CN114757330A - Urban instantaneous water consumption prediction method based on LSTM - Google Patents
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Abstract
The invention discloses an LSTM-based urban instantaneous water consumption prediction method, which comprises the steps of establishing a prediction mechanism by taking a long-short term memory artificial neural network as a basic regression algorithm, acquiring actual normal operation data of part of urban water consumption units as training samples, learning and training to obtain an LSTM prediction model based on the long-short term memory artificial neural network, and finally obtaining a prediction value output by the LSTM prediction model by taking the urban water consumption in the next time period as the input of prediction. The invention adopts the urban water consumption data sets with similar fluctuation rates to participate in LSTM training, can solve the defect of low accuracy of the historical urban water consumption prediction algorithm, and can well solve the problem of slow convergence of the algorithm.
Description
Technical Field
The invention belongs to the technical field of urban water consumption prediction, and particularly relates to an LSTM-based urban instantaneous water consumption prediction method.
Background
With the continuous development of society and economy, the demand of urban water is increasing, but the available water supply is very limited. In order to solve the outstanding contradiction, the local water resource utilization condition must be analyzed and predicted so as to carry out long-term unified planning and management on local water resources and hydraulic engineering. Common urban water consumption prediction methods are divided into three main categories, namely an intuitive prediction method, a time sequence prediction method and a simulation model prediction method. The visual prediction method is a qualitative prediction method, which refers to a method for judging the future water use condition by depending on the visual judgment ability of people, and has certain subjectivity. The time series prediction method only depends on historical observation data to predict future water consumption, and each significant influence factor is not fully considered. The simulation model prediction method can overcome the limitations of the former two prediction methods, such as a prediction modeling method based on a neural network. The method can conveniently and flexibly predict the urban water consumption, thereby having certain practical value.
The factors influencing the urban water consumption are numerous, and a large amount of unpredictability and non-statistics exist, so that the difficulty of water quantity prediction is increased. The prediction of urban water consumption belongs to a nonlinear system, and a neural network shows obvious superiority in the aspect. Most of the current neural networks are trained by using a BP algorithm, and the biggest defects of the neural networks are that the neural networks are easy to fall into local extrema and require long training time.
Disclosure of Invention
The invention aims to provide an LSTM-based urban instantaneous water consumption prediction method, which solves the problems that most of neural networks adopted by existing urban water consumption prediction adopt BP (back propagation) algorithms, are easy to fall into local extreme values and require long training time.
The technical scheme adopted by the invention is as follows: the method for predicting the instantaneous urban water consumption based on the LSTM comprises the steps of firstly establishing a prediction mechanism by taking a long-short term memory artificial neural network as a basic regression algorithm, then collecting actual normal operation data of part of urban water consumption units as training samples, learning and training to obtain an LSTM prediction model based on the long-short term memory artificial neural network, and finally obtaining a predicted value output by the LSTM prediction model by taking the urban water consumption in the next time period as prediction input.
The present invention is also characterized in that,
the method for predicting the urban instantaneous water consumption based on the LSTM comprises the following specific operation steps:
step 1: acquiring urban water consumption data at historical time;
step 2: carrying out normalization processing on the urban water consumption data to obtain a training set;
and step 3: constructing an LSTM prediction model;
and 4, step 4: training the constructed LSTM model according to a training set to obtain an LSTM optimized prediction model of urban water consumption;
and 5: and predicting the urban water consumption at the next moment by the trained LSTM optimization prediction model to generate an urban water consumption prediction result.
The step 1 is as follows:
collecting daily flow data of historical urban water consumption from the hydrological station, wherein the flow data loss rate is required to be less than 10%, otherwise, giving up the daily flow data.
The step 2 is as follows: carrying out maximum value normalization processing on the urban water consumption data, namely dividing each water consumption data by the maximum value;
wherein, Fiti(i∈[1,2,…,n]) For the ith sampling data, Fit, in the municipal water consumption datamaxIs the maximum value of the sampled data;
and carrying out normalization processing on the data, wherein the data values of every three continuous time points are a group of training sets, and predicting the data value of the next time point.
And 3, realizing the neural network of the LSTM prediction model by an input layer, a hidden layer and an output layer, wherein the hidden layer has 6 neurons.
