A method for predicting the downstream water level of a reservoir based on a deep learning model

Abstract

The invention discloses a method for predicting the downstream water level of a reservoir based on a deep learning model. The maximum information coefficient is used to screen relevant factors of the downstream water level. On the basis of the correlation analysis, a genetic algorithm is used to optimize to obtain the best feature combination among the single relevant factors; Taking the best feature combination of downstream water level related factors as input, a deep learning model (CNNLSTM) based on convolutional neural network and long short-term memory network is constructed; Adam gradient optimization algorithm is used to train the weight variables of CNNLSTM model, and the trained CNNLSTM is used as A model for predicting the water level downstream of a reservoir. The prediction method of the invention takes into account the relevant factors of the downstream water level, optimizes the feature combination of the relevant factors, adopts a deep learning prediction model, effectively improves the prediction accuracy of the downstream water level, and has the advantages of accurately calculating the power generation output in the reservoir dispatching. Crucial role.

Description

technical field [0001] The invention relates to the technical field of reservoir downstream water level prediction, in particular to a reservoir downstream water level prediction method based on a deep learning model. Background technique [0002] Hydroelectric energy is clean, cheap and renewable green energy. The accurate calculation of the power generation output of the reservoir is a necessary link in the reservoir scheduling, and it is of great significance for the reservoir to achieve comprehensive benefits such as flood control, power generation, water supply and shipping. The downstream water level is one of the relevant factors in the calculation of power generation output. At present, in reservoir scheduling, the downstream water level is usually obtained by checking the tail water level flow curve by the discharge flow. However, the tail water level flow curve is usually obtained at the initial stage of the reservoir construction. As the reservoir operates for m...

AI Technical Summary

Problems solved by technology

[0006] (1) The downstream water level obtained traditionally through the tail water level and flow relationship curve has errors, which has an adverse effect on reservoir regulation

[0007] (2) Recalibrating the relationship curve of tailwater level and flow can reduce the error of downstream water level, but it is time-consuming, laborious and expensive, and it is not an efficient method

[0008] (3) Although the hydrodynamic simulation method can obtain the downstream water level with relatively high accuracy, it has the problems of complex modeling and time-consuming solution

[0009] (4) The accuracy of ordinary machine learning models to predict downstream water levels is limited

[0013] (3) The deep learning model is widely used in the fields of speech recognition and image processing, but it is rarely used in the field of water level prediction in the downstream of the reservoir, and the reference literature is limited

Images