• DocumentCode
    603611
  • Title

    Forecasting domestic hourly load profiles using vector regressions

  • Author

    Chanza, M. ; Ramjith, P. ; van Harmelen, G.

  • Author_Institution
    Enerweb, Johannesburg, South Africa
  • fYear
    2013
  • fDate
    3-4 April 2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Electricity distribution to households and business is the final step in the electricity generation, transmission and distribution value chain. Redistributed energy by municipalities to the South African domestic market is key to planning energy demand and supply. In this study we use the concept of vector regression modelling in forecasting total domestic hourly load profiles. Vector regression is done by aligning the time series so that days and hours match across the time period. A linear model is then fitted across this vector [1]. MSE, MAD and the MAPE are used in the study to evaluate model accuracy. Results from this study show that domestic load profiles are seasonal with peaks in the morning and in the evening around 8pm. Also winter months show high usage compared to the other seasons and this domestic load is by far the most dominant profile shape of netenergy sent out from generation.
  • Keywords
    load forecasting; mean square error methods; power markets; power system simulation; regression analysis; time series; vectors; MAD; MAPE; MSE; South African domestic market; electricity distribution value chain; electricity generation value chain; electricity transmission value chain; energy demand planning; energy supply planning; forecasting domestic hourly load profile; linear model; time series; vector regression modelling; Accuracy; Forecasting; Load forecasting; Load modeling; Predictive models; Time series analysis; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Domestic Use of Energy Conference (DUE), 2013 Proceedings of the 21st
  • Conference_Location
    Cape Town
  • Print_ISBN
    978-1-4799-0050-3
  • Type

    conf

  • Filename
    6524784