CN111553394A - Reservoir water level prediction method based on cyclic neural network and attention mechanism - Google Patents
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Abstract
The invention belongs to the field of reservoir operation control, and discloses a reservoir water level prediction method based on a recurrent neural network and an attention mechanism, which comprises the following steps: collecting flow and water level data to form a data set, and classifying the data set; converting the water level and flow values of the data set to obtain a standard data set; constructing a cyclic neural network model with an attention mechanism, and defining a target function; selecting data of a standard data set to train the recurrent neural network model; verifying and testing the trained recurrent neural network model; and predicting the water level of the key section by using the trained recurrent neural network model. The calculation precision of the model can be continuously improved along with the accumulation of the reservoir operation data, the dependence on the accuracy of the boundary condition is low, and the problem of low calculation precision of the traditional hydrodynamic method can be effectively solved; in reservoir flood season scheduling, the key section water level can be accurately predicted, and technical support is provided for flood control and prosperity scheduling decisions of large-scale reservoirs or cascade reservoirs.
Description
Technical Field
The invention belongs to the field of reservoir operation control, and particularly relates to a reservoir water level prediction method based on a recurrent neural network and an attention mechanism.
Background
The forecasting calculation of the water level of the key section of the reservoir plays an important role in reservoir flood control dispatching. In the traditional method, under the condition of given warehousing flow and dam front water level, the water level and flow of all sections in a reservoir area are calculated by solving a hydrodynamics equation (saint-wien equation system), and the water level of a key section is contained in the water level. The method has the advantages that the method has complete mathematical theory support, can completely connect all hydraulic parameters together to form a whole by constructing a more detailed river network space distribution structure, is convenient to process and can adapt to various initial conditions; the method has the disadvantages that after the model is established, whether the model meets the requirements needs to be checked, particularly for the calibration and verification of the river course roughness of the sensitivity parameter, a large amount of labor, time and calculation cost are needed, and the requirement on the accuracy of the input boundary condition is high.
For a large reservoir, particularly in a flood season, the accuracy of a hydrodynamic model is not high, and the main reasons can be summarized as the following three points: (1) the warehousing flow of the reservoir is difficult to accurately count. For large reservoirs which extend for hundreds of kilometers, the influx flow of rainfall, torrential flood and the like in an interval is difficult to accurately calculate, so that the boundary condition of the model is inaccurate, which is a main cause of errors; (2) the hydrodynamic model has systematic errors. The hydrodynamic model depends on the terrain of the river channel and the position of the section, measurement errors exist in the data, the length of the river channel of the large reservoir is large, the number of the sections is large, and the accumulated errors also influence the prediction effect of the water level; (3) the conditions for the establishment of the holy-weinan equation set may not be met during the flood season. The slow flow is a precondition established by the holy-south equation set, and the flow velocity is usually obviously increased and the applicability of the holy-south equation set is reduced in the rainstorm or large-flow process in the flood season.
Through research of ten years, the water level calculation of the reservoir area of the three gorges reservoir in the Yangtze river basin in China still has a calculation error of 1-3 meters for the water level of the important section long-life station, so that the reason for the calculation error of the hydrodynamic model is extremely difficult to overcome. In order to solve the technical problem, no effective solution is provided at present.
Disclosure of Invention
The invention has the technical problems that the existing method for calculating the key section water level of the large reservoir by solving the hydrodynamic equation is influenced by measurement and statistical errors of warehousing flow, river channel section and the like, the accuracy of the predicted and calculated key section water level is not high, and the reasons causing the errors are extremely difficult to overcome.
The invention aims to solve the problems and provides a reservoir water level prediction method based on a recurrent neural network and an attention mechanism, which is characterized in that knowledge is learned in historical data of reservoir operation, the reservoir inlet and outlet flow and the mapping relation between the water level before the dam and the critical section water level are searched, and the calculation precision of the critical section water level is improved by using historical information; the method is a data-driven method, and is based on a gradient descent optimization algorithm[1]And the correlation between the data is automatically learned, and compared with the traditional hydrodynamic method, the method reduces the dependence on the accuracy of the boundary conditions and only needs to keep the accuracy of the future boundary conditions and the accuracy of the historical boundary conditions basically stable. Meanwhile, the method has evolutionary capability, the calculation precision of the water level of the critical section can be continuously improved along with the accumulation of historical data, and the problem that the calculation precision of the traditional hydrodynamic method is not high can be effectively solved.
