781614998037

Ambient Intelligence is a paradigm in Artificial Intelligence which promotes intelligent computing and responds to the necessities of every person in many different environments whether it is a home, classroom, a shopping, or a hospital. Nowadays we are increasingly seeing and talking about issues such as aging populations or the increase in the number of patients with chronic diseases (among other issues). Looking at the solutions available in this area it is necessary to develop more effective systems that can support people and improve their quality of life. This work is a brief study on EKRUCAmI architecture and its suitability to the healthcare sector. Some healthcare systems will be presented which have been designed to use EKRUCAmI architecture. © 2017 The authors and IOS Press.

关键词： Ambient intelligence

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Energy Consumption Forecasting using Neuro-Fuzzy Inference Systems: Thales TRT building case study

Energy Consumption Forecasting using Neuro-Fuzzy Inference S...

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IEEE Symposium Series on Computational Intelligence

作者： Aria Jozi Tiago Pinto Isabel Praca Sergio Ramos Zita Vale Benedicte Goujon Teodora Petrisor GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/ IPP) Porto Portugal Thales Research & Technology France Palaiseau France

Electrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the outcomes of the actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rule-based systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.

关键词： Energy consumption Forecasting Fuzzy rule-based systems TRT building

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A low-cost multi-agent system for physical security in smart buildings

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AIP Conference Proceedings 2024年第1期2919卷

作者： Tiago Fonseca Tiago Dias João Vitorino Luís Lino Ferreira Isabel Praça School of Engineering Polytechnic of Porto (ISEP/IPP) 4249-015 Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) 4249-015 Porto Portugal

Modern organizations face numerous physical security threats, from fire hazards to more intricate concerns regarding surveillance and unauthorized personnel. Conventional standalone fire and intrusion detection solutions must be installed and maintained independently, which leads to high capital and operational costs. Nonetheless, due to recent developments in smart sensors, computer vision techniques, and wireless communication technologies, these solutions can be integrated in a modular and low-cost manner. This work introduces Integrated Physical Security System (IP2S), a multi-agent system capable of coordinating diverse Internet of Things (IoT) sensors and actuators for an efficient mitigation of multiple physical security events. The proposed system was tested in a live case study that combined fire and intrusion detection in an industrial shop floor environment with four different sectors, two surveillance cameras, and a firefighting robot. The experimental results demonstrate that the integration of several events in a single automated system can be advantageous for the security of smart buildings, reducing false alarms and delays.

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Machine learning for rectal cancer prediction based on metabolic changes on amino acids

Machine learning for rectal cancer prediction based on metab...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： José Tavares Pedro Brandão Ivo Barros Jose Gonçalves Isabel Praça Lucia Lacerda Marisa Santos Computer Engineering Department Porto School of Engineering (ISEP) Porto Portugal UMIB-Unit for Multidisciplinary Research in Biomedicine (ICBAS) and ITR - Laboratory for Integrative and Translational Research in Population Health University of Porto Porto Portugal Coloretal Surgery Unit Surgery 1 Service Centro Hospitalar e Universitario de Santo Antonio Porto Portugal Biochemical Genetics Unit Centro de Genética Medica Centro Hospitalar e Universitario de Santo António Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Porto School of Engineering (ISEP) Porto Portugal

Machine learning is an area of Artificial Intelligence in which applying algorithms to a dataset makes it possible to predict results or even discover relationships that would be unnoticeable at first glance. Currently the amount of information available in different areas, especially in health care where data collection and analysis seek to define personalized medicine strategies. This is a field where using Machine Learning-based tools can assume a relevant role. This work presents a study of diverse classification algorithms in the area of machine learning applied to identification of amino acid profiles. The authors defined as a major objective to develop a new biomarker profile for prediction and prognosis of rectal cancer. The data involved in the training of classification algorithms refer to patients with metabolic diseases and rectal cancer. The best machine learning classification models will be tested to achieve the most effective decision support system for a most adequate treatment option selection in order to reduce the morbidity and mortality rate.

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Innovative Electricity Market Designs to Support a Transition to (Near) 100% Renewable Power System: First Results from H2020 TradeRES Project 22

Innovative Electricity Market Designs to Support a Transitio...

