Reservoir level prediction and scheduling method based on multiple linear regression and meteorological data

Abstract

The invention relates to a reservoir level prediction and scheduling method based on multiple linear regression and meteorological data, and the method comprises the following steps: S1, obtaining a reservoir level historical related data set, and carrying out the preprocessing; and S2, on the basis of the pre-processed reservoir water level related data set, constructing and training a multiple linear regression model, and constructing a reservoir water level scheduling model and a warehouse-out control scheduling model. S3, reservoir water level data are obtained and combined with meteorological data, the trained reservoir water level scheduling model is called, and the reservoir water level is predicted; s4, on the basis of the prediction result, whether the reservoir prediction water level reaches the early warning water level is judged, and if the reservoir prediction water level reaches the early warning water level, early warning information is transmitted to the warehouse-out control scheduling model, and the flood discharge amount needing to be adjusted is calculated; and S5, adjusting the opening degree of a reservoir gate on the basis of the calculated flood discharge amount needing to be adjusted. According to the invention, accurate prediction of the reservoir level can be realized, early warning of the future reservoir level can be timely realized, and a flood discharge adjustment scheme can be obtained.

Description

technical field [0001] The invention relates to the technical field of reservoir monitoring, in particular to a reservoir water level prediction and scheduling method based on multiple linear regression and meteorological data. Background technique [0002] The water level of the reservoir is an important factor affecting the health of the reservoir, and the water level of the reservoir has always been the primary monitoring target in the reservoir health monitoring project. Most of the existing technologies are biased towards reservoir water level monitoring, that is, how to collect more reliable reservoir water level data. Reservoir water level regulation technology still relies on reservoir management experience and real-time rainfall and real-time reservoir water level data to calculate a more reasonable reservoir flood discharge and adjust the reservoir water level in real time. This technology cannot adjust the water level of the reservoir in advance, and cannot meet ...

AI Technical Summary

Problems solved by technology

This technology cannot adjust the water level of the reservoir in advance, and cannot meet the flood discharge demand of the reservoir for a safe water level when it rains heavily

[0003] The existing reservoir water level monitoring technology cannot predict the water level, and cannot make reasonable adjustments to the reservoir water level in advance. The function of this method is limited to the collection of raw data

The existing reservoir water level regulation technology relies on the experience of managing reservoirs, and its mobility is weak, and it is not combined with the reservoir water level prediction technology. When heavy rain, heavy rain, or torrential rain occurs in the future, it cannot make reasonable adjustments to the reservoir water level in advance according to the weather conditions. , causing the water level of the reservoir to be too high in the future. At the same time, there will be a large amount of precipitation. If the flood discharge is not increased, the health of the reservoir will be damaged, and the dam will even face the danger of collapse.

When faced with this situation, reservoir managers usually increase the amount of flood discharge. Due to the influence of the weather, the downstream of the reservoir has a high water level at this time. Increasing the amount of flood discharge at this time will cause the downstream residential areas to be flooded, directly causing economic losses, and even endangering the lives of downstream residents. life safety

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