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

作者： Silva, Rafael 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 R. Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031750120

As the European Union sets a target to increase renewable energy consumption by 2030, progressing towards a decentralized energy production becomes more a (greater?) priority. This can be achieved with initiatives such as energy communities, which when combined with active consumer participation and the integration of IoT devices and Cloud computing technologies in households can enhance energy management, however, it also introduces privacy concerns. Single-board computers are cost-effective computing solutions, but in turn face challenges with scalability and reliability. To address these issues, we propose Caravels, a distributed solution that leverages container applications for intelligent energy communities. Caravels promotes direct peer-to-peer communication among users, enabling real-time applications and collaborative deployments while ensuring user data privacy and system scalability. We conducted a case study that implements Caravels across Raspberry Pi nodes, demonstrating its feasibility and adaptability. The results indicate Caravels’ promising potential to empower households towards sustainable energy management and overcoming technological constraints and privacy concerns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Scalability

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Energy Community Integration of a Smart Home Based on an Open Source Multiagent System 21st

Energy Community Integration of a Smart Home Based on an Ope...

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21st International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2023

作者： Ribeiro, Bruno Faia, Ricardo 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 R. Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031376153

Smart grid is a revolutionary concept that came to renew the way the power systems are thought. However, for these systems to be possible it is necessary and recommended that the consumers have the right technology integrated in their houses, in other words, have smart homes. This paper describes the demonstration of a smart home solution based on multiagent systems for the optimization of the house energy usage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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Chatbot Architecture for a Footwear E-Commerce Scenario 20th

Chatbot Architecture for a Footwear E-Commerce Scenario

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

作者： Rodrigues, Vasco Santos, Joaquim Carrasco, Pedro Jesus, Isabel Barbosa, Ramiro Matos, Paulo Marreiros, Goreti GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto4200-072 Portugal LASI - Intelligent Systems Associate Laboratory Guimarães4800-058 Portugal UC - University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031383328

The amount of information accessible to businesses grows as more individuals get access to the internet. This information can be used by businesses to make corporate decisions that can highly affect them. E-commerce is the result of businesses starting to export their operations to the internet as a result of taking advantage of this reality. To help a footwear e-commerce platform keep up with the current demands of the market in terms of accessibility and customer service, a chatbot was created to help on this request. To build this chatbot, datasets were created leading to Intent Classification and Named-Entity Recognition pre-trained models, utilizing Bert-Large, getting fine-tuned with those datasets. These models achieved a F1-score of 0.90 and 0.88 respectively in their tasks. The Sentiment Analysis functionality of TextBlob was also utilized to help the chatbot comprehend user’s text polarity to reply appropriately. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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A context-awareness approach to tourism and heritage routes generation 9th

A context-awareness approach to tourism and heritage routes ...

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9th International Symposium on Ambient Intelligence, ISAmI 2018

作者： Ramos, Carlos Marreiros, Goreti Martins, Constantino Faria, Luiz Conceição, Luís Santos, Joss Ferreira, Luís Mesquita, Rodrigo Lima, Lucas Schwantes GECAD - Research Group on Engineering and Intelligent Computing for Innovation and Development Polytechnic of Porto Porto Portugal IFSC - Federal Institute of Santa Catarina FlorianópolisSanta Catarina Brazil

ISBN: (纸本)9783030017453

The aims of the TheRoute (Tourism and Heritage Routes including Ambient Intelligence with Visitants’ Profile Adaptation and Context Awareness) project is to conduct studies, research and experimentation around the challenge of automatic generation of routes for visitors. The suggested routes fit the profile of visitors and groups of visitors, including aspects like emotion, mood and personality, and be aware of the context (e.g. weather, security). TheRoute is developed according the Ambient Intelligence perspective. At this point of the project execution we have already developed the full system architecture as a System of Systems approach according to an Ambient Intelligence perspective, to allow the best possible performance in the system utilization for the final user. intelligent route generation uses user preferences for the categories of points of interest, as well as their personality traits. © Springer Nature Switzerland AG 2019.

关键词： Ambient intelligence

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House management system with real and virtual resources: Energy efficiency in residential microgrid

House management system with real and virtual resources: Ene...

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International Global Information Infrastructure Symposium (GIIS)

作者： Gabriel Santos Filipe Femandes Tiago Pinto Marco Silva Omid Abrishambaf Hugo Morais Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP - Polytechnic of Porto Porto Portugal GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP - Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781509036097

The reduction of the greenhouse gas emissions is a priority all around the globe. The investment on renewable energy sources is contributing for new opportunities in the context of the smart grids and microgrids. Recent advances are transforming the consumer into a prosumer, being able to adapt the consumption depending on its own generated power, and selling the surplus or buying the missing power. In this context, home management systems are emerging as an effective means to support the management of energy resources in the context of communication between functions/devices of a smart home. This paper presents a new agent-based home energy management approach, using ontologies to enable semantic communications between heterogeneous multi-agent entities. The main goal is to support an efficient energy management of end consumers in the context of microgrids, obtaining a scheduling for both real and virtual resources. A case study is presented, which simulates a 25-bus microgrid that includes a laboratorial controlled house (with real and simulated resources), which is managed by the proposed energy management system.

