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IEEE Symposium Series on Computational Intelligence

作者： Aria Jozi Tiago Pinto Isabel Praca Sergio Ramos Zita Vale Benedicte Goujon Teodora Petrisor GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/ IPP) Porto Portugal Thales Research & Technology France Palaiseau France

Electrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the outcomes of the actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rule-based systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.

关键词： Energy consumption Forecasting Fuzzy rule-based systems TRT building

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Wang and Mendel's fuzzy rule learning method for energy consumption forecasting considering the influence of environmental temperature

Wang and Mendel's fuzzy rule learning method for energy cons...

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International Global Information Infrastructure Symposium (GIIS)

作者： Aria Jozi Tiago Pinto Isabel Praça Francisco Silva Brígida Teixeira Zita Vale GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP-Polytechnic of Porto Porto Portugal

Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel's Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources.

关键词： Energy consumption Buildings Forecasting Databases Learning systems Weather forecasting

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Optimization of electricity markets participation with QPSO

Optimization of electricity markets participation with QPSO

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International Conference on European Electricity Market, EEM

作者： Ricardo Faia Tiago Pinto Zita Vale GECAD-Research Group on Intelligent Engineering Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/IPP) Porto Portugal

All around the world, the electric sector has suffered significant changes. With these alterations, electrical systems have become international, with several countries connected by a system where the management is done in common grounds. With the incorporation of large scale distributed generation, competitiveness in electrical markets has increased as small generators unite in order to be able to compete with large producers. In this game where the main objective is to win, and the premium is money it is necessary to be keen to be able to sell the available electricity at the best possible prices. With the objective of supporting players' decisions, decision support tools play a crucial role. These tools enable market players with suggestions of actions to increase their advantage from market participation. This paper presents a Quantum-based Particle Swarm Optimization (QPSO) methodology to solve the problem of optimal participation in multiple electricity markets.

关键词： Optimization Electricity supply industry Portfolios Particle swarm optimization Biological system modeling Quantum mechanics Contracts

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Allocation of fixed costs considering Distributed Generation and distinct approaches of Demand Response remuneration in distribution networks

Allocation of fixed costs considering Distributed Generation...

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Clemson University Power Systems Conference (PSC)

作者： Fábio Pereira João Soares Pedro Faria Zita Vale GECAD — Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP-Polytechnic Institute of Porto Porto Portugal

In the present context the distribution networks are characterized by the increasing penetration of Distributed Generation (DG) resources, which imposes important changes in operation practice. Moreover, the concept of Demand Response (DR) is currently a very important resource. The present paper proposes a methodology that makes possible the allocation of fixed costs in distribution networks considering the integration of DG and DR resources. It considers several possibilities for consumers DR remuneration. The fixed cost allocation is determined in three phases - DC Optimal Power Flow (DC-OPF), Kirschen's tracing method, and MW-mile method. The presented case study contemplates a distribution network with 49 buses, 47 DG units, 4 external suppliers and 50 consumers.

关键词： Remuneration Resource management Density estimation robust algorithm Load flow Indexes Distributed power generation Optimal scheduling

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Preface

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and computing 2018年 620卷 v-vi页

作者： Omatu, Sigeru Rodríguez, Sara Villarrubia, Gabriel Faria, Pedro Sitek, Pawel Prieto, Javier Faculty of Engineering Department of Electronics Information and Communication Engineering Osaka Institute of Technology OsakaOsaka Japan Faculty of Science Department of Computer Science and Automation Control University of Salamanca Salamanca Spain GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Instituto Superior de Engenharia do Porto Porto Portugal Faculty of Electrical Engineering and Computer Science Department of Control and Management Systems Department of Information Systems Kielce University of Technology Kielce Poland

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Generation of intelligent reports for ubiquitous group decision support systems

Generation of intelligent reports for ubiquitous group decis...

