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检索条件"机构=GECAD - Knowledge Research Group on Intelligent Engineering and Computing for Advanced Innovation"

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Optimizing Energy Costs in Energy Communities: A Monthly Tariff Clustering Approach 22

Optimizing Energy Costs in Energy Communities: A Monthly Tar...

引用

22nd International Conference on intelligent Systems Applications to Power Systems, ISAP 2024

作者： Lezama, Fernando Bairrao, Diego Doria, Filipe Vale, Zita LASI - Polytechnic of Porto GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal

ISBN: (纸本)9798331531751

In collaborative energy communities, optimizing energy costs is a critical aspect of sustainable management. This article explores the potential benefits of applying clustering algorithms to vary retail tariffs monthly, aiming to reduce energy bills for the community as a whole. The article compares a traditional approach of applying the same tariff to all community members throughout the year with a novel approach of dynamically changing tariffs based on monthly clustering results. A case study is conducted, wherein energy bill costs per month are analyzed under different tariff scenarios utilizing k-means clustering. Results indicate that the proposed approach yields promising reductions in energy costs, up to 8.76% (1170.18 EUR) improvement compared to the traditional method. The study contributes valuable insights into the practical application of clustering in energy community management and highlights the potential for significant cost savings through dynamic tariff adjustments. © 2024 IEEE.

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Secure, Searchable, and Consent-Driven Healthcare Data Sharing System 22

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

引用

22nd International Symposium on Network computing and Applications, NCA 2024

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

ISBN: (纸本)9798331510183

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 Re-Encryption 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. ©2024 IEEE.

关键词： Differential privacy

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Optimizing House Energy Management with MILP Considering Shifting of Electric Appliances and Battery Storage System

Optimizing House Energy Management with MILP Considering Shi...

引用

2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Faia, Ricardo Faria, Pedro Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350396782

The integration of renewable energy sources into the power grid has led to the need for efficient energy management systems that consider the presence of energy storage systems and controllable loads. In this paper, the authors propose a mixed-integer linear programming optimization model for energy management that takes into account the operation of shift electric appliances and battery storage systems. The proposed model aims to minimize the overall electricity cost while ensuring that the energy demand of the system is met, the battery state of charge is maintained within safe operating limits, and the shift electric appliance met with the user requirements. The authors consider the operational constraints of shifting electric appliances, which can be scheduled to operate during periods of low electricity prices. Additionally, they account for the energy storage system, which can be charged during periods of excess renewable energy production and discharged during periods of high demand. Four different scenarios are tested to prove the value of the model. © 2023 IEEE.

关键词： Battery storage systems Minimize Costs Shift appliances

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Smart and advanced Electrical Skills: Identifying Current Gaps and Emerging Trends 5

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

引用

5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

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

ISBN: (纸本)9798350352863

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. © 2024 IEEE.

关键词： Power grids

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Deep Learning in Automated Tests for the Automotive Industry 15th

Deep Learning in Automated Tests for the Automotive Indus...

引用

15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Branco, Beatriz C. Barbosa, Ramiro S. Porto Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development ISEP/IPP Porto Portugal

ISBN: (纸本)9783031810824

The automotive industry is one of the biggest industries nowadays, representing almost 80% of the projects in Capgemini engineering Portugal, where this work was developed. In this paper, a new approach for automated tests in this sector is presented, based on the application of Deep Learning (DL) algorithms. Experimental results show that the new solution reduces the frameworks needed for testing, as well as the human interaction, improving the efficiency in HMI testing for the company. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Automated tests Automotive industry Deep learning

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Demand Response-based Energy Management Model for Energy Communities considering Data Privacy of the Members

Demand Response-based Energy Management Model for Energy Com...

引用

2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Barreto, Ruben Gomes, Luis Faria, Pedro Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350396782

This paper proposes a demand response-based energy management model for energy communities, considering the respective members’ data privacy. Through forecasting and clustering algorithms, this model can identify demand response opportunities for the next day, rank and select the participants for the event and monitor and evaluate the respective event. The paper’s novelty lies in increasing the energy community members' active participation by allowing them to submit one or more proposals to participate in a demand response event. In this way, a member can, on the one hand, have more freedom to choose how to participate and, on the other hand, have more opportunities to contribute to the DR event with their energy flexibility. This model was tested with real data from an energy community with 50 buildings, which can provide flexibility through reductions and shifting. The results showed that the model’s efficiency could increase by considering the multiple proposals per member. Moreover, it was verified that with just one event, it is possible to reduce the CO2 emissions and the energy cost by 50% and 73%, respectively, as well increasing the energy community sustainability by 10%. © 2023 IEEE.