The step 4 is specifically as follows:
based on the self-defined parameters in the LSTM prediction model, inputting the training set prepared in the step 2 into the LSTM prediction model, performing forward calculation according to formulas (1) to (6), reducing the loss function value through an LSTM optimizer, and updating the LSTM prediction model and the LSTM optimizer weight parameter omegaf、ωi、ωcAnd ωoObtaining the learned network weight parameters, completing the optimization training of the LSTM prediction model, and obtaining the LSTM optimization prediction model;
the specific process of the forward calculation is as follows:
output from previous timeAnd current time input XtCalculating input gateForgetting doorAnd last moment memory unitAs shown in formulas (1) to (3):
③ pass through the output doorWill be provided withTo the current time outputAs in formulae (5) to (6):
wherein σ (·) and tanh (·) are Sigmoid function and hyperbolic tangent function, respectively; an all indicates a scalar product of two vectors; omegaf、ωi、ωc、ωoRespectively an input gateForgetting doorLast moment memory unitAnd output gateA weight matrix of (a); bf、bi、bc、boRespectively an input gate Forgetting doorMemory unit at last momentAnd an output gateThe offset vector of (2).
The step 5 is as follows:
step 5.1: selecting urban water consumption data of nearly 1 month, and carrying out normalization processing on the data according to the method in the step 2;
step 5.2: and (3) inputting the data subjected to normalization processing in the step 5.1 into an LSTM optimization prediction model to obtain an output value, and multiplying the output value by the maximum value used in normalization in the step 2 to obtain a predicted value of the urban water consumption at the next time point.
The invention is also characterized in that:
the invention has the beneficial effects that:
1. the invention adopts the urban water consumption data set with similar fluctuation rate to participate in the LSTM training, can solve the defect of low accuracy of the historical urban water consumption prediction algorithm, and can well solve the problem of slow algorithm convergence.
2. The invention predicts the urban water consumption by means of the LSTM prediction network, and can automatically realize the prediction of the urban water consumption.
3. The invention uses the LSTM model to realize more accurate convergence result, reduces the original prediction error rate of the urban water consumption to 1.505%, and shows that the model has higher prediction precision and better application value.
Drawings
FIG. 1 is a general flow diagram of the prediction method of the present invention;
FIG. 2 is a graph showing the results of an embodiment of the prediction method of the present invention;
FIG. 3 is a diagram of the prediction of the daily water consumption in a city according to the prediction method of the present invention.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
The invention discloses an LSTM-based urban instantaneous water consumption prediction method, which is implemented according to the following steps with reference to FIG. 1: the method for predicting the instantaneous urban water consumption based on the LSTM comprises the steps of firstly establishing a prediction mechanism by taking a long-short term memory artificial neural network as a basic regression algorithm, then collecting actual normal operation data of part of urban water consumption units as training samples, learning and training to obtain an LSTM prediction model based on the long-short term memory artificial neural network, and finally obtaining a predicted value output by the LSTM prediction model by taking the urban water consumption in the next time period as prediction input.
The specific operation steps are as follows:
step 1: acquiring urban water consumption data at historical time;
step 2: carrying out normalization processing on the urban water consumption data to obtain a training set;
and step 3: constructing an LSTM prediction model;
and 4, step 4: training the constructed LSTM model according to a training set to obtain an LSTM optimized prediction model of urban water consumption;
And 5: and predicting the urban water consumption at the next moment by the trained LSTM optimization prediction model to generate an urban water consumption prediction result.
The step 1 is as follows:
collecting daily flow data of historical urban water consumption from the hydrological station, wherein the flow data loss rate is required to be less than 10%, otherwise, giving up the daily flow data.
The step 2 is as follows: carrying out maximum normalization processing on the urban water consumption data, namely dividing each water consumption data by the maximum value;
wherein, Fiti(i∈[1,2,…,n]) For the ith sampling data, Fit, in the municipal water consumption datamaxIs the maximum value of the sampled data;
and carrying out normalization processing on the data, wherein the data values of every three continuous time points are a group of training sets, and predicting the data value of the next time point.
The LSTM prediction model consists of an input layer, a hidden layer and an output layer; and taking the output of the previous time node as the input of the next time node, and obtaining a predicted value after the final output is subjected to linear regression processing.
Wherein, the neuron of the LSTM model is input by an input gateForgetting doorOutput gateThe components are connected into a neuron model through a weight and an activation function.
