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水电站入库径流量的灰色自记忆预测方法研究

李力;沈冰;李荣峰;赵长森   

  1. 1.西安理工大学 水利水电学院，陕西 西安 710048；
    2.山西省水利厅，山西 太原 030002；
    3.山西省水利水电科学研究院，山西 太原 030002；4.中国科学院地理与资源研究所，北京 100101
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-03-20 发布日期:2007-03-20

Gray SelfMemory Prediction of Inflow to Hydropower Station Reservoir

LI Li;SHEN Bing;LI Rong feng;ZHAO Chang sen   

  1. 1. Institute of Water Resources and Hydro electric Engineering, Xi’an University of Technology, Xi’an 710048, China; 2. Shanxi Water Resources Bureau, Taiyuan 030002, China; 3. Shanxi Institute of Water Resources and Hydropower Research, Taiyuan 030002, China;4. Institute of Geography and Resource, Chinese Academy Science, Beijing 100101, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-03-20 Published:2007-03-20

摘要： 运用灰色系统理论与数据序列自记忆性原理相结合的方法，提出了数据序列预测的灰色自记忆模型，并应用于年径流量及汛期来水预测.山西沁河曹河水电站年径流及汛期径流预测的实例表明，该模型能处理非平稳数据序列并反映动态数据序列的极值趋势，可提高预测精度.

关键词: 径流量, 自记忆, 灰色模型, 预测

Abstract: Combining the theory of gray system with self memory principle, a new model for predicting time series of reservoir inflow is proposed. The model has been used to predict the annual and flood period inflow to reservoir of Caohe Hydropower Station on the Jin River. The case study indicates that the model can deal with non stationary time series and provide the inflow trend and extreme values with improved accuracy.

Key words: inflow, self memory, gray model, prediction