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Electricity Load Prediction Using Hierarchical Fuzzy Logic Systems

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3682))

Abstract

Electricity load forecasting has been the subject of research over the past several years by researchers and practitioners in academia and industry. This is due to its very important role for effective and economic operation of power stations. In this paper an intelligent hierarchical fuzzy logic system using genetic algorithms for the prediction and modelling of electricity consumption is developed. A hierarchical fuzzy logic system is developed to model and predict daily electricity load fluctuations. The system is further trained to model and predict electricity consumption for daily peak.

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Author information

Authors and Affiliations

  1. School of Information Sciences and Engineering, University of Canberra, Canberra, Australia

    Masoud Mohammadian & Ric Jentzsch

Authors
  1. Masoud Mohammadian
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  2. Ric Jentzsch
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Mohammadian, M., Jentzsch, R. (2005). Electricity Load Prediction Using Hierarchical Fuzzy Logic Systems. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_107

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  • DOI: https://doi.org/10.1007/11552451_107

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28895-4

  • Online ISBN: 978-3-540-31986-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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