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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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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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Web-based platform for the management of citizen energy communities and their members

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Energy Informatics 2021年第2期4卷 1-17页

作者： Pereira, Helder Gomes, Luis Faria, Pedro Vale, Zita Coelho, Carlos GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal Institute of Engineering - Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

The appearance of citizen energy communities demands the conception, development, and testing of new management models for the community and its end-users. Citizen energy communities promote the active participation of end-users, including them in the management of the community. End-users are incentivized to participate in demand response programs and share their energy among peers, enabling a decrease in their energy costs. In this paper, it is proposed a platform for the management of citizen energy communities. The paper focuses and presents four services related to energy tariffs, end-users’ aggregation, price elasticity, and load response. The services are based on historical data and enable deep analysis of end-users’ energy profiles. As the platform allows the upload of different scenarios, it is possible to test and validate management models in multiple energy communities and scenarios and study their impact in different conditions. The paper presents a case study, where all the services are applied to a community with 996 end-users. © 2021, The Author(s).

关键词： Elasticity

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Feature Selection on Epistatic Problems Using Genetic Algorithms with Nested Classifiers 26th

Feature Selection on Epistatic Problems Using Genetic Algor...

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26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Carvalho, Pedro Ribeiro, Bruno Rodrigues, Nuno M. Batista, João E. Vanneschi, Leonardo Silva, Sara University of Coimbra Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering Coimbra Portugal 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 LASIGE Department of Informatics Faculty of Sciences University of Lisbon Lisbon Portugal Universidade Nova de Lisboa Campus de Campolide Lisboa1070-312 Portugal

ISBN: (纸本)9783031302282

Feature selection is becoming an essential part of machine learning pipelines, including the ones generated by recent AutoML tools. In case of datasets with epistatic interactions between the features, like many datasets from the bioinformatics domain, feature selection may even become crucial. A recent method called SLUG has outperformed the state-of-the-art algorithms for feature selection on a large set of epistatic noisy datasets. SLUG uses genetic programming (GP) as a classifier (learner), nested inside a genetic algorithm (GA) that performs feature selection (wrapper). In this work, we pair GA with different learners, in an attempt to match the results of SLUG with less computational effort. We also propose a new feedback mechanism between the learner and the wrapper to improve the convergence towards the key features. Although we do not match the results of SLUG, we demonstrate the positive effect of the feedback mechanism, motivating additional research in this area to further improve SLUG and other existing feature selection methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Climatization and luminosity optimization of buildings using genetic algorithm, random forest, and regression models

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Energy Informatics 2021年第2期4卷 1-18页

作者： Mota, Bruno Albergaria, Miguel Pereira, Helder Silva, José Gomes, Luis Vale, Zita Ramos, Carlos Institute of Engineering - Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

With the rise in popularity of artificial intelligence, coupled with the growing concern over the environment, there has been a surge in the use of intelligent energy management systems. Additionally, as more buildings transition into the smart grid and, consequently, more energy and environmental data is gathered, there has been a significant increase in the number of data-driven approaches for building management systems. This paper proposes a methodology that aims to optimize the climatization and luminosity inside a building, using a genetic algorithm, a random forest, and two polynomial models. The proposed methodology enables the real-time management of the building taking into account the user needs and preferences. Air conditioner units and light systems are optimized to minimize energy costs, while also improving the air quality and considering the users’ temperature and luminosity preferences. This paper shows the results achieved, by the proposed solution, in an office building case study. The promising results demonstrate the possibility of minimizing energy costs while maximizing the users’ comfort. © 2021, The Author(s).

关键词： Air quality

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Distribution Network Digital Twin: a Basic Machine Learning based Voltage Estimator

Distribution Network Digital Twin: a Basic Machine Learning ...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Mohamad El Iaali Reza Razi Antoine Bruyere Bruno Francois Joao Soares Univ. Lille Arts et Metiers Institute of Technology Centrale Lille Junia ULR 2697 - L2EP Lille France GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate LAboratory Polytechnic of Porto Porto Portugal

The increasing integration of renewable energy sources, and the widespread use of electric vehicles have introduced greater complexity and dynamics in power flows in distribution systems. With bidirectional power flows enhancing the observability of the grid becomes essential to identify critical feeders. In this context, data-driven methods offer a promising approach to enhance situational awareness for power system operators and improve their understanding of the system behavior under variabilities. This paper proposes a voltage estimation method based on a Machine Learning algorithm to address this challeng.. By leveraging data-driven techniques, the proposed method aims to provide accurate and efficient voltage estimations in distribution networks, contributing to the effective management and control of modern power systems.

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Anomaly Detection in Cyber-Physical Systems: Reconstruction of a Prediction Error Feature Space

Anomaly Detection in Cyber-Physical Systems: Reconstruction ...

