入库径流预报误差随机模型及其应用

为了在量化入库径流预报误差的条件下有效提高调度方案制作的精度,基于高斯混合模型(GMM)良好的自适应性,能更准确地描述单一预见期径流预报误差分布的特点,以及高维meta-student t Copula函数具有将多个类型边缘分布有机耦合的优势,建立了多个预见期入库径流预报误差的GMM-Copula随机模型。以雅砻江流域锦屏一级水电站水库为例,对预见期分别为6 h、12 h、18 h、24 h的入库径流预报误差进行了分析与随机模拟。结果表明,随着预见期的增加,模拟误差与实际误差的主要统计特征值相差不大,满足预设精度要求,且变化规律一致,验证了模型的可行性与有效性,为水库调度方案的编制与实施提供了参考依据。

Stochastic model of reservoir runoff forecast errors and its application

To effectively improve the accuracy in formulated operation schemes under the condition of quantifying reservoir runoff forecast errors, a Gaussian mixture model (GMM)- Copula stochastic model is developed to describe runoff forecast errors for multiple forecast periods. This model is based on the characteristic of the GMM that accurately describes the distribution of runoff forecast errors for a single forecast period due to good adaptive performance. It incorporates the advantage of the high-dimensional meta-student t Copula function through dynamically coupling multiple types of marginal distributions. In a case study of the Jinping I hydropower station in the Yalong River basin, we conduct stochastic simulations and analysis of the reservoir runoff forecast errors for forecast periods of 6 h, 12 h, 18 h and 24 h. The results show that for different forecast periods, simulation errors and actual errors share the same changing pattern and similar major statistical eigenvalues, meeting the predetermined accuracy requirement. This verifies that our stochastic model is feasible and effective and it helps formulate and implement reservoir operation schemes.