Abstract
Outflow forecasting of upstream large hydropower stations is essential for dispatching of cascade hydropower system. In this study, seven outflow forecasting methods were proposed from aspects of optimizing power generation streamflow, water-energy conversion characteristics and data mining, and were applied to the actual dispatching of the Three Gorges-Gezhouba cascade hydropower system in China. Results show that the data mining method for tail water level is recommended to replace empirical formula for its insensitivity with outflow deviations in rolling forecast. Due to the complex hydrological characteristics, the data mining method is the only recommended method to forecast outflow in the flood season; while in the non-flood season, the variable comprehensive efficiency and water consumption rate methods are also workable. These three recommended outflow forecasting methods can reduce the accumulative deviation of tail and forebay water level, and improve the operation safety of cascade hydropower system significantly.
Published Version
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