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International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2021

作者： Vieira, Ana Carneiro, João Conceição, Luís Martins, Constantino Souza, Julio Freitas, Alberto Marreiros, Goreti GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering Polytechnic of Porto Porto Portugal CINTESIS – Center for Health Technology and Services Research Porto Portugal

ISBN: (纸本)9783030857097

The aging of the worldwide population has led to a growing prevalence of vascular diseases, negatively impacting national healthcare systems and patients. The application of information technologies in the health sector has the potential to allow the remote monitoring of patients and the personalization of healthcare. This work proposes the architecture for a remote monitoring platform that supports the self-management of vascular diseases in the context of the Inno4Health project, with the goal of stimulating the innovation in the continuous monitoring of the patients’ health. The platform aims to support health professionals and patients with vascular diseases through the continuous monitoring of their health condition and presentation of monitoring reports. Self-management tools will be provided to patients through the presentation of personalized recommendations adapted to their current health condition. By doing so, we believe it will be possible to improve the patients’ self-management of their disease as well as decrease the risk of disease-related complications. © 2021, Springer Nature Switzerland AG.

关键词： Remote control

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Contextual adaptative interfaces for industry 4.0 17th

Contextual adaptative interfaces for industry 4.0

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17th International Symposium on Distributed computing and Artificial Intelligence, DCAI 2020

作者： Canito, Alda Mota, Daniel Marreiros, Goreti Corchado, Juan M. Martins, Constantino GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering Polytechnic of Porto Porto Portugal Department of Computer Science University of Salamanca Salamanca Spain

ISBN: (纸本)9783030538286

Information technologies are intrinsically connected to the manufacturing processes, with more data generated each second. To efficiently operate machines, users must sort out information that is relevant to them in specific moments and contexts. In this paper, we propose an architecture that combines context – e.g. location, type of order, available assets, previous actions – with information established through user stereotypes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Industry 4.0

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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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David – A Novel Approach to an Industrial Recommender System 20th

David – A Novel Approach to an Industrial Recommender Syste...

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20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022

作者： Marques, Rui Martins, Constantino Moreno-García, María N. GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development ISEP – Instituto Superior de Engenharia do Porto Porto Portugal USAL - Universidad de Salamanca Salamanca Spain

ISBN: (纸本)9783031186967

A conceptual approach to a recommender system that is industrial oriented and optimized for business-to-business. With particular needs, industrial datasets seek assertiveness and contextualization to capitalize on recommender systems. Muscle memory must be implanted on business users, enabling them to harvest the benefits of such technologies. These types of users, which differ from standard ones, are fuzzy and vague in their choices, avoiding explicit forms of feedback. This paper addresses state-of-the-art of such industrial systems, its users and explores a potential solution. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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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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Study of multi-tariff influence on the distributed generation remuneration 16th

Study of multi-tariff influence on the distributed generatio...

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16th International Conference on Distributed computing and Artificial Intelligence, DCAI 2019

作者： Silva, Cátia Faria, Pedro Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP - Polytechnic Institute of Porto Rua DR. Antonio Bernardino de Almeida Porto431 4200-072 Portugal

ISBN: (纸本)9783030239459

The energy market, with the introduction of the smart grids concept, opens the door to small distributed energy resources. However, these resources introduce an added level of difficulty to market management, requiring an entity to aggregate and manage them optimally. This paper proposes an approach that integrates these small resources. The methodology is composed of optimal scheduling, aggregation and remuneration based on aggregation. The method chosen for aggregation is k-means. In relation to previous works, the innovation goes through the multi-period and the comparison that this can have in the formation of groups. Thus, three scenarios were created: Whole Week, Work Days and Weekend. Profiles were added for 548 units of DG. The justification for the formation of groups will be a fairer remuneration and according to the contribution of each resource to the management of the network. © 2020, Springer Nature Switzerland AG.

