Water level prediction method and device and storage medium

Abstract

The invention relates to the technical field of water level prediction, in particular to a water level prediction method and device and a storage medium. The method comprises the steps of obtaining generated power parameters and average water consumption rates of an upstream hydropower station and a current hydropower station; according to the power generation power parameters and the average water consumption rate of the upstream hydropower station, the power generation water amount of the upstream hydropower station in the set time period is obtained through calculation, and according to thepower generation power parameters and the average water consumption rate of the current hydropower station, the power generation water amount of the current hydropower station in the set time periodis obtained through calculation; according to the power generation water amounts of the upstream hydropower station and the current hydropower station in the set time period, determining the net increasing water amount of the current hydropower station; calculating to obtain a water level change value of the current hydropower station according to the net increasing water yield, and determining apredicted water level of the current hydropower station according to the water level change value of the current hydropower station. According to the method, specific operation data of the upstream hydropower station and the current hydropower station are used as support, and water level prediction of the current hydropower station in the dry season can be efficiently and accurately completed.

Description

technical field [0001] The invention relates to the technical field of water level prediction, in particular to a water level prediction method, device and storage medium. Background technique [0002] Water level prediction refers to predicting the upstream or downstream water level of a hydropower station a certain time in advance. Accurate water level prediction is of great significance to hydropower station scheduling. However, hydropower stations are affected by many factors during their operation, resulting in large deviations in the prediction of water level changes in hydropower stations, which in turn affects the implementation effect of the dispatching plan. Especially for cascade hydropower stations with close hydraulic connections, the water level rises and falls with hysteresis, which makes it more difficult to accurately predict the water level of downstream run-of-the-river hydropower stations. The water level on the dam of a hydropower station determines the...

AI Technical Summary

Problems solved by technology

In terms of water level prediction of cascade power stations, especially the water level of downstream power stations, due to the close connection between cascade power stations, the operation of upstream power stations will affect the water level of lower power stations, and there is a time lag in the impact due to water flow propagation, while ordinary Feedforward neural networks such as BP neural networks or RBF neural networks are difficult to capture this feature

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