• DocumentCode
    2539299
  • Title

    The Annual Power Demand Prediction Approach by Fuzzy-Genetic Algorithm

  • Author

    Lin, Wen-Bin ; Lin, Chia-Ching ; Chiang, Huann-Keng ; Chen, Chien-An ; Tai, Liang-I

  • Author_Institution
    Grad. Sch. of Eng. Sci. & Tech., Nat. Yunlin Univ. of Sci. & Technol., Douliou, Taiwan
  • fYear
    2010
  • fDate
    13-15 Dec. 2010
  • Firstpage
    224
  • Lastpage
    227
  • Abstract
    This paper focus on two types of the industry class high-voltage consumer, to investigate various kind of electricity fees which includes demand charge, energy charge, power factor charge and penalty charge, and correlation among them according to the monthly electricity fee calculation structure in the past. Using the simulation of Fuzzy theory analysis and the Optimal Learning of Genetic Algorithm method, the optimal contract capacity can be derived by selecting annual peak load as a key parameter. The Industrial Class Consumer can predict the plant operation power consumption to fulfill energy conservation goal.
  • Keywords
    electricity supply industry; genetic algorithms; power system economics; Fuzzy theory analysis; annual peak load; annual power demand prediction approach; demand charge; electricity fee calculation structure; energy charge; energy conservation; fuzzy-genetic algorithm; industry class high-voltage consumer; optimal learning; penalty charge; power consumption; power factor charge; Books; Contracts; Electricity; Equations; Load modeling; Mathematical model; Power demand; Fuzzy theory; Genetic Algorithm; annual power demand; the optimum contract capacity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-1-4244-8891-9
  • Electronic_ISBN
    978-0-7695-4281-2
  • Type

    conf

  • DOI
    10.1109/ICGEC.2010.62
  • Filename
    5715410