The technical scheme of the invention is a reservoir water level prediction method based on a recurrent neural network and an attention mechanism, which comprises the following steps,
step 1: collecting historical data of the flow of the reservoir in and out of a reservoir and the water level of the key section before the dam to form a data set, and classifying the data set;
step 2: respectively transforming the water level and flow numerical values of each type of data set, mapping to an interval [0,1] to obtain a standard data set, and carrying out data segmentation on each type of standard data set according to the principle that 80% of the standard data set is a training set, 10% of the standard data set is a verification set and 10% of the standard data set is a test set;
and step 3: respectively constructing a recurrent neural network model with an attention mechanism aiming at each type of data set;
step 3.1: establishing a circulating neural network model, taking the flow of the reservoir in and out of the reservoir and the water level before the dam as the input of the neural network model, and taking the output of the neural network model as the predicted water level of the key section;
step 3.2: defining an optimization objective function based on a cyclic neural network model, wherein the optimization objective is that the error between the predicted water level and the actually measured water level is minimum;
and 4, step 4: selecting the training set training recurrent neural network model in the step 2, and solving the objective function in the step 3 to obtain optimized model parameters;
and 5: selecting the verification set in the step 2 to verify the cyclic neural network model, if the verified prediction effect is unqualified, adjusting the hyper-parameters of the model, re-training the model, and executing the step 4; if the verification is passed, executing the step 6;
step 6: selecting the test set in the step 2 to test the recurrent neural network model, recording the test result and evaluating the model;
and 7: and taking the real-time flow rate of the inlet and the outlet and the water level before the dam as the input of the model to obtain the output of the model, namely the predicted water level of the key section.
Further, in step 1, the data sets are classified according to different warehousing flows and dam front water level levels according to the water regime.
Further, in step 3, the optimization objective function is a least square function.
Preferably, the optimization objective function includes a least square term and a regular term of the neural network parameters, and the regular term of the neural network parameters and the weight coefficients thereof are used for preventing the network parameters from being over-fitted.
Preferably, in step 4, the objective function in step 3 is solved, and the objective function is solved by using a gradient descent method. Each iteration of the optimization algorithm is performed by selecting a part of samples from the training set instead of the whole training set, so that the convergence speed of the model is increased, and the prediction effect is improved.
Preferably, the Recurrent neural network model uses gru (gated redundant unit) computation nodes.
Compared with the prior art, the invention has the beneficial effects that: by introducing a data-driven thought and utilizing a recurrent neural network with a concern mechanism, the mapping relation between the flow of the reservoir in and out of the reservoir and the water level before the dam to the water level of the critical section is directly learned, the calculation precision of the model can be continuously improved along with the accumulation of the operation data of the reservoir, the dependence on the accuracy of the boundary condition is low, and the problem of low calculation precision of the traditional hydrodynamics method can be effectively solved. In the flood season scheduling of the reservoir, the water level of the critical section can be accurately predicted, and technical support is provided for flood control and prosperity scheduling decisions of a large reservoir or a cascade reservoir.
Drawings
The invention is further illustrated by the following figures and examples.
Fig. 1 is a schematic flow chart of a reservoir water level prediction method according to the present invention.
FIG. 2 is a data classification diagram of different water regimes according to an embodiment.
Fig. 3 is a schematic structural diagram of a recurrent neural network with a mechanism of interest according to an embodiment.
Fig. 4 is a comparison effect graph of the predicted water level and the measured water level of the three gorges reservoir 2009-year and 2019-year long-life station in the embodiment.
Fig. 5 is a comparison effect diagram of the predicted water level and the measured water level of the 2011 longevity station in the three gorges reservoir of the embodiment.
