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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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Demand Response-based Model for an Energy Community Considering Members and Electric Vehicles Participation

Demand Response-based Model for an Energy Community Consider...

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

作者： Barreto, Ruben Gomes, Luis Vale, Zita Polytechnic of Porto GECAD - Res. Grp. on Intelligent Eng. and Computing for Advanced Innovation and DevelopmentLASI Porto Portugal

ISBN: (纸本)9798350347432

This paper proposes a demand response-based model to manage the energy resources of an energy community, considering the data privacy of the respective community members and electric vehicles. First, through a day-ahead forecasting algorithm, the implemented model identifies possible demand response events to be launched in the energy community. Then, the community members and electric vehicles are ranked to identify which will be selected to participate in the respective demand response event. This selection is based on several metrics created to assess different types of community members' data through clustering algorithms. Furthermore, this model also considers real-time monitoring and evaluation of the demand response event. Finally, the implemented model was tested with energy community data containing 750 possible candidates to participate in the demand response event with their flexibility, where these candidates are households and several different electric vehicles. The results show that the implemented model enables a saving of 13.66 EUR while reducing the CO2 emissions by 32.34 kg in a single demand response event. © 2023 IEEE.

关键词： Data privacy

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

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

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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...

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

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

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

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

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

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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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Towards Fair Energy Communities: Integrating Storage, Sharing and Pricing Strategies

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IFAC-PapersOnLine 2024年第13期58卷 320-325页

作者： Calvin Goncalves Fernando Lezama Zita Vale Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) LASI - Polytechnic of Porto Porto Portugal

This study employs a Mixed-Integer Linear Programming model to optimize energy transactions in an energy community. This research promotes energy sharing through grid and peer-to-peer transactions by incorporating energy storage systems. The article also integrates fairness metrics to evaluate economic equity. The case study examines households with varying equipment acquisition power, highlighting the impact of pricing structures on economic fairness. Analyzing optimal results, the study focused on a community of ten households, six of which are prosumers with PV and energy storage of different capacities. Results show that combining peer-to-peer transactions and high-capacity PV and storage can lead up to a 183% improvement in energy bills. On the other hand, members participating in energy sharing without PV and storage within the community only achieve ~13% improvement in their energy bill, clearly showing a disparity in cost savings linked to equipment capacity acquisition.

关键词： energy community energy sharing energy storage systems fairness metrics pricing strategies

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Retrieval-Augmented Generation Based Assistant: A Smart Home Case Study 23rd

Retrieval-Augmented Generation Based Assistant: A Smart Home...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Ribeiro, Bruno Gomes, Luis Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031734991

Smart homes are starting to become more present in our daily lives. Smart homes are useful for enabling energy management services and reducing monetary costs of the consumers. Despite the increasing research in these aspects of the smart home, few actually have in mind the interactivity of the system they propose. Therefore, this paper, an extension of a previous work, proposes a new human-machine interface based on multi-agent systems and natural language models with retrieval-augmented generation techniques for smart homes to facilitate the consumers adaptation to this type of systems. The assistant enables the user to control the house and provides him with the information he needs, even when a problem within the system occurs. Through the experiment the assistant showed to be flexible, able to provide answers to questions outside of the smart home system domain and it increases the scalability of the system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart homes

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Production Scheduling for Energy Cost Optimization with Constraints: Comparison of Genetic Algorithm and PSO Techniques 19th

Production Scheduling for Energy Cost Optimization with Cons...

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19th International Conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Mota, Bruno Faria, Pedro Ramos, Carlos GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031750120

intelligent production scheduling has become predominant in smart factories that aim to greatly reduce energy costs. For this purpose, scheduling systems need to not only consider multiple machines, PV energy, and electricity price volatility but also take into account constraint applicability and flexible job shop layouts to meet smart factories’ demands. To solve these complex scheduling problems, Artificial Intelligence (AI) is the ideal approach. Accordingly, the premise of the present paper is to implement and compare quantitatively three AI algorithms for intelligent production scheduling: a proposed Genetic Algorithm (GA), and a conventional GA and Particle Swarm Optimization (PSO) from the DEAP Python library. Different manufacturing concepts are considered to solve the aforementioned issues, including tasks, machines, products, and manufacturing orders. In addition, three constraints are explored: schedule completion deadline, manufacturing order deadline, and time leap. Validation of the AI algorithms for production scheduling is done through a real case study with real-production data. Results show that the proposed GA has, on average, lower energy costs of about 20.2% and 22.2% when compared to the conventional GA and PSO, respectively. Furthermore, when constraints were imposed, only the proposed GA was effective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Particle swarm optimization (PSO)

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