Bayesian Statistic Forecasting Model for Middle-Term and Long-Term Runoff of a Hydropower Station

Abstract

The middle-term and long-term runoff forecasting model of the hydropower station reservoir is established with the Bayesian statistic forecasting theory; uncertainty of the hydrological forecasting is quantitatively described in the form of a probability distribution to explore the statistic forecasting theory and its application value. The uncertainty of the input factor is processed with the forecasting model of grey correlation of meteorological factors, and real-time weather data are effectively combined with the historical hydrological data to break through the restriction of traditional deterministic forecasting methods in the aspects of information utilization and sample study to improve the precision of hydrological forecasting. The established model has been assessed by the example of the reservoir of the Fengman hydropower plant. It is indicated by the analog computation result that this model, compared with the deterministic runoff forecasting method, has advantages not only in quantitatively considering the uncertainty in decision making, but also in improving the precision of runoff forecasting in the expected significance, and has comparatively high application value.