Fig. 6 is a comparison effect graph of the predicted water level and the measured water level of the 2012 longevity station of the three gorges reservoir according to the embodiment.
Fig. 7 is a comparison effect graph of the predicted water level and the measured water level of the longevity station in the three gorges reservoir 2014 according to the embodiment.
Fig. 8 is a comparison effect graph of the predicted water level and the measured water level of the 2017 longevity station of the three gorges reservoir in the embodiment.
Detailed Description
The reservoir water level prediction method based on the recurrent neural network and the attention mechanism is mainly applied to large reservoirs with certain operation histories. The three gorges reservoir is a reservoir with the largest reservoir capacity in the Yangtze river basin in China, has more than ten years of history of operation, is rich in accumulated data, has a distance of 757km between the upstream Zhutuo and the front of the dam, is easy to submerge when large storage flow occurs in a long-life region at the tail of the reservoir in a flood season and a water storage season, and is a sensitive region for submerging the tail of the three gorges reservoir. The embodiment selects the three gorges reservoir to predict the water level of the section of the long-life station of the three gorges reservoir.
As shown in fig. 1, the reservoir water level prediction method based on the recurrent neural network and the attention mechanism includes the following steps,
step 1: and collecting historical data of the flow of the reservoir in and out of the reservoir and the water level before the dam and the critical section. According to the historical operating condition of the reservoir, the flow of the reservoir entering and exiting the reservoir and the water level data of the key section in front of the dam in the past decade are collected to form a historical data set.
The embodiment collects the operation data from 2009, 1, 0 to 2019, 11, 18, 23 of the three gorges reservoir, including the three gorges smooth flow, the three gorges outbound flow, the cunning station flow, the wulong station flow and the phoenix mountain water level, and collects 95376 pieces of data in one group per hour. The three gorges smooth flow, the cun-beach flow and the Wulong flow are all warehouse entry flow components, the smooth flow is taken as the main part, and the Fenghuangshan water level represents the dam front water level.
And (4) according to the water regime, manually classifying the collected data according to different warehousing flows and dam front water level grades, and respectively executing the steps 2 to 6 for each class.
The three gorges reservoir has large variation of warehousing flow and dam front water level in one year, and the water regime is required to be carried out according to different warehousing flow and dam front water levelClassification, then building a model and evaluation separately for each class. FIG. 2 is a specific case of classification, where z represents the dam front water level in meters (m), q represents the three gorges smooth flow in cubic meters per second (m)3/s)。
Step 2: and mapping the water level and flow values to an interval [0,1] for each classified data set to obtain a standard data set, and performing data segmentation on each classified standard data set according to the principle that 80% of the standard data set is a training set, 10% of the standard data set is a verification set and 10% of the standard data set is a test set.
The way the data is normalized is calculated as follows:
standardized water level (original water level-lowest water level)/(highest water level-lowest water level)
Normalized flow ═ (original flow-minimum flow)/(maximum flow-minimum flow)
And step 3: and (3) aiming at each type of data set, respectively constructing a recurrent neural network model with an attention mechanism, taking the standardized flow of the reservoir in and out of the reservoir and the water level in front of the dam in a time sequence as input, and outputting the predicted water level of the key section. And defining a least square objective function based on the model, so that the predicted water level of the key section is the same as or similar to the actually measured water level.
And 4, step 4: inputting the standardized training set formed in the step 2 into the recurrent neural network model constructed in the step 3, and solving the objective function constructed in the step 3 by using a gradient descent method to obtain optimized model parameters.
The example used a gradient descent method referred to the "gradient descent method" of Liuying super, Zhang Yuan published by university of Nanjing university of science.
In the embodiment, a TensorFlow open source software package implementation model is used, and an Adam optimizer in the TensorFlow open source software package implementation model is used for solving the objective function in the step 3, wherein the Adam optimizer is one implementation of a gradient descent method. TensorFlow software is disclosed in the article "TensorFlow: leaving functions at scale" Abadi Martini published by Acm SigplanNotices 5 2016.
