• DocumentCode
    44845
  • Title

    Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids

  • Author

    Borges, Cruz E. ; Penya, Yoseba K. ; Fernandez, I.

  • Author_Institution
    Deusto Inst. of Technol.-DeustoTech Energy, Univ. of Deusto, Bilbao, Spain
  • Volume
    9
  • Issue
    3
  • fYear
    2013
  • fDate
    Aug. 2013
  • Firstpage
    1570
  • Lastpage
    1577
  • Abstract
    We present here a combined aggregative short-term load forecasting method for smart grids, a novel methodology that allows us to obtain a global prognosis by summing up the forecasts on the compounding individual loads. More accurately, we detail here three new approaches, namely bottom-up aggregation (with and without bias correction), top-down aggregation (with and without bias correction), and regressive aggregation. Further, we have devised an experiment to compare their results, evaluating them with two datasets of real data and showing the feasibility of aggregative forecast combinations for smart grids.
  • Keywords
    load forecasting; smart power grids; aggregative short-term load forecasting method; bottom-up aggregation; global prognosis; power system; regressive aggregation; smart grid; top-down aggregation; Forecasting; Load forecasting; Load modeling; Polynomials; Predictive models; Smart grids; Support vector machines; Demand forecasting; prediction methods;
  • fLanguage
    English
  • Journal_Title
    Industrial Informatics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1551-3203
  • Type

    jour

  • DOI
    10.1109/TII.2012.2219063
  • Filename
    6307853