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14th International conference of the IMACS TC1 Committee, ELECTRIMACS 2022

作者： Van Waeyenberge, Alexander Canizes, Bruno Soares, João Ramos, Sérgio Ravyts, Simon Chavez, Juliana Vale, Zita GECAD Research Center Polytechnic of Porto 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 ESAT ELECTA KU Leuven Ghent Belgium

ISBN: (纸本)9783031248368

Changes will be required to handle the increased power flow in the network as the distribution infrastructure ages and the number of EVs and renewable grows. Designing and operating an intelligent network that reacts to changing power flows to ensure the optimal operation is the most economical option than fortifying the network with heavier cables. This work aims to build a model of a 13 bus medium voltage distribution network with high penetration of distributed energy resources and use it to analyze network conditions. The MATLAB Simulink software is used to model and evaluate the network. The outcomes suggest the model is promising and valid even when renewable generation is at low levels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart power grids

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Clustering Support for an Aggregator in a Smart Grid Context 18th

Clustering Support for an Aggregator in a Smart Grid Context

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18th International Conference on Hybrid intelligent Systems, HIS 2018

作者： Silva, Cátia Faria, Pedro Vale, Zita GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Rua Dr. António Bernardino de Almeida Porto4200-072 Portugal

ISBN: (纸本)9783030143466

The future of the industry foresees the automation and allocation of more intelligence to processes. A revolution in relation to the present. With this, new challenges and consequently more complexity is added to the management of the sectors. In the electric sector is introduced the theme of the Smart grids and so all the concepts aggregated with it. The possibility of the existence of demand response programs and the expansion of the distributed generation units for small players are key concepts and with enormous influence in the management of the markets belonging to this sector. Thus, a method is proposed that would help manage these resources through their aggregation, opening a new port for business models based on this idea. The benefit will be to take advantage of a more effective and efficient way the energy potential present in each group that is formed. Thus, in this paper will be explored the potential of clustering methods for the aggregation of resources. © 2020, Springer Nature Switzerland AG.

关键词： Smart power grids

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Explainable Artificial Intelligence for Definition of Inputs in Neural Networks and K-nearest Neighbors Forecasting of Electricity Consumption

Explainable Artificial Intelligence for Definition of Inputs...

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

作者： Ramos, Daniel Faria, Pedro Vale, Zita Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350396782

Nowadays, data science and machine learning areas play a crucial role by performing predictions of energy with reliable historic of consumptions and other sensors data. This is crucial for decision makers to formulate an optimized plan to buy and sell energy ahead of time. According to many data scientists the decision criteria to select sensors to be added to the historic of consumptions consists on the sensors evidencing a higher correlation with the consumption variable. However, experts in the explainable artificial intelligence field study the positive and negative impact of the sensors to the prediction accuracy. . This paper analyzes the correlation of the sensors of a building to the consumption variable. Furthermore, explanations regarding the contribution of the consumption and sensors features to the forecasting performance are supported by an explainable artifial intelligence method named Local Interpretable Model Agnostic Explanation. The forecasted consumptions use two methods: Artificial Neural Network and K-nearest Neighbors. © 2023 IEEE.

关键词： Decision Making Energy sector Explainable Artificial Intelligence

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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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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 challenge for a retail store to provide the right products accordingly to customer preferences. Recommender systems are a tool to cope with this challenge, 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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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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Applying machine learning classifiers in argumentation context 17th

Applying machine learning classifiers in argumentation conte...

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

作者： Conceição, Luís Carneiro, João Marreiros, Goreti Novais, Paulo ALGORITMI Centre University of Minho Guimarães4800-058 Portugal GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering Polytechnic of Porto Porto4200-072 Portugal

ISBN: (纸本)9783030530358

group decision making is an area that has been studied over the years. group Decision Support Systems emerged with the aim of supporting decision makers in group decision-making processes. In order to properly support decision-makers these days, it is essential that GDSS provide mechanisms to properly support decision-makers. The application of Machine Learning techniques in the context of argumentation has grown over the past few years. Arguing includes negotiating arguments for and against a certain point of view. From political debates to social media posts, ideas are discussed in the form of an exchange of arguments. During the last years, the automatic detection of this arguments has been studied and it’s called Argument Mining. Recent advances in this field of research have shown that it is possible to extract arguments from unstructured texts and classifying the relations between them. In this work, we used machine learning classifiers to automatically classify the direction (relation) between two arguments. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Machine learning

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An Ontology to Promote Interoperability between Cyber-physical Security Systems in Critical Infrastructures 6

An Ontology to Promote Interoperability between Cyber-physic...

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Canito, Alda Aleid, Katia Praca, Isabel Corchado, Juan Marreiros, Goreti University of Salamanca Department of Computer Science Salamanca Spain GECAD-Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781728186351

Critical infrastructures, like airports and hospitals, provide essential services and are considered huge investments in any country. Ensuring the security of these critical infrastructures is a necessity in every possible aspect. These infrastructures, however, currently make use of different technological solutions with distinct security levels, features and knowledge representation formats which often do not communicate directly with each other, leaving the task of processing the global view of the institution's security to the end-users. In order to have a complete view of the current cybersecurity status of a given system, interoperability between these tools must be addressed. Different ontologies have been proposed to deal with subdomains of cybersecurity. Similarly, these fail to provide a full view of cyber and physical security that would be necessary to achieve a global security approach. In this paper, we explore the current status and activities of cybersecurity as a basis for the development process, propose an integrated ontology for combined cyber and physical security. After that, we suggest several evaluation methods to be applied to the resulting ontology in order to check its usefulness in achieving the interoperability goal. The focus will be on the different systems needed for an integrated security of airports. © 2020 IEEE.

关键词： Interoperability

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Algorithms for context-awareness route generation 11th

Algorithms for context-awareness route generation

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11th International Symposium on Ambient Intelligence, ISAmI 2020

作者： Pinto, Ricardo Conceição, Luís Marreiros, Goreti GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering Polytechnic of Porto Porto4200-072 Portugal ALGORITMI Centre University of Minho Guimarães4800-058 Portugal

ISBN: (纸本)9783030583552

This work has as its main goal the investigation and experimentation on automatic generation of routes for tourists and visitors of points of interest, considering the knowledge of the routes, the profile of the visitor and the context awareness of the tour. The context of a trip can be taken through various sources of information, such as the location of the tourist, the time of the visit, the weather conditions, as well as relevant aspects and characteristics of the user's activity and profile. The developments of this work are part of TheRoute project, and its main goal is the development of a route generation module that considers the context of the tourist, the trip and the environmental constraints. In order to solve the proposed problem, two algorithmic solutions were developed. One is an adaption of the A* algorithm with cuts, while the other is based on Ant Colony Optimization, a Swarm Intelligence algorithm. The results from the experiments allowed to conclude that the A* with cuts, oriented to the heuristic for the path with the highest score, is the one that obtains the best conjugation of results for the defined satisfaction metrics. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Ant colony optimization

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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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