关键词： Active Participation Clustering Demand Response Energy Community Energy Flexibility

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Detection of Human Falls via Computer Vision for Elderly Care – An I3D/RNN Approach 1

引用

20th International Symposium on Distributed computing and Artificial Intelligence, DCAI 2023

作者： Leal, João Moayyed, Hamed Vale, Zita Porto Portugal 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)9783031383335

ISBN: (纸本)9783031383328

As the population continues to age, the number of elderly individuals is increasing at an unprecedented rate as the active age group in developed countries continues to shrink. This demographic shift has resulted in a shortage of resources and limitations in the provision of adequate elderly care. Among the many health risks faced by the elderly, falls are a common and serious problem, which can lead to injuries, hospitalizations, and even death. Despite the prevalence of falls, traditional manual detection methods are often unreliable and inadequate. In response to this, this paper proposes an automatic fall detection system based on video cameras that uses a unique combination of an inflated 3D convolutional neural network (I3D) and a recurrent neural network (RNN). This model was evaluated using multiple fall detection datasets, including a newly developed dataset of simulated falls. The results of this study demonstrate that this hybrid model is highly effective at detecting falls and is on par with other state-of-the-art systems, boasting 94% F1 score and 96% recall values. This technology has the potential to significantly improve the quality of life and safety of the elderly population by providing timely and accurate detection of falls. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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Production and Maintenance Scheduling for Total Cost and Machine Longevity Optimization

Production and Maintenance Scheduling for Total Cost and Mac...

引用

2023 International Conference on Future Energy Solutions, FES 2023

作者： Mota, Bruno Faria, Pedro Canizes, Bruno Ramos, Carlos GECAD - Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350332308

Production and maintenance scheduling for total cost-effective and machine longevity manufacturing is a key aspect in dealing with the ever-increasing energy prices, competitiveness, added maintenance costs, and environmental pressures that the manufacturing sector faces nowadays. This paper addresses these issues by proposing a novel intelligent production scheduling system for joint optimization of production and maintenance for overall cost minimization and machine longevity improvement. To achieve this, it is proposed a Genetic Algorithm (GA) for production and maintenance scheduling of flexible job shop manufacturing environments. The proposed GA takes into account volatile market energy prices, Renewable Energy Resources (RERs), surplus energy selling, maintenance activities, and constraints imposed on the production plan. A case study from the literature is used as a baseline scenario to validate the proposed scheduler. It uses real-production data and considers three unique machines with 275 tasks to be scheduled among them. Using the baseline scenario, it was possible to demonstrate the robustness of the proposed scheduler in reducing total costs, by taking advantage of the volatility of energy prices, as well as utilizing RERs to cover energy expenses or for selling excess energy. Furthermore, it highlights the ability to reduce the overload of single machines. Accordingly, it was possible to achieve cost reductions of up to 11.0% and improvements of 24.4% in machine longevity when compared to the baseline scenario. © 2023 IEEE.

关键词： Genetic algorithms

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Joint Optimization of Production and Maintenance for Effective Manufacturing Using a Genetic Algorithm

Joint Optimization of Production and Maintenance for Effecti...

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2023 IEEE International Conference on Environment and Electrical engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Mota, Bruno Faria, Pedro Ramos, Carlos GECAD - Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350347432

While energy prices are seen as the major drive for competitiveness in the manufacturing field, intelligent maintenance scheduling is also one of the most widespread and significant issues plaguing the manufacturing industry. Maintenance activities can cost between 15% and 70% of the price of goods sold, thus it is essential to optimize both energy and maintenance costs. The purpose of this paper is to propose a production line optimization system that focuses on reducing the total costs (i.e., energy and maintenance costs) while also taking into account dynamic pricing, Renewable Energy Resource (RER) usage, and constraints applied in the production plan. In the proposed system, tasks and maintenance activities are scheduled by using a Genetic Algorithm. To validate the proposed system, a baseline scenario that uses real production data is considered. The obtained results show that the system is able to comply with imposed maintenance hours while also minimizing costs by shifting tasks to higher RER generation and lower energy price times, while the opposite is done to maintenance activities. © 2023 IEEE.

关键词： Genetic algorithms

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Retrieval-Augmented Generation Powered by a Multi-agent System to Assisted the Operation of Industries 22nd

Retrieval-Augmented Generation Powered by a Multi-agent Syst...

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22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Oliveira, Francisco Gomes, Luis Vale, Zita GECAD – Research Group on Intelligent Engineering and Computing for Advancement Innovation and Development Polytechnic of Porto Porto Portugal LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9783031704147

The introduction of large language models in recent years transformed the use of artificial intelligence in our daily life, making it available to common users and allowing them to interact with natural language processing machines. However, the common and general knowledge of large language models is not reliable when it comes to the quality of the response and its veracity. The proposed work integrates the use of retrieval-augmented generation with large language models with a multi-agent system framework to enable the creation of trees of knowledge where multiple agents have domain specific knowledge. The proposed agent-based solution was tested in a realistic case study where multiple knowledge domains are distributed amongst six different agents. The case study evaluates the capacity of agents to delegate questions according to its topics and the capacity of correctly replying to questions. The results are promising, demonstrating the ability of large language models to assess and process information that wasn’t used in its training process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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