As shown in fig. 2, the working principle can be summarized as follows: the input gate is used for controlling the input access or the access permission of the gate control equipment, and the forgetting gate controls the external state at the last moment The gate control device allows the amount of the gate control device to enter the time t after the time t flows, and the output gate is used for controlling the output value at the time tA door control device that is somewhat visible to the outside.
Firstly forgetting gate outputs by reading last momentAnd current time input XtOutputs a value between 0 and 1, determines what information we would discard from the neuron, then inputs gates determine that new information is stored in the neuron, and finally outputs gates based on the neuron memory cellsOutputting the predicted valueThe corresponding calculation results are as follows:
output from previous timeAnd current time input XtCalculating input gateForgetting doorAnd last moment memory unitAs shown in formulas (1) to (3):
③ pass through the output doorWill be provided withTo the current time outputAs in formulae (5) to (6):
wherein σ (·) and tanh (·) are Sigmoid function and hyperbolic tangent function, respectively; an all indicates a scalar product of two vectors; omegaf、ωi、ωc、ωoRespectively an input gateForgetting doorLast moment memory unitAnd output gateA weight matrix of (a); bf、bi、bc、boRespectively an input gateForgetting doorLast moment memory unitAnd output gateThe offset vector of (2).
Examples
The method comprises the following steps: obtaining the water consumption of city
Table 1 shows the recorded data of 12 years of water consumption/hundred million m in 2003-2014 of a city, Shandong province3
Step two: data normalization processing
From the water usage data in table 1: fitmax=7.08
The data were normalized and the results are shown in table 2.
TABLE 2 Water consumption normalization data
Step three: partitioning test sets
Test set partitioning standard: the data values at every three consecutive time points are a set of training sets, and the next time point data value is predicted, with the results shown in table 3.
Table 3 test set partitioning table
Step four: construction of LSTM prediction model
Designing an LSTM prediction model and an LSTM optimizer for learning and training; the neural network of the LSTM prediction model is realized by an input layer, a hidden layer and an output layer, wherein the hidden layer has 6 neurons, and the learning rate lr is 0.01. The goal of the LSTM prediction model is to minimize the mean square error between the predicted and true values. Step five: training optimized LSTM prediction model
Based on the self-defined parameters in the LSTM prediction model, inputting the training set prepared in the third step into the LSTM prediction model designed in the fourth step, performing forward calculation according to formulas (1) to (6), reducing the loss function value through an LSTM optimizer, and updating the LSTM prediction model and the LSTM optimizer weight parameter omega f、ωi、ωcAnd ωoAnd obtaining the learned network weight parameters to complete the training of the LSTM prediction model.
Step six: urban water consumption prediction
And (4) taking the last time point in each group of test sets as the input of the LSTM model, applying the LSTM prediction model obtained by training in the fifth step to obtain an output value, multiplying the output value by the maximum value used for normalization to obtain a predicted value of the urban water consumption at the next time point, and obtaining a prediction result shown in a table 4.
TABLE 4 Water consumption prediction based on LSTM model
The data results in table 4 show that the relative errors between the predicted values and the actual values in all the years are within 2% except that the difference between the urban water consumption in 2009, 2010 and 2012 is larger and the error is larger in two years before and after the urban water consumption. The difference between the predicted value and the actual value is proved to be small, and the fitting degree is high.
In fig. 3, the dots are real monitoring data of daily water consumption of a certain city (96 data are detected every 15 minutes), and the curve is predicted by using the method of the invention to predict the dots, namely: three consecutive dots are obtained each time, and the next point is predicted to generate a curve. FIG. 3 shows that the prediction result of the method has high precision and is close to actual data.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (7)
1. The method for predicting the instantaneous urban water consumption based on the LSTM is characterized by firstly establishing a prediction mechanism by taking a long-short term memory artificial neural network as a basic regression algorithm, then acquiring actual normal operation data of part of urban water consumption units as training samples, learning and training to obtain an LSTM prediction model based on the long-short term memory artificial neural network, and finally obtaining a prediction value output by the LSTM prediction model by taking the urban water consumption in the next period as prediction input.
2. The LSTM-based urban instantaneous water usage prediction method of claim 1,
step 1: acquiring urban water consumption data at historical time;
step 2: carrying out normalization processing on the urban water consumption data to obtain a training set;
and step 3: constructing an LSTM prediction model;
and 4, step 4: training the constructed LSTM model according to a training set to obtain an LSTM optimized prediction model of urban water consumption;
and 5: and predicting the urban water consumption at the next moment by the trained LSTM optimization prediction model to generate an urban water consumption prediction result.
3. The LSTM-based method for predicting instantaneous urban water consumption according to claim 1, wherein step 1 comprises the following steps:
Collecting daily flow data of historical urban water consumption from the hydrological station, wherein the flow data loss rate is required to be less than 10%, otherwise, giving up the daily flow data.
4. The LSTM-based method for predicting instantaneous urban water consumption according to claim 1, wherein step 2 is as follows: carrying out maximum normalization processing on the urban water consumption data, namely dividing each water consumption data by the maximum value;
wherein, Fiti(i∈[1,2,…,]) For the ith sampling data, Fit, in the municipal water consumption datamaxIs the maximum value of the sampled data;
and carrying out normalization processing on the data, wherein the data values of every three continuous time points are a group of training sets, and predicting the data value of the next time point.
5. The LSTM-based urban instantaneous water consumption prediction method according to claim 1, wherein the neural network of the LSTM prediction model of step 3 is implemented by an input layer, a hidden layer and an output layer, wherein the hidden layer has 6 neurons.
6. The LSTM-based method for predicting instantaneous urban water consumption according to claim 1, wherein step 4 is as follows:
based on the self-defined parameters in the LSTM prediction model, inputting the training set prepared in the step 2 into the LSTM prediction model, performing forward calculation according to formulas (1) to (6), reducing the loss function value through an LSTM optimizer, and updating the LSTM prediction model and the LSTM optimizer weight parameter omega f、ωi、ωcAnd omegaoObtaining the learned network weight parameters, completing the optimization training of the LSTM prediction model, and obtaining the LSTM optimization prediction model;
the specific process of the forward calculation is as follows:
firstly, the output of the last moment is utilizedAnd current time input XtCalculating input gateForgetting doorAnd last moment memory unitAs shown in formulas (1) to (3):
③ pass through the output doorWill be provided withTo the current time outputAs in formulae (5) to (6):
wherein σ (·) and tanh (·) are Sigmoid function and hyperbolic tangent function, respectively; an all indicates a scalar product of two vectors; omegaf、ωi、ωc、ωoRespectively an input gateForgetting doorLast moment memory unitAnd output gateA weight matrix of (a); bf、bi、bc、boRespectively an input gateForgetting doorLast moment memory unitAnd output gateThe offset vector of (2).
7. The LSTM-based urban instantaneous water consumption prediction method according to claim 4, wherein step 5 is as follows:
step 5.1: selecting urban water consumption data of nearly 1 month, and carrying out normalization processing on the data according to the method in the step 2;
step 5.2: and (3) inputting the data subjected to normalization processing in the step 5.1 into an LSTM optimization prediction model to obtain an output value, and multiplying the output value by the maximum value used in normalization in the step 2 to obtain a predicted value of the urban water consumption at the next time point.
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CN116451874A (en) * | 2023-06-14 | 2023-07-18 | 埃睿迪信息技术(北京)有限公司 | Urban water consumption prediction method, device and equipment |
CN116861192A (en) * | 2023-07-27 | 2023-10-10 | 广东中山建筑设计院股份有限公司 | Urban water consumption prediction method based on SATT-TCN-LSTM model |
CN118037120A (en) * | 2024-02-27 | 2024-05-14 | 青海九零六工程勘察设计院有限责任公司 | Emergency water supply capacity evaluation method for river valley underground water source |
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CN116451874A (en) * | 2023-06-14 | 2023-07-18 | 埃睿迪信息技术(北京)有限公司 | Urban water consumption prediction method, device and equipment |
CN116451874B (en) * | 2023-06-14 | 2023-09-05 | 埃睿迪信息技术(北京)有限公司 | Urban water consumption prediction method, device and equipment |
CN116861192A (en) * | 2023-07-27 | 2023-10-10 | 广东中山建筑设计院股份有限公司 | Urban water consumption prediction method based on SATT-TCN-LSTM model |
CN118037120A (en) * | 2024-02-27 | 2024-05-14 | 青海九零六工程勘察设计院有限责任公司 | Emergency water supply capacity evaluation method for river valley underground water source |
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