ANNモデルを用いた短期電力負荷予測におけるリスクの定量化  [in Japanese] Risk Quantification for ANN Based Short-Term Load Forecasting  [in Japanese]

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Abstract

A new risk assessment method for short-term load forecasting is proposed. The proposed method makes use of an Artificial Neural Network (ANN) to forecast one-step ahead daily maximum loads and evaluate uncertainty of in load forecasting. As ANN the model, the Radial Basis Function (RBF) network is employed to forecast loads due to the good performance. Sufficient realistic pseudo-scenarios are required to carry out quantitative risk analysis. The multivariate normal distribution with the correlation between input variables is used to give more realistic results to ANN. In addition, the method of Moment Matching is used to improve the accuracy of the multivariate normal distribution. The Peak Over Threshold (POT) approach is used to evaluate risk that exceeds the upper bounds of generation capacity. The proposed method is successfully applied to real data of daily maximum load forecasting.

Journal

  • IEEJ Transactions on Power and Energy

    IEEJ Transactions on Power and Energy 126(1), 29-35, 2006-01-01

    The Institute of Electrical Engineers of Japan

References:  24

Cited by:  5

Codes

  • NII Article ID (NAID)
    10016922316
  • NII NACSIS-CAT ID (NCID)
    AN10136334
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    03854213
  • NDL Article ID
    7759818
  • NDL Source Classification
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL Call No.
    Z16-794
  • Data Source
    CJP  CJPref  NDL  J-STAGE 
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