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International Global Information Infrastructure Symposium (GIIS)

作者： Luís Conceição João Carneiro Diogo Martinho Goreti Marreiros Paulo Novais GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal Algoritmi Centre University of Minho Guimarães Portugal

Supporting group decision-making is a complex process, especially when decision-makers have no opportunity to gather at the same place and at the same time. Besides that, finding solutions may be difficult in case agents representing decision-makers are not able to understand the process and support them accordingly. In this work we present some topics of information that can be reported to decision-makers to improve their perception about the negotiation process. We classified those topics according to two dimensions and we defined an algorithm to select which information will be built for each report.

关键词： Decision making Context Decision support systems Proposals Organizations Data mining Complexity theory

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Integration of Pumping in Virtual Power Players management considering demand response

Integration of Pumping in Virtual Power Players management c...

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International Conference on European Electricity Market, EEM

作者： Diogo Boldt Pedro Faria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development R. Dr. António Bernardino de Almeida 431 Porto Portugal

The increase of distributed generation in several countries around the world brought several challenges to planning and operation of the electric network. In situations of high penetration of non-storable resources generation, mainly wind power, demand response programs and pumping may be applied, in order to encourage the increase of consumption, to guarantee a better electric network management. The present paper presents a methodology focusing on demand response programs, distributed generation and pumping, which aims to be used by a Virtual Power Player, who is able to manage the available resources optimizing its costs. The main objective is to use pump to store water to reservoirs, so the reservoir owner would have enough power to boost for energy generation. The case study includes 2223 consumers and 47 distributed generators, which was implemented using a Portuguese power system real scenario, 9th March 2014.

关键词： Reservoirs Wind power generation Load management Optimization Mathematical model Distributed power generation

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Energy consumption forecasting based on Hybrid Neural Fuzzy Inference System

Energy consumption forecasting based on Hybrid Neural Fuzzy ...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Aria Jozi Tiago Pinto Isabel Praça Francisco Silva Brigida Teixeira Zita Vale Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal

Forecasting the electricity consumption is one of the most challenging tasks for energy domain stakeholders. Having reliable electricity consumption forecasts can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study regarding the forecast of electricity consumption using a methodology based on Hybrid neural Fuzzy Inference System (HyFIS). The proposed approach considers two distinct strategies, namely one strategy using only the electricity consumption as the input of the method, and the second strategy uses a combination of the electricity consumption and the environmental temperature as the input. A case study considering the forecasting of the consumption of an office building using the proposed methodologies is also presented. Results show that the second strategy is able to achieve better results, hence concluding that HyFIS is an appropriate approach to incorporate different sources of information. In this way, the environmental temperature can help the HyFIS method to achieve a more reliable forecast of the electricity consumption.

关键词： Forecasting Buildings Training Fuzzy logic Databases Energy consumption Servers

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Benders' decomposition applied to Energy Resource Management in smart distribution networks

Benders' decomposition applied to Energy Resource Management...

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Clemson University Power Systems Conference (PSC)

作者： J. Soares Bruno Canizes Z. Vale G. K. Venayagamoorthy GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal Real-Time Power and Intelligent Systems Lab. Clemson University Clemson SC USA

This paper proposes a Benders' decomposition approach for the large-scale Energy Resource Management (ERM). The problem considers large amount of Distributed Energy Resources (DER) including Electric Vehicles (EVs) with gridable capacity. The proposed model is designed for Virtual Power Players/Plants (VPPs) managing a smart distribution network with the aim to maximize profits considering the day-ahead time horizon. Previous literature has shown difficulties in solving similar problems using classical techniques in a centralized scheme, namely considering nonlinear network constrains. The proposed approach reduces computational burden of the original Mixed Integer Nonlinear Programming (MINLP) problem. The Benders' cuts are introduced for the ERM formulation in order to allow the communication between the slave and the master problem. Analysis of two large-scale instances have been carried out for smart distribution networks, namely a 33-bus and a real Portuguese 180-bus network assuming high penetration of DERs and EVs.

关键词： Energy resources Optimization Partial discharges Load flow Smart grids Processor scheduling Load management

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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