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Security of Information and Networks (SIN), International Conference on

作者： Nuno Oliveira Norberto Sousa Jorge Oliveira Isabel Praça Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Porto School of Engineering (ISEP) Porto Portugal

ISBN: (纸本)9781728192673

Cyber-physical systems are infrastructures that use digital information such as network communications and sensor readings to control entities in the physical world. Many cyber-physical systems in airports, hospitals and nuclear power plants are regarded as critical infrastructures since a disruption of its normal functionality can result in negative consequences for the society. In the last few years, some security solutions for cyber-physical systems based on artificial intelligence have been proposed. Nevertheless, knowledge domain is required to properly setup and train artificial intelligence algorithms. Our work proposes a novel anomaly detection framework based on error space reconstruction, where genetic algorithms are used to perform hyperparameter optimization of machine learning methods. The proposed method achieved an F1-score of 87.89% in the SWaT dataset.

关键词： Support vector machines Machine learning algorithms Machine learning Cyber-physical systems Predictive models Prediction algorithms Security

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Electric Vehicles Uncertainty Forecasting

Electric Vehicles Uncertainty Forecasting

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Saudi Arabia Smart Grid Conference (SASG)

作者： Juliana Chavez Zahra Foroozandeh Sérgio Ramos João Soares Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto School of Engineering (ISEP) 4200-072 Porto Portugal Polytechnic of Porto School of Engineering (ISEP) 4200-072 Porto Portugal

The growing integration of electric vehicles has attracted a lot of interest. However, they are highly affected by EV charging uncertainties and are, therefore, difficult to forecast accurately. This paper presents an Artificial Neural Network (ANN) method ANN to forecast electric vehicle uncertainties. ANN was trained using historical data from a residential building, such as arrival time, departure time and initial SOC. Then, it was tested during 24 hours through different scenarios. For each one of the cases, the model’s accuracy was assessed by comparing historical data to forecast information. The associated errors were also calculated. The outcomes reveal that the suggested forecasting method is very effective in reducing EV forecasting errors and, as a result, is better at regulating EV uncertainty.

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Short-term electricity demand forecasting via variational autoencoders and batch training-based bidirectional long short-term memory

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartC期52卷

作者： Moradzadeh, Arash Moayyed, Hamed Zare, Kazem Mohammadi-Ivatloo, Behnam [a]Faculty of Electrical and Computer Engineering University of Tabriz *** Tabriz Iran [b]GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (P. PORTO) P-4200-072 Porto Portugal [c]Information Technologies Application and Research Center İstanbul Ticaret University Istanbul Turkey

Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus, the forecasting results in most cases did not have acceptable accuracy due to the mentioned problems. In this paper, in order to short-term electricity load forecast in Tabriz, Iran, a hybrid technique based on deep learning applications called Variational Autoencoder Bidirectional Long Short-Term Memory (VAEBiLSTM) is presented. Pre-processing, noise cancellation, and time-series state modeling of the data are prominent features of the developed load forecasting model. In addition, in order to prevent overfitting problems in the process of training large amounts of data, the training process is developed in the form of batch training. Load forecasting is done using meteorological and environmental data of Tabriz city as well as historical information and days of the week as input variables. In the hybrid method structure, the Variational Autoencoders are applied to the data for data preprocessing and reconstruction. Then, the normalized, noise-free data is utilized as a dataset for training the Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed training method for BiLSTM is based on batch training. To present the effectiveness of the proposed technique in a comparative approach, the conventional LSTM and Support Vector Regression (SVR) algorithms are also applied to the data. Each network is trained with input data related to the years of 2017 and 2018 to predict the electricity load of the Tabriz city separately for each of the four seasons of the 2019 year. The forecasting results obtained from each method are evaluated by different statistical performance indicators. It can be seen that the proposed

关键词： Electricity demand Load forecasting Deep learning Variational Autoencoders (VAE) Bidirectional long short-term memory (BiLSTM)

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Renewable Energy Communities Classification

Renewable Energy Communities Classification

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IEEE General Meeting Power& Energy Society

作者： Bruno Canizes João Costa Zita Vale 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 Porto Portugal Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto Portugal

It is clear and inevitable that the current electricity sector will change to a new model. For everyone in this value chain, the future holds new projects and challeng.s that will get harder and harder. Technological advances have led to the creation of new ideas, such as renewable energy communities, decentralized renewable energy generation, and even the storage of renewable energy. These things have given consumers a more active role in the electricity sector and helped them reach their environmental goals in a big way. With the rise of communities that use renewable energy, there is a need for new management tools, mostly in generation and load management. So, this paper suggests a platform that can group consumers and prosumers together based on their energy use and where they live and also classify those groups (communities). Two algorithms, K-means, and DBSCAN, are thought to be able to do this. With this work, it will be possible to check how each algorithm works and how well it puts players into communities. Nine cities in the north of Portugal were considered to show how the proposed platform could be used. The findings provide evidence that the developed platform is adequate, versatile, and reliable regarding classifying communities that use renewable energy.

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