• DocumentCode
    2063925
  • Title

    Free electricity market: how industrial customers and ESCOs can make the most from load forecasting techniques

  • Author

    Cristofaro, Massimo ; Frosini, Lucia ; Anglani, Norma

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Pavia, Italy
  • fYear
    2003
  • fDate
    23-25 June 2003
  • Firstpage
    13
  • Lastpage
    18
  • Abstract
    The implementation of linear and neural dynamic models to get the load forecasting for an industrial site, on different time frames (hourly, daily and weekly based) is presented in this paper. The identification process of these models is based on the electric energy consumptions sampled on a quarter of an hour basis over the last two years is a single point of acquisition. The performance of the proposed models have been compared: in this paper, results on the weekly-based models reported and the effective cost reduction verified. This well-known tool is investigated from an ESCO´s point of view, whose intention is to promote additional services to customers who are keen to invest less on equipment than on value-added services.
  • Keywords
    costing; customer relationship management; dynamic programming; linear systems; load forecasting; minimisation; neural nets; power consumption; ESCO; additional service; cost reduction; electric energy consumption; electricity cost; free electricity market; identification process; industrial customer; industrial energy flow; industrial site; linear dynamic model; load forecasting; model performance; neural dynamic model; optimal management; optimal planning; time frame; value-added service; weekly-based model; Contracts; Costs; Electrical equipment industry; Electricity supply industry; Energy consumption; Instruments; Load forecasting; Manufacturing; Predictive models; Production;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Soft Computing in Industrial Applications, 2003. SMCia/03. Proceedings of the 2003 IEEE International Workshop on
  • Print_ISBN
    0-7803-7855-5
  • Type

    conf

  • DOI
    10.1109/SMCIA.2003.1231336
  • Filename
    1231336