And 5: verifying the model on the standard verification set formed in the step 2 according to the model parameters obtained in the step 4, adjusting the hyper-parameters in the model if the predicted effect of the verification is not expected, and returning to the step 4 to retrain the model; if the verification of the verification set is passed, step 6 is performed.
Step 6: and (3) selecting the test set in the step (2) to test the recurrent neural network model, recording the test result and evaluating the model.
And 7: and taking the real-time flow of the inlet and the outlet and the water level before the dam as the input of the model to obtain the water level of the key section predicted by the model.
In step 3, the constructed three-layer recurrent neural network model with the attention mechanism is shown in fig. 3, wherein the bottom part is the recurrent neural network, and the top part is the attention mechanism. The input of the whole model is the reservoir outlet and inlet flow and the dam front water level at the first n moments, the output is the long-life station water level at the (n + 1) th moment, and all the input and output are standardized data. In the embodiment, n is 2, and the time interval between two moments is 6 hours. W, U, b in FIG. 3 are network parameters; x is the number oftThe computing method comprises the steps that input vectors at time t are provided, components of the input vectors comprise three gorges smooth flow, warehouse-out flow, cun-dun flow, Wulong flow and dam front water level, h is a hidden state of corresponding nodes, the computing sequence of the input vectors conforms to the time sequence, flow and water level information is gradually synthesized from left to right and from bottom to top, each rectangle represents one synthesized computing node, common computing nodes comprise traditional computing nodes, LSTM (Long short-Term Memory) computing nodes and GRU (gated Current Unit) computing nodes, the computing modes of different computing nodes are shown in table 1, wherein sigma is a sigmoid function, tanh is a tanh function, ⊙ is a computing symbol multiplied by corresponding elements of the vectors, and GRU computing nodes are adopted in the embodiment.
TABLE 1 calculation of different nodes
For the focus mechanism part, output
By the recurrent neural netThe third layer of hidden state linear weighting structure, the weight coefficient is generated by the hidden state through a layer of feedforward neural network, the specific calculation mode is as follows:
α1,α2…αn-1,αnweight coefficients of the nodes of the third layer, βjFor the hidden state of each node of the third layer, Wα、bα、UαAre all parameters of the feedforward neural network, and e is a natural base number.
Based on the output of the model, an objective function is constructed:
wherein y isiIs the actual water level of the water tank,
the prediction result of the model is obtained, and N is the number of samples; the first term in the target function is a least square term, so that the predicted water level is the same as or similar to the actual water level; the second term is a regular term, lambda is a weight coefficient, and the function of the term is to limit the value range of parameters and prevent the model from being over-fitted.
In the embodiment, the operation data of the three gorges reservoir 2009-2018 is used for training and verification, and the operation data of the 2019 is used for testing. Table 2 shows the distribution of error intervals between the predicted water levels of the training set, the validation set, and the test set in the data sets of different categories according to the example, where z represents the water level before the dam, and q represents the smooth flow of the three gorges. Due to the regulation and storage function of the three gorges reservoir, the annual variation of the water level of the long-life station is about 30m, the maximum prediction error in the method is-1.27 m, and the error rate is only-4.2%; the maximum prediction error in 2019 is-0.69 m, the error rate is-2.3%, and the method has obvious advantages compared with the error of 1-3m in the traditional hydrodynamics method.
