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Machine Learning Applications to Power Systems

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Book cover Machine Learning and Its Applications (ACAI 1999)

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Abstract

The recent developments in the power system area, i.e. the on-going liberalization of the energy markets, the pressing demands for power system efficiency and power quality, the increase of dispersed, renewable generation and the growing number of interconnections and power exchanges among utilities, dictate the need for improvements in the power system planning, operation and control. At the same time, the power equipment industry faces new challenges in nowadays ever-increasing competition. Artificial Intelligence techniques together with traditional analytical techniques can significantly contribute in the solution of the related problems. Indeed, during the last 15 years, pattern recognition, expert systems, artificial neural networks, fuzzy systems, evolutionary programming, and other artificial intelligence methods have been proposed in an impressive number of publications in the power system community.

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Authors and Affiliations

  1. Department of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, 15773, Athens, Greece

    Nikolaos Hatziargyriou

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  1. Nikolaos Hatziargyriou
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Editors and Affiliations

  1. National Centre for Scientific Research “Demokritos”, Institute of Informatics and Telecommunications, P.O. Box 60228, Ag. Paraskevi, 15310, Athens, Greece

    Georgios Paliouras , Vangelis Karkaletsis  & Constantine D. Spyropoulos ,  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Hatziargyriou, N. (2001). Machine Learning Applications to Power Systems. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_20

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  • DOI: https://doi.org/10.1007/3-540-44673-7_20

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42490-1

  • Online ISBN: 978-3-540-44673-6

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