Zita Vale

Zita ValeZita Vale (GECAD research group, Polytechnic of Porto, Portugal) is Professor at the School of Engineering (ISEP) of the Polytechnic of Porto (IPP) and Director of GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovatio and Developments. Her main interests regard the application of Artificial Intelligence Techniques to Power systems, including Knowledge based systems, Multi-agent systems, Neural Networks, Meta-heuristics, Optimization, Machine Learning, and Knowledge Discovery Techniques. She has been involved in more than 40 R&D projects from which she coordinated more than 20 projects. She is principally interested in Smart Grids, with an intensive use of Renewable Energy Sources, Distributed Energy Resources and Distributed Generation, addressing the management of energy resources, the negotiation of DER in electricity markets, demand response, and electric vehicles. She is also interested in electricity markets, addressing prices and tariffs, decision-support for market participants, ancillary services, derivatives market, pricing and market simulation. Read More Read Less

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Progress in Artificial Intelligence37 %
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Progress in Artificial Intelligence37 %
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Local Electricity Markets45 %
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08 Jul 2021
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Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions37 %
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Progress in Artificial Intelligence37 %
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09 Aug 2017
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Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems36 %
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Computational Intelligence for Engineering Systems37 %
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02 Dec 2010
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Computational Intelligence for Engineering Systems37 %
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27 Jan 2013
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Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection37 %
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Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection36 %
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Demand Response in Smart Grids18 % NR
Publisher: Mdpi AG
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28 Jan 2023
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Intelligent Data Mining and Analysis in Power and Energy Systems14 % NR
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09 Dec 2022
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Distributed Energy Resources Management 201824 % NR
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21 Jan 2020
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Applications of Modern Heuristic Optimization Methods in Power and Energy Systems10 % NR
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17 Apr 2020
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