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Congress on Evolutionary Computation

作者： Fernando Lezama Joao Soares Ricardo Faia Tiago Pinto Zita Vale GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation Development School of Engineering Porto Portugal BISITE Research Center University of Salamanca Salamanca Spain

Power systems are showing a dynamic evolution in the last few years, caused in part by the adoption of smart grid technologies. The integration of new elements that represent a source of uncertainty, such as renewables generation, electric vehicles, variable loads and electricity markets, poses a higher degree of complexity causing that traditional mathematical formulations struggle in finding efficient solutions to problems in the smart grid context. In some situations, where traditional approaches fail, computational intelligence has demonstrated being a very powerful tool for solving optimization problems. In this paper, we analyze the application of Differential Evolution (DE) to address an energy resource management problem under uncertain environments. We perform a systematic parameter tuning to determine the best set of parameters of four state-of-the-art DE strategies. Having knowledge of the sensitivity of DE to the parameter selection, self-adaptive parameter control DE algorithms are also implemented, showing that competitive results can be achieved without the application of parameter tuning methodologies. Finally, a new hybrid-adaptive DE algorithm, HyDE, which uses a new “DE/target - to - perturbed_best/1” strategy and an adaptive control parameter mechanism, is proposed to solve the problem. Results show that DE strategies with fixed parameters, despite very sensitive to the setting, can find better solutions than some adaptive DE versions. Overall, our HyDE algorithm excelled all the other tested algorithms, proving its effectiveness solving a smart grid application under uncertainty.

关键词： Uncertainty Smart grids Mathematical model Complexity theory Optimization Energy resources Weather forecasting

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CECOS: a Centralized Management Platform Supported by Distributed Services to Represent and Manage Resources Aggregation Entities and its End-users in a Smart Grid Context

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IFAC-PapersOnLine 2022年第9期55卷 309-314页

作者： Helder Pereira Bruno Ribeiro Luís Gomes Zita Vale Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/IPP) Porto Portugal

The evolution of the smart grid and its benefits for energy players drives changes in the behaviour of all the parties involved in the power and energy systems. Thus, the existence of platforms to manage participation in aggregation entities and services gains importance, as they help to monitor players’ energy resources, and make their management more intuitive. This paper proposes an upgrade to the platform CECOS, initially designed to manage energy communities and their members. With the upgrade, the platform manages the players and entities belonging to the smart grid. The new version of the platform is not limited to its original services, as it allows the registration of distributed services, such as demand response, or forecast, by service providers. Furthermore, it allows the participation in several aggregation entities simultaneously, and provides a mobility mechanism, to allow players to move among entities to achieve their goals. A case study was developed to evaluate the platform, considering different types of forecasts and demonstrating favourable outcomes in terms of the predictions’ error decrease.

关键词： energy management systems distributed energy services players' mobility mechanism energy communities microgrids, aggregators

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S3PHER: Secure and Searchable System for Patient-driven HEalth data shaRing

arXiv

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arXiv 2024年

作者： Costa, Ivan Amorim, Ivone Maia, Eva Barbosa, Pedro Praça, Isabel GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal IPP - Instituto Politécnico do Porto Porto Portugal

Healthcare data contains some of the most sensitive information about an individual, yet sharing this data with healthcare practitioners can significantly enhance patient care and support research efforts. However, current systems for sharing health data between patients and caregivers do not fully address the critical security requirements of privacy, confidentiality, and consent management. Furthermore, compliance with regulatory laws such as GDPR and HIPAA is often deficient, largely because patients typically are asked to provide general consent for healthcare entities to access their data. Recognizing the limitations of existing systems, we present S3PHER, a novel approach to sharing health data that provides patients with control over who accesses their data, what data is accessed, and when. Our system ensures end-to-end privacy by integrating a Proxy Re-Encryption Scheme with a Searchable Encryption Scheme, utilizing Homomorphic Encryption to enable healthcare practitioners to privately search and access patients’ documents. The practicality and benefits of S3PHER are further validated through end-to-end deployment and use-case analyses, with tests on real datasets demonstrating promising execution times. © 2024, CC BY.

关键词： Information management

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Secure, Searchable, and Consent-Driven Healthcare Data Sharing System

Secure, Searchable, and Consent-Driven Healthcare Data Shari...

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IEEE International Symposium on Network computing and Applications

作者： Ivan Costa Ivone Amorim Eva Maia Pedro Barbosa Isabel Praça GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal IPP - Instituto Politécnico do Porto Porto Portugal

ISBN: (数字)9798331510183

ISBN: (纸本)9798331510190

Healthcare data contains some of the most sensitive information about an individual, yet sharing this data with healthcare practitioners can significantly enhance patient care and support research efforts. However, current systems for sharing health data between patients and caregivers do not fully address the critical security requirements of privacy, confidentiality, and consent management. Furthermore, compliance with regulatory laws such as GDPR and HIPAA is often deficient, largely because patients typically are asked to provide general consent for healthcare entities to access their data. Recognizing the limitations of existing systems, we present a novel approach to sharing health data that provides patients with control over who accesses their data, what data is accessed, and when. Our system ensures end-to-end privacy by integrating a Proxy ReEncryption Scheme with a Searchable Encryption Scheme, utilizing Homomorphic Encryption to enable healthcare practitioners to easily and securely search and access patients’ documents. A time performance analysis is also presented, which allowed us to observe that the number of keywords has a much greater impact on the running time of the different processes than the number of files.

