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Parallel Processing Model for Syntactic Pattern Recognition-Based Electrical Load Forecast

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Parallel Processing and Applied Mathematics (PPAM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8384))

Abstract

A model of a recognition of distorted/fuzzy patterns for a electrical load forecast is presented in the paper. The model is based on a syntactic pattern recognition approach. Since a system implemented on the basis of the model is to perform in a real-time mode, it is parallelized. An architecture for parallel processing and a method of tasks distribution is proposed. First experimental results are also provided and discussed.

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Authors and Affiliations

  1. Information Technology Systems Department, Jagiellonian University, Ul. Prof. St. Lojasiewicza 4, 30–348, Cracow, Poland

    Mariusz Flasiński, Janusz Jurek & Tomasz Peszek

Authors
  1. Mariusz Flasiński
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  2. Janusz Jurek
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  3. Tomasz Peszek
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Corresponding author

Correspondence to Mariusz Flasiński .

Editor information

Editors and Affiliations

  1. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Department of Computer Science, Knoxville, Tennessee, USA

    Jack Dongarra

  3. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

  4. Technical University of Denmark Informatics and Mathematical Modelling, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Flasiński, M., Jurek, J., Peszek, T. (2014). Parallel Processing Model for Syntactic Pattern Recognition-Based Electrical Load Forecast. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2013. Lecture Notes in Computer Science(), vol 8384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55224-3_32

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  • DOI: https://doi.org/10.1007/978-3-642-55224-3_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55223-6

  • Online ISBN: 978-3-642-55224-3

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