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22nd Wind and Solar Integration Workshop, WIW 2023

作者： Estanqueiro, Ana Strbac, Goran Chrysanthopoulos, Nikolaos Santos, Gabriel Johanndeiter, Silke Algarvio, Hugo Syse, Helleik Sperber, Evelyn Wang, Ni Jimenez, Ingrid Qiu, Dawei Vale, Zita Nienhaus, Kristina Kochems, Johannes Schimeczek, Christoph Sijm, Jos de Vries, Laurens Lopes, Fernando Couto, António Renewable Energies and Energy Efficiency Unit Laboratório Nacional de Energia e Geologia I.P. Estrada do Paço do Lumiar Lisboa Portugal Department of Electrical and Electronic Engineering Imperial College London London United Kingdom GECAD - Research Group on Intelligent Eng. and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida Porto Portugal EnBW Baden-Württemberg Department of Energy Economics Durlacher Allee 93 Karlsruhe76137 Germany Bityoga AS Prof. Olav Hansens vei 7A Stavanger4021 Norway Inst. of Networked Energy Systems Energy Systems Analysis Stuttgart Germany Energy Transition Studies TNO Radarweg 60 Amsterdam1043 NT Netherlands Faculty of Technology Policy and Management TU Delft Jaffalaan 5 2628 BX Netherlands

ISBN: (纸本)9781839539824

Developing innovative electricity market designs to facilitate a sustainable transition to (near) 100% renewable power systems while meeting societal needs is a crucial and actual topic of research. This article presents preliminary key findings from the H2020 European project TradeRES, addressing this critical topic. The project uses agent-based and optimization models to effectively capture the behaviour of different market players, and to analyse the current and future power system energy mixes of selected European case studies with different physical and spatial scales from: i) local energy communities and local energy markets (LEMs);ii) national/regional - the Netherlands, Germany, and Iberia (Portugal and Spain);and iii) pan-European energy markets. The first results on LEMs indicate a substantial economic benefit for participants and enhanced revenue streams for distributed energy resources, able to i) incentivise further decentralised investments;ii) promote the growth of variable renewable energy systems (vRES) and iii) increase flexibility at the local level. The outcomes are sensitive to the tariffs’ structure, while the retail sector competitiveness was identified as a critical parameter affecting its efficiency. For the pan-European and national/regional case studies, the first set of simulations had consistent outcomes, namely, by pointing out current design of energy-only markets to be insufficient to incentivize the high levels of vRES foreseen in Europe. Different support schemes (e.g., fixed market premia, contract for differences) were tested and results suggest they may play a relevant role in effectively covering the cost of vRES in a market environment. © Energynautics GmbH.

关键词： Power markets

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Improving the lifestyle behavior of type 2 diabetes mellitus patients using a mobile application

Improving the lifestyle behavior of type 2 diabetes mellitus...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Andreia Pinto João Viana Diogo Martinho Vítor Crista José Miguel Diniz Joana Reis João Matos David Greer Goreti Marreiros Alberto Freitas Department of Community Medicine Information and Health Decision Sciences (MEDCIDS) Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Institute of Engineering Polytechnic of Porto Porto Portugal Health Data Science PhD Program Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Lisboa Portugal CLOO - Behavioral Insights Unit Faculty of Psychology CICPSI - Research Center for Psychological Science University of Lisbon Lisboa Portugal Behavioral Insights Unit CLOO Porto Portugal Behavioral Insights Unit CLOO Lisboa Portugal

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Type 2 diabetes is an increasingly prevalent disease and patients do not always manage the disease properly. Therefore, creating tools that help diabetics self-manage their condition over time is of the utmost importance. Technological tools that include features meeting this population's needs, with an integrated personalized feedback system, in an environment of gamified incentives, may be the way for developing a sustained app's usage. This work aims to present a still in development, novel approach to managing diabetes type 2 with a focus on data analysis from user-inputs, gamification, and personalized coaching, with an accessible user interface. Thus, we will present some functionalities, in particular the theoretical and practical concept that is behind this development, namely the Transtheoretical model of behavior change to create different profiles, to provide customized feedback according to the user's behavioral stage, and also to ascertain if it induces any improvement at the behavioral level.

关键词： Computers Data analysis Computational modeling User interfaces Diabetes Behavioral sciences Mobile applications

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Preface

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and computing 2018年 620卷 v-vi页

作者： Omatu, Sigeru Rodríguez, Sara Villarrubia, Gabriel Faria, Pedro Sitek, Pawel Prieto, Javier Faculty of Engineering Department of Electronics Information and Communication Engineering Osaka Institute of Technology OsakaOsaka Japan Faculty of Science Department of Computer Science and Automation Control University of Salamanca Salamanca Spain GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Instituto Superior de Engenharia do Porto Porto Portugal Faculty of Electrical Engineering and Computer Science Department of Control and Management Systems Department of Information Systems Kielce University of Technology Kielce Poland

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