关键词： Distributed power generation

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A Consumer Trustworthiness Rate for Participation in Demand Response Programs

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IFAC-PapersOnLine 2020年第2期53卷 12596-12601页

作者： Cátia Silva Pedro Faria Zita Vale GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal Polytechnic of Porto Porto Portugal

Local energy communities with information from the real-time market may improve the market operation but also increase the complexity of the management problem thanks to the uncertainty associated with the actual response of these resources. For instance, consumers with price knowledge may change their power consumption to lower-cost periods. The authors present a model to deal with uncertainty from the Aggregator perspective: apply reliability rates to each consumer according to their actual response in events of Demand Response (DR). The consumers with higher rates are chosen to participate in the local flexibility markets. To compute the final rate, three different independent rates are used: Historical rate with past information, Cut-rate from the response in the actual period and the Last Day Rate which is the final reliability rate from the previous day. In the present paper, the influence of each independent rate, through the weight used, is studied.

关键词： Demand Response Uncertainty Aggregator Smart Grids Optimization

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Product Recommendation based on Shared Customer's Behaviour

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Procedia Computer Science 2016年 100卷 136-146页

作者： Fátima Rodrigues Bruno Ferreira GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering – Polytechnic of Porto (ISEP/IPP)

Today consumers are exposed to an increasing variety of products and information never seen before. This leads to an increasing diversity of consumers’ demand, turning into a challeng. for a retail store to provide the right products accordingly to customer preferences. Recommender systems are a tool to cope with this challeng., through product recommendation it is possible to fulfill customers’ needs and expectations, helping maintaining loyal customers while attracting new customers. However the huge size of transactional databases typical of retail business reduces the efficiency and quality of recommendations. In this paper a hybrid recommendation system that combines content-based, collaborative filtering and data mining techniques is proposed to surpass these difficulties. The recommendation algorithm starts to obtain similar groups of customers using customer lifetime value. Next an association rule mining approach based on similar shopping baskets of customers of the same cluster, in a specific time period is implemented in order to provide more assertive and personalized customer product recommendations. The algorithm was tested with data from a chain of perfumeries. The experimental results show that the proposed algorithm when compared with a base recommendation (made solely on past purchases of customers) can increase the value of the sales without losing recommendation accuracy.

关键词： clustering market basket association rules product recommendation

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Key Performance Indicators to Support the Participation in Demand Response Programs:A Testing Framework for End Users

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IFAC-PapersOnLine 2020年第2期53卷 12602-12607页

作者： Débora de São José Pedro Faria Cátia Silva Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal Polytechnic of Porto Porto Portugal

With the new challeng.s and complexity in power electricity management, demand response programs can be a way to reduce stress and streng.hen power grids. However, as demand response implies end users to intentionally change their consuming patterns to adapt to grids needs, some decision-making support tools are necessary. The present paper proposes an energy management and controlling tool to assist electricity end users to make their decision to change consumption pattern in a DR scenario while using key performance indicators. The tool was tested using a group of 20 end users and showed a consistent result throughout all the elements in the sample.

关键词： System Management Monitoring Key Performance Indicators KPI Demand Response

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Knowledge Retrieval Mechanism for Smart Buildings Based on IoT Devices Data 1

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9th International Conference on Energy and Environment research, ICEER 2022

作者： Teixeira, Nuno 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 Porto4200-072 Portugal

ISBN: (数字)9783031435591

ISBN: (纸本)9783031435584

The use of smart building solutions can bring advantages to both a building and its users. The main motivation of this work is to propose a solution based on internet of things devices to retrieve knowledge from historic data and, using the knowledge gained, contribute to the comfort of the user and the building’s sustainability. The use of internet of things devices enables the easy deployment of the proposed system in today’s buildings, where this kind of devices are commonly found. The knowledge regarding the role of each device and how they interact or impact each other will be studied by the proposed solutions that will deploy a continuous mechanism. The designed software architecture was developed in python, to allow easy implementation of different features. To test the proposed solution, it was used real data from a research building where several types of sensors were correlated to assess relations among data. The results show significant correlations between celling light consumption and the room’s light intensity sensor, and between the operation of air conditioning units and the room’s temperature, for example, data from 2 months, regarding air conditioner unit in room N102, demonstrates a − 0.85 correlation with the room temperature, when operating in cooling mode. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Internet of things

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