关键词： Data privacy Privacy Medical services Performance analysis Security Homomorphic encryption

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Electric Vehicles Uncertainty Forecasting

Electric Vehicles Uncertainty Forecasting

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Chavez, Juliana Foroozandeh, Zahra Ramos, Sergio Soares, Joao Vale, Zita Gecad - Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development Road Dr. Antonio Bernardino de Almeida 431 Porto4200-072 Portugal Road Dr. Antonio Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9781665475891

The growing integration of electric vehicles has attracted a lot of interest. However, they are highly affected by EV charging uncertainties and are, therefore, difficult to forecast accurately. This paper presents an Artificial Neural Network (ANN) method ANN to forecast electric vehicle uncertainties. ANN was trained using historical data from a residential building, such as arrival time, departure time and initial SOC. Then, it was tested during 24 hours through different scenarios. For each one of the cases, the model's accuracy was assessed by comparing historical data to forecast information. The associated errors were also calculated. The outcomes reveal that the suggested forecasting method is very effective in reducing EV forecasting errors and, as a result, is better at regulating EV uncertainty. © 2022 IEEE.

关键词： Machine learning

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Introduction to Deep Learning Business Applications for Developers 1st ed.

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丛书名： For professionals by professionals

2018年

作者： Armando Vieira Bernardete Ribeiro

ISBN: (数字)9781484234532

ISBN: (纸本)9781484234525

Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. Youll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer. After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework. What You Will Learn Find out about deep learning and why it is so powerful Work with the major algorithms available to train deep learning models See the major breakthroughs in terms of applications of deep learning Run simple examples with a selection of deep learning libraries Discover the areas of impact of deep learning in business Who This Book Is ForData scientists, entrepreneurs, and business developers.

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Benchmarking AutoGen with different large language models

Benchmarking AutoGen with different large language models

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Artificial Intelligence (CAI), IEEE Conference on

作者： Rafael Barbarroxa Bruno Ribeiro Luis Gomes Zita Vale GECAD – Research Group on Intelligent Engineering and Computing for Advancement Innovation and Development LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Although a controversial solution for daily use, the large language models (LLM) appeared near the users as an incredible solution that demonstrates the usefulness of artificial intelligence in recurring tasks. While current LLM solutions are based on direct queries from the users, the use of multi-agent systems supported by LLMs could be a major evaluation in distributed solutions. The few solutions that support multi-agent systems for LLMs, such as ChatDev and AutoGen, were developed based solely on ChatGPT LLM. This paper explores the feasibility of changing the LLM used in a configuration of two agents developed in AutoGen. The results show that the GPT models provided by OpenAI are still the best performers, however, other models can be chosen to reduce costs and still have good results.

关键词： Costs Large language models Benchmark testing Chatbots Task analysis Multi-agent systems

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Smart and advanced Electrical Skills: Identifying Current Gaps and Emerging Trends

Smart and Advanced Electrical Skills: Identifying Current Ga...

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Emmanuel Karapidakis Marini Markaki Spyridon Apostolakis Bruno Canizes Radamanthys Androulakis Fábio Castro Electrical and Computer Engineering Department Hellenic Mediterranean University Heraklion Greece GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic Institute of Porto Porto Portugal GECAD - Research Group on Research Department ECTE Rethymno Greece

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

With the rise of smart buildings, new expertise gaps and security vulnerabilities are emerging, necessitating a comprehensive understanding of their current state and proactive future planning to address these challenges. Such identification is crucial for mapping the pathway towards vocational modernization (keeping up with trends and goal alignment). To assess these gaps, this paper explores the extensive literature that draws on workshops, expert interviews, and research to identify emerging roles and training needs across seven countries: Greece, Spain, Portugal, Italy, North Macedonia, Germany, and the Netherlands. In particular, it examines the validity of its findings in critical areas such as security, privacy, and the integration of smart grids with energy markets. Additionally, the paper highlights future skills gaps emerging from advancements in IoT, automation, and renewable energy technologies. It emphasizes the growing demand for specialized electrical expertise to optimize energy production, integrate monitoring and storage systems, and manage electric vehicle charging and energy communities effectively. In conclusion, the study identifies critical gaps in expertise and training needed to keep up with the rapid evolution of smart building technologies. Emerging trends highlight the demand for interdisciplinary skills, particularly in IoT, automation, and renewable energy. As these fields advance, expertise in cybersecurity, energy management, and smart grid integration will be crucial, emphasizing the need for proactive workforce development and planning. The findings can be used to inform and direct policymakers towards the smooth reexamination of vocational training.

关键词： Renewable energy sources Smart buildings Automation Production Market research Vocational training Smart grids Planning Computer security Energy management

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Distribution Network Digital Twin: a Basic Machine Learning based Voltage Estimator 14

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

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14th IEEE International Conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2023

作者： Iaali, Mohamad El Razi, Reza Bruyere, Antoine Francois, Bruno Soares, Joao Univ. Lille Arts et Metiers Institute of Technology Centrale Lille Junia Ulr 2697 - L2EP LilleF-59000 France Gecad - Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development Lasi - Intelligent Systems Associate LAboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781665455541

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. © 2023 IEEE.

关键词： Distributed power generation

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Generation of intelligent reports for ubiquitous group decision support systems

Generation of intelligent reports for ubiquitous group decis...

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International Global Information Infrastructure Symposium (GIIS)

作者： Luís Conceição João Carneiro Diogo Martinho Goreti Marreiros Paulo Novais GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal Algoritmi Centre University of Minho Guimarães Portugal

Supporting group decision-making is a complex process, especially when decision-makers have no opportunity to gather at the same place and at the same time. Besides that, finding solutions may be difficult in case agents representing decision-makers are not able to understand the process and support them accordingly. In this work we present some topics of information that can be reported to decision-makers to improve their perception about the negotiation process. We classified those topics according to two dimensions and we defined an algorithm to select which information will be built for each report.

关键词： Decision making Context Decision support systems Proposals Organizations Data mining Complexity theory

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