关键词： Ontologies Context Smart grids Microgrids Energy resources Energy efficiency

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Production Line Energy Cost Optimization with Renewable Energy Resource Usage in a Flexible Job Shop Configuration

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IFAC-PapersOnLine 2023年第2期56卷 7972-7977页

作者： Bruno Mota Pedro Faria Carlos Ramos GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto 4249-015 Portugal

Ever-increasing electricity prices due to inflation has become one of the main problems for manufacturing industries to deal with, mainly in a competitive market. The usage of Renewable Energy Resources (RER)s can be a great way to mitigate the energy costs of the electricity market, by not only covering the energy consumption but also turning a profit with the surplus. This paper aims to address these issues by proposing an intelligent production line scheduling system, in a flexible job shop configuration, that focuses on reducing energy costs. It considers dynamic pricing, RERs usage and surplus selling, and complex constraints applied in the production plan. To achieve these results, a Genetic Algorithm (GA) is employed. A case study from the literature, that uses real production data, is presented which does not take into account energy selling. Results show that with the proposed GA energy costs can be further reduced by 24.6% when considering RERs surplus selling.

关键词： Energy cost optimization Evolutionary algorithm Job shop scheduling

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Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017

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2017年

作者： Fernando de la Prieta Zita A Vale Luís M Antunes Tiago Manuel Pinto Andrew T Campbell Vicente J Julín Antonio J R Neves Mara N Moreno

ISBN: (纸本)9783319615776

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2017 in the special sessions: Agent-Based Social Simulation, Modelling and Big-Data Analytics (ABM); Advances on Demand Response and Renewable Energy Sources in Agent Based Smart Grids (ADRESS); Agents and Mobile Devices (AM); Computer vision in Multi-Agent Robotics (RV); Persuasive Technologies (PT); Web and Social Media Mining (WASMM). The volume also includes the papers accepted for publication in the Doctoral Consortium (DCAI, DCAI-DECON, ISAMI, MIS4TEL, PAAMS, PACBB 2017 conferences).

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Demand Response Event Participants Selection Using Classification Methods

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IFAC-PapersOnLine 2023年第2期56卷 10917-10922页

作者： Cátia Silva Pedro Faria Zita Vale Intelligent Systems Associated Laboratory (LASI) Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Polytechnic of Porto (P.PORTO) Porto Portugal

The active consumers' role in the power and energy market is changing. To deal with the volatile behavior from the Distributed Generation (DG) resources, load flexibility must be provided from the demand side. So, Demand Response (DR) events are triggered. To reduce the response uncertainty, the authors proposed a Trustworthy Rate (TR) to classify the performance of the active consumers in a community. For comparison with previous works' approaches, the innovation from the present study introduces classification methods for deciding the best approach for selecting the DR program participants (load shifting) for specific contexts and reduction goals. From the results, the Decision Tree model created had a lower accuracy performance than the Artificial Neural Network one. To evaluate the effectiveness of the proposed model, the mean absolute error (MAE) was used by the authors.

关键词： Artificial Neural Network Decision Tree Demand Response Smart Grids

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Learning-Based Models for intelligent Control Over Air Conditioning Units in a Smart Building 19th

Learning-Based Models for Intelligent Control Over Air Condi...

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

作者： Ribeiro, Bruno Silva, Rafael Mota, Bruno Gomes, Luis Vale, Zita 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 R. Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031750120

In power and energy systems, artificial intelligence is commonly used for energy forecasting and decision-making. However, the real deployment of artificial intelligence models for operation in smart buildings is not common, and there is a significant gap between the literature-proposed models and deployed models. In this research, the authors apply multiple machine learning models to learn user-profiles and comfort levels regarding air conditioning usage: decision tree, random forest, support vector machine, and artificial neural network. To validate the models, a real use case with a one-year dataset was used. The dataset preparation is also part of this research. The models were analyzed using simulation and the best solution, in this case support vector machine, was deployed in a real uncontrollable environment using a containerized-based solution. The promising results demonstrate that the proposed methodology can be deployed in real buildings and learn the air conditioning usage profiles within one year of use. The authors also proceed with the deployment of a learning model in a smart building. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Demand Response Programs for Peak Reduction Using Consumers Aggregation

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IFAC-PapersOnLine 2023年第2期56卷 10941-10946页

作者： Modar Zheiry Pedro Faria Zita Vale Intelligent Systems Associated Laboratory (LASI) Research Group for Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Polytechnic of Porto (P.PORTO) Porto Portugal

Many studies have examined the potential of the flexible loads within the power systems, taking advantage of Demand Response (DR) programs and Data Mining (DM) to optimize the system response and achieve sustainability through load management. The authors propose aggregation models to study their impact on the power system, using unsupervised DM technique which is Clustering (k-means). The scientific contribution of this paper is related to providing peak-reduction using clustering for load aggregation. A case study has been provided, consisting of three scenarios based on the load aggregation model. The results indicate the system responses to the different scenarios and illustrate the features of each load aggregation model. Furthermore, the results demonstrate how using DR programs combined with DM can effectively provide benefits to the system stability.

关键词： Energy Management Demand Response Peak Reduction Load Aggregation Clustering

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