TABLE 2 table of predicted water level error intervals for various data sets of the example
| Class interval | Training set | Verification set | Test set |
| z∈[173.51,175.16] | [-0.36m,0.26m] | [-0.26m,0.24m] | [-0.15m,0.26m] |
| z∈[170,173.5] | [-0.25m,0.24m] | [-0.21m,0.23m] | [-0.27m,0.26m] |
| z∈[144.77,169.99],q∈[25000,71200] | [-1.27m,1.11m] | [-1.19m,1.08m] | [-0.69m,0.39m] |
| z∈[165.01,169.99],q∈[995,24999] | [-0.31m,0.26m] | [-0.28m,0.26m] | [-0.21m,0.21m] |
| z∈[160.01,165],q∈[995,24999] | [-0.35m,0.59m] | [-0.32m,0.51m] | [-0.31m,0.20m] |
| z∈[150.01,160],q∈[995,24999] | [-0.90m,0.88m] | [-0.62m,0.52m] | [-0.46m,0.37m] |
| z∈[144.77,150],q∈[995,24999] | [-0.96m,1.01m] | [-0.98m,1.02m] | [-0.39m,0.48m] |
Fig. 4 is a comparison graph of the predicted water level and the measured water level from 1 month 1 in 2009 to 18 months in 2019, wherein the predicted water level curve and the measured water level curve mostly coincide with each other. The elevations of the land expropriation line and the migration line of the longevity station of the three gorges reservoir are 175.7m and 178m respectively, and the water level exceeds the land expropriation line in 2011, 2012, 2014 and 2017. Fig. 5 to 8 show water level prediction conditions in 2011, 2012, 2014 and 2017 respectively, and it can be seen that when the water level exceeds the land utilization line, the prediction accuracy is very high, so that the method provided by the invention can provide an accurate water level prediction value for real-time scheduling of the three gorges reservoir.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. The reservoir water level prediction method based on the recurrent neural network and the attention mechanism is characterized by comprising the following steps,
step 1: collecting historical data of the flow of the reservoir in and out of a reservoir and the water level of the key section before the dam to form a data set, and classifying the data set;
step 2: respectively transforming the water level and flow values of each type of data set to obtain standard data sets, and dividing each type of standard data sets into a training set, a verification set and a test set;
and step 3: aiming at each type of data set, respectively constructing a recurrent neural network model with an attention mechanism, and defining a target function;
step 3.1: establishing a circulating neural network model, taking the flow of the reservoir in and out of the reservoir and the water level before the dam as the input of the neural network model, and taking the output of the neural network model as the predicted key section water level;
step 3.2: defining an optimization objective function based on a cyclic neural network model, wherein the optimization objective is that the error between the predicted water level and the actually measured water level is minimum;
and 4, step 4: selecting the training set training recurrent neural network model in the step 2, and solving the objective function in the step 3 to obtain optimized model parameters;
and 5: selecting the verification set in the step 2 to verify the cyclic neural network model, if the verified prediction effect is unqualified, adjusting the hyper-parameters of the model, re-training the model, and executing the step 4; if the verification is passed, executing the step 6;
step 6: selecting the test set in the step 2 to test the recurrent neural network model, recording the test result and evaluating the model;
and 7: and taking the real-time flow rate of the inlet and the outlet and the water level before the dam as the input of the model to obtain the output of the model, namely the predicted water level of the key section.
2. The reservoir water level prediction method based on the recurrent neural network and the attention mechanism as claimed in claim 1, wherein in step 1, the data sets are classified according to different storage flow rates and dam front water level levels according to water conditions.
3. The reservoir water level prediction method based on the recurrent neural network and the attention mechanism according to claim 1, wherein in step 3, the optimization objective function is a least square function.
4. The recurrent neural network and attention mechanism-based reservoir water level prediction method of claim 3, wherein the optimization objective function comprises a least square term and a regular term of neural network parameters, and the regular term of neural network parameters and its weight coefficients are used to prevent the network parameters from being over-fitted.
5. The reservoir water level prediction method based on the recurrent neural network and the attention mechanism as claimed in claim 1, wherein in step 4, the objective function of step 3 is solved, and the objective function is solved by a gradient descent method.
6. The recurrent neural network and attention mechanism-based reservoir water level prediction method according to any one of claims 1 to 5, wherein the recurrent neural network model employs GRU calculation nodes.
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| CN114611778A (en) * | 2022-03-04 | 2022-06-10 | 山东锋士信息技术有限公司 | Reservoir water level early warning method and system based on warehousing flow |
| CN114611778B (en) * | 2022-03-04 | 2022-09-06 | 山东锋士信息技术有限公司 | Reservoir water level early warning method and system based on warehousing flow |
| CN117689501A (en) * | 2024-02-03 | 2024-03-12 | 深圳市广汇源环境水务有限公司 | Reservoir flood prevention dynamic forecasting and early warning method and system platform |
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