• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system

    Thumbnail
    View/Open
    94556_1.pdf (1.370Mb)
    Author(s)
    Bennett, Christopher J
    Stewart, Rodney A
    Lu, Jun Wei
    Griffith University Author(s)
    Lu, Junwei
    Stewart, Rodney A.
    Year published
    2014
    Metadata
    Show full item record
    Abstract
    The advent of distributed renewable energy supply sources and storage systems has placed a greater degree of focus on the operations of the Low Voltage (LV) electricity distribution network. However, LV networks are characterised by having much higher variability in time series demand meaning that modelling techniques solely relying on iterative forecasts to produce a next day demand profile forecast are insufficient. To cater for the complexity of LV network demand, a novel hybrid expert system comprised of three modules, namely, correlation clustering, discrete classification neural network, and a post-processing procedure ...
    View more >
    The advent of distributed renewable energy supply sources and storage systems has placed a greater degree of focus on the operations of the Low Voltage (LV) electricity distribution network. However, LV networks are characterised by having much higher variability in time series demand meaning that modelling techniques solely relying on iterative forecasts to produce a next day demand profile forecast are insufficient. To cater for the complexity of LV network demand, a novel hybrid expert system comprised of three modules, namely, correlation clustering, discrete classification neural network, and a post-processing procedure was developed. The system operates by classifying a set of key variables associated with a future day and refining a recalled historical demand profile as the forecast. The expert system exhibited high hindcast accuracy when trained with a residential LV transformer's demand data with R2 values ranging from 0.86 to 0.87 and MAPE ranging from 11% to 12% across the three phases of the network. Under simulated real world conditions the R2 statistic reduced slightly to 0.81-0.84 and the MAPE increased to 12.5-13.5%. Future work will involve integrating the developed expert system for forecasting next day demand in an LV network into a comprehensive distributed energy resource management algorithm.
    View less >
    Journal Title
    Energy
    Volume
    67
    DOI
    https://doi.org/10.1016/j.energy.2014.01.032
    Copyright Statement
    © 2014 Elsevier Ltd. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
    Subject
    Mechanical engineering
    Resources engineering and extractive metallurgy
    Electrical engineering
    Fluid mechanics and thermal engineering
    Publication URI
    http://hdl.handle.net/10072/61378
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander