CN113837463B - Nuclear power station system operation trend prediction method based on improved random forest - Google Patents

Nuclear power station system operation trend prediction method based on improved random forest Download PDF

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CN113837463B
CN113837463B CN202111107117.6A CN202111107117A CN113837463B CN 113837463 B CN113837463 B CN 113837463B CN 202111107117 A CN202111107117 A CN 202111107117A CN 113837463 B CN113837463 B CN 113837463B
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刘永阔
单龙飞
艾鑫
黄学颖
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Abstract

The invention provides a nuclear power station system operation trend prediction method based on an improved random forest, which comprises the following steps: (1) a random forest single-step prediction step; (2) a random forest multi-step prediction step; (3) multi-step predictive correction. The random forest prediction method used in the invention is accurate in single-step prediction, but has poor multi-step prediction effect, because the random forest can predict the change trend of the curve in prediction, but is insensitive to data change, the error of the random forest can be increased when the data is increased or reduced slowly, and the slope prediction considers the change of the slope in a short time and is sensitive to the change of the data in a short time, so the prediction can be performed in a mode of weighted average of the random forest prediction and the slope prediction, the accuracy of multi-step prediction can be effectively improved, and the availability of multi-step prediction of the random forest is improved.

Description

Nuclear power station system operation trend prediction method based on improved random forest
Technical Field
The invention relates to the field of online operation trend prediction of a nuclear power station system, in particular to a nuclear power station system operation trend prediction method based on an improved random forest.
Background
In the industrial field, safety is important, and especially in the nuclear power field with frequent accidents and high hazard, the safety is important. The operation trend prediction method predicts the future operation trend of nuclear power by using the history data of nuclear power station operation, and adopts corresponding measures before the occurrence of faults, thereby avoiding further deterioration of accidents. Therefore, research on the operation trend prediction method is of great significance in improving the safety of the nuclear power station.
Currently, nuclear power plants have a variety of methods applied to trend prediction. Such as: traditional statistical time series methods (autoregressive prediction method, moving average prediction method, autoregressive moving average prediction method), gray prediction method, neural network prediction method (such as BP neural network and the like) and some machine learning prediction methods (such as support vector machine, random forest and the like). The gray prediction method and the neural network prediction method are widely applied. The gray prediction method is suitable for short-term load prediction, especially when the data samples are missing. In the neural network prediction method, the BP neural network is most applied, but has strong dependence on initial parameters, is easy to sink into local minima, has low convergence rate and has a prediction effect to be improved. Especially in the real-time prediction process, retraining is needed for each prediction, and in order to avoid the optimization of the parameter optimizing algorithm which is required by the local minimum, a large amount of resources are required to be occupied. Random forests, while also commonly used for operational trend prediction, have poor prediction accuracy in a multi-step prediction process.
Disclosure of Invention
The invention aims to solve the problem of larger multi-step prediction errors of an operation trend prediction method, and discloses a nuclear power station system operation trend prediction method based on an improved random forest.
The purpose of the invention is realized in the following way:
a nuclear power station system operation trend prediction method based on an improved random forest comprises the following steps:
step 1: a random forest single-step prediction step;
step 1.1: constructing a random forest model according to the historical data of the current parameters;
Step 1.2: the next time stamped data is brought into a random forest. Traversing the whole decision tree from the root node to the leaf node according to the rule when constructing the decision tree by the time stamp in the decision tree, and taking the value of the leaf node as the predicted value of the decision tree;
Step 1.3: repeating step 1.2, traversing each decision tree, generating a predicted value by each decision tree, and averaging weighted values of all predicted values to obtain a final predicted value
Step 2: a random forest multi-step prediction step;
The time series multi-step prediction predicts the data of the next step of the parameter according to the historical data X before the current time of the parameter and the predicted value p i of each step of prediction, and the steps are as follows:
Step 2.1: predicting data p i at the next moment according to the single-step prediction of the random forest in the step 1;
Step 2.2: the data predicted in step 1.1 and the historical data were used as new training data [ x..p i ], trained on random forests. The random forest single step prediction is repeated to predict the next moment of data.
Step 2.3: and repeating the step 1.2 and the step 1.3 to realize multi-step prediction.
Step 3: multi-step predictive correction;
correcting the slope of the curve, and predicting the next moment of data through the current moment of data and the slope of the next moment of curve:
Let the current time be t, n pieces of history data of the current time be:
x＝{xt-n+1,xt-n+2,......,xt}
the least square method is carried out on every m pieces of historical data in the above formula, and n-m+1 slopes can be obtained in total:
k＝{kt-(n-m+2),kt-(n-m+3),......,kt}
the slope change rate can be calculated by the slope k in the above equation:
by the rate of change of slope in the above To predict the slope of the next steps:
the future n-step prediction result of the random forest is set as follows:
X＝{xt+1,xt+2,......,xt+n}
the slope prediction future n steps prediction result is:
Y＝{yt+1,yt+2,......yt+n}
the worse the accuracy of the random forest is along with the increase of the predicted steps, the weight corresponding to the result is reduced along with the increase, and the weight range is verified by experiments to be: [0.7,0.3] the corresponding slope prediction weight variation range is: [0.3,0.7]. Therefore, the weight corresponding to each step of the n-step prediction random forest is divided into the following steps according to the linearity:
w＝{0.7,...,n·(0.7-0.3)/15,...,0.3}
The corresponding slope prediction weight change range is 1-w, and the final prediction result is as follows:
res＝{xt+1·w1+yt+1·(1-w1),......,xt+n·wn+yt+n·(1-wn)}
compared with the prior art, the invention has the beneficial effects that:
The random forest prediction method used in the invention is accurate in single-step prediction, but has poor multi-step prediction effect, because the random forest can predict the change trend of the curve in prediction, but is insensitive to data change, the error of the random forest can be increased when the data is increased or reduced slowly, and the slope prediction considers the change of the slope in a short time and is sensitive to the change of the data in a short time, so the prediction can be performed in a mode of weighted average of the random forest prediction and the slope prediction, the accuracy of multi-step prediction can be effectively improved, and the availability of multi-step prediction of the random forest is improved.
Drawings
FIG. 1 is a flow chart of the improved random forest prediction of the present invention;
FIG. 2 is a graph of a single step operational trend prediction of an improved random forest of the present invention;
FIG. 3 is a graph of the improved random forest multi-step operational trend prediction of the present invention;
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description.
The implementation of the invention comprises the following steps:
Step 1: random forest single step prediction step
The time series single step prediction is to predict the data of the next moment of the parameter according to the historical data before the current time of the parameter, and the steps are as follows:
1) And constructing a random forest model according to the historical data of the current parameters.
2) The next time stamped data is brought into a random forest. The time stamp in the decision tree traverses the whole decision tree from the root node to the leaf node according to the rule when constructing the decision tree, and takes the value of the leaf node as the predicted value of the decision tree.
3) Repeating step 2) traverses each decision tree, each decision tree generates a predicted value, and the weighted average of all predicted values is the final predicted value.
Step 2: random forest multi-step prediction step
The time series multi-step prediction predicts the data of the next step of the parameter according to the historical data X before the current time of the parameter and the predicted value p i of each step of prediction, and the steps are as follows:
1) According to the random forest single step prediction in step 1 above, the data p i at the next time is predicted.
2) The data predicted in 1) and the historical data were used as new training data [ X,..p i ], trained on random forests. The random forest single step prediction is repeated to predict the next moment of data.
3) Repeating the steps 1) and 2) to realize multi-step prediction.
Step 3: multi-step predictive correction
Since the deviation between the multi-step predicted data of the random forest and the actual data is large, the slope of the curve is adopted for correction. The change of the slope of the curve can reflect the change trend of the curve to a certain extent, and if the change of the slope of the next moment of the curve is known, the data of the next moment can be predicted through the data of the current moment and the slope of the next moment of the curve.
Let the current time be t, n pieces of history data of the current time be:
x＝{xt-n+1,xt-n+2,......,xt} (1)
and (3) carrying out a least square method on every m pieces of historical data in the step (3), and obtaining n-m+1 slopes in total:
k＝{kt-(n-m+2),kt-(n-m+3),......,kt} (2)
the slope change rate can be calculated by the slope k in (4):
by the slope change rate in (5) To predict the slope of the next steps:
Although the random forest can predict the change trend of the curve in prediction, the random forest is insensitive to the change of the data, the error of the random forest may become larger when the data is increased or decreased slowly, and the slope prediction considers the change of the slope in a short time and is more sensitive to the change in the short time of the data, so the random forest prediction and the slope prediction can be performed in a weighted average mode.
The future n-step prediction result of the random forest is set as follows:
X＝{xt+1,xt+2,......,xt+n} (5)
the slope prediction future n steps prediction result is:
Y＝{yt+1,yt+2,......yt+n} (6)
the worse the accuracy of the random forest is along with the increase of the predicted steps, the weight corresponding to the result is reduced along with the increase, and the weight range is verified by experiments to be: [0.7,0.3] the corresponding slope prediction weight variation range is: [0.3,0.7]. Therefore, the weight corresponding to each step of the n-step prediction random forest is divided into the following steps according to the linearity:
w＝{0.7,...,n·(0.7-0.3)/15,...,0.3} (7)
The corresponding slope prediction weight change range is 1-w, and the final prediction result is as follows:
res＝{xt+1·w1+yt+1·(1-w1),......,xt+n·wn+yt+n·(1-wn)} (8)
The program is written by adopting a Python3.6 language by taking PyCharm as a development platform, and has the main functions of:
When the system is connected, inputting the real-time operation data of the nuclear power station, intercepting historical data with a certain length by using a sliding window as training data, training and improving a random forest prediction model by using the training data, and then predicting the operation trend of the nuclear power station system by using the trained model. The predicted results are displayed in the figure together with the actual running results.
The technical scheme of the invention is as follows:
1. The invention uses PCTran simulation software to obtain the running data of the pressurized water reactor nuclear power station, and uses the simulation data of the steam generator heat transfer pipe rupture accident as the verification data.
2. An improved random forest prediction model is constructed according to the flow chart shown in fig. 1, and the flow is as follows:
a. Selecting the number i of steps to be predicted;
b. Selecting the current k historical data X= { X n-k+1,...,xn } by using a sliding window, and combining the current k historical data X= { X n-k+1,...,xn } with the next predicted value p i to form a training number
Data { x n-k+1,...,xn,pi };
c. Training the random forest and slope prediction by using the training data in the step b, and outputting a prediction result of the next moment;
d. weighting and outputting a final predicted result p i by the predicted result of the random forest and the slope prediction in the step c;
f. And judging whether the current prediction step number n is smaller than i, if so, returning to i prediction values, otherwise, jumping to the step b to continue prediction.
(3) For the prediction flow of (2), if the number of prediction steps i=1 is a single-step prediction, the result of the prediction for the steam generator steam yield is shown in fig. 2. If i >1 is a multi-step prediction, the results for the steam generator steam production are shown in fig. 3, taking i=15 as an example.

Claims (1)

1. A nuclear power station system operation trend prediction method based on an improved random forest is characterized by comprising the following steps: the method comprises the following steps:
step 1: a random forest single-step prediction step;
step 1.1: constructing a random forest model according to the historical data of the current parameters;
Step 1.2: bringing the next time stamped data into a random forest; traversing the whole decision tree from the root node to the leaf node according to the rule when constructing the decision tree by the time stamp in the decision tree, and taking the value of the leaf node as the predicted value of the decision tree;
Step 1.3: repeating the step 1.2, traversing each decision tree, generating a predicted value by each decision tree, and averaging weighted average values of all the predicted values to obtain a final predicted value;
step 2: a random forest multi-step prediction step;
The time series multi-step prediction predicts the data of the next step of the parameter according to the historical data X before the current time of the parameter and the predicted value p i of each step of prediction, and the steps are as follows:
Step 2.1: predicting data p i at the next moment according to the single-step prediction of the random forest in the step 1;
Step 2.2: training by using the predicted data and the historical data in the step 1.1 as new training data [ X, & gt p i ] through a random forest; repeating the single-step prediction of the random forest to predict the data of the next moment;
step 2.3: repeating the step 1.2 and the step 1.3 to realize multi-step prediction;
Step 3: multi-step predictive correction;
correcting the slope of the curve, and predicting the next moment of data through the current moment of data and the slope of the next moment of curve:
Let the current time be t, n pieces of history data of the current time be:
x＝{xt-n+1,xt-n+2,......,xt}
the least square method is carried out on every m pieces of historical data in the above formula, and n-m+1 slopes can be obtained in total:
k＝{kt-(n-m+2),kt-(n-m+3),......,kt}
the slope change rate can be calculated by the slope k in the above equation:
by the rate of change of slope in the above To predict the slope of the next steps:
the future n-step prediction result of the random forest is set as follows:
X＝{xt+1,xt+2,......,xt+n}
the slope prediction future n steps prediction result is:
Y＝{yt+1,yt+2,......yt+n}
the worse the accuracy of the random forest is along with the increase of the predicted steps, the weight corresponding to the result is reduced along with the increase, and the weight range is verified by experiments to be: [0.7,0.3] the corresponding slope prediction weight variation range is: [0.3,0.7]; therefore, the weight corresponding to each step of the n-step prediction random forest is divided into the following steps according to the linearity:
w＝{0.7,...,n·(0.7-0.3)/15,...,0.3}
The corresponding slope prediction weight change range is 1-w, and the final prediction result is as follows:
res＝{xt+1·w1+yt+1·(1-w1),......,xt+n·wn+yt+n·(1-wn)}.
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