Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)

The Korean Institute of Electrical Engineers (대한전기학회)
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A short-term Load Forecasting Using Chaotic Time Series

Chaos특성을 이용한 단기부하예측

  • Choi, Jae-Gyun (School of Electrical Engineering, Seoul National University) ;
  • Park, Jong-Keun (School of Electrical Engineering, Seoul National University) ;
  • Kim, Kwang-Ho (Dep. of electrical Engineering, KangWon University)
  • Published : 1996.07.22
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Abstract

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network(Back-propagation) is proposed. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.

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