CN112801418B - Method for predicting defective materials of power grid - Google Patents
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Abstract
The application discloses a method for predicting power grid defect materials, which comprises the steps of starting a plurality of threads based on the core number of a central processing unit, and carrying out parallel training on each base model by utilizing each thread; predicting the power grid defect materials by using the trained base model respectively to obtain corresponding prediction results; taking the inverse of the root mean square error of the base model as the weight of the base model; weighting and fusing the weight of the base model and the prediction result to finish the prediction of the power grid defect materials; the method improves the speed of training the base model and improves the prediction speed in a multithreading mode; meanwhile, the prediction precision is improved through weighting and fusion of multiple models.
Description
Technical Field
The application relates to the technical field of performance optimization and material demand prediction, in particular to a power grid defect material prediction method.
Background
The stable and healthy operation of the power grid system is very important for people to produce and live; but the grid system is too bulky and the equipment cannot operate intact at all times. Extreme weather, emergency conditions, equipment aging, etc. can cause grid faults.
For equipment materials of the power grid, there are three main types: daily equipment supplies, emergency equipment supplies and major disaster defect supplies. The application mainly aims at emergency equipment materials. When the equipment fails, the equipment needs to be replaced in all places so as to ensure the normal operation of the power grid; however, the warehouse in each place should purchase the materials, so that the materials are not lost, and the warehouse cannot be excessively stored, thus the problem worthy of research is solved.
At present, most researches on prediction of defective equipment are focused on improving the prediction precision of a certain type of materials, but in a power grid system, different areas and different material data are distributed differently, and the data distribution is not very regular. Therefore, to accurately predict the grid layering defect materials, multiple models need to be fused. The traditional fusion method is average weighted fusion or Stacking fusion. However, both of these methods have some drawbacks, and the average weighted fusion method is too simple and has problems in terms of accuracy. The Stacking fusion method is relatively complex, depends on the fusion model of the second stage, and is required to be higher and not necessarily higher in precision.
Meanwhile, when the models are fused, the models are trained in series one by one in the traditional way, and then the models are selected and fused, but the efficiency is not high; as the amount of data grows, the serial prediction and fusion continues, reducing efficiency.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems occurring in the prior art.
Therefore, the application provides a power grid defect material prediction method, which can solve the problem of poor material prediction effect of emergency equipment.
In order to solve the technical problems, the application provides the following technical scheme: starting a plurality of threads based on the core number of a central processing unit, and performing parallel training on each base model by utilizing each thread; predicting the power grid defect materials by using the trained base model respectively to obtain corresponding prediction results; taking the inverse of the root mean square error of the base model as the weight of the base model; and carrying out weighted fusion on the prediction result based on the weight of the base model to complete the prediction of the power grid defect materials.
As a preferable scheme of the power grid defect material prediction method of the application, the method comprises the following steps: opening the plurality of threads comprises defining an implementation class of a Runneable interface and rewriting a run method of the Runneable interface; creating an instance of a Runneable implementation class; calling the parametrized construction of the Thread class, transferring the instance as a parameter, and creating an object of the Thread class; the start method of the Thread class object is called.
As a preferable scheme of the power grid defect material prediction method of the application, the method comprises the following steps: the base model comprises a ridge regression model, a lasso regression model, an elastic network regression model, a gradient lifting tree, an extreme gradient lifting model and a negative feedback neural network model.
As a preferable scheme of the power grid defect material prediction method of the application, the method comprises the following steps: the step of parallel training of the base model comprises the steps of solving an optimization target according to a target formula to complete training of the base model; the target formula is as follows:
wherein ,nsamples For the number of samples, w is the weight coefficient of the vector in each dimension of the sample, X is sample data, y is the amount of material defects, alpha and beta are regular coefficients, and W is 1 、||w|| 2 Respectively, the first-order norm and the second-order norm of the coefficient.
As a preferable scheme of the power grid defect material prediction method of the application, the method comprises the following steps: the prediction result comprises the steps of predicting the power grid defect materials by utilizing the base model, and respectively obtaining prediction valuesThe predicted valueThe method comprises the following steps:
wherein ,the method is characterized in that the method comprises the steps of predicting the power grid defect materials of a ridge regression model, a lasso regression model and an elastic network regression model, wherein x is the characteristic data of the input power grid materials, θ is a weight parameter vector, and T is a transposed symbol.
As a preferable scheme of the power grid defect material prediction method of the application, the method comprises the following steps: the predicted valueComprising the steps of (a) a step of,
wherein ,predicting values of defect materials for gradient lifting tree and extreme gradient lifting model, f k For the kth classification regression tree, K is the scoreThe number of class regression trees Γ is the space in which the regression trees are classified.
As a preferable scheme of the power grid defect material prediction method of the application, the method comprises the following steps: the predicted valueComprising the steps of (a) a step of,
wherein ,is the predicted value, w, of the defect material of the negative feedback neural network model 1 Is a parameter of the first layer, sigma is an activation function, w 2 Is a weight parameter of the second layer.
As a preferable scheme of the power grid defect material prediction method of the application, the method comprises the following steps: the fitted base model is filtered out according to the threshold of root mean square error.
The application has the beneficial effects that: the method improves the speed of training the base model and improves the prediction speed in a multithreading mode; meanwhile, the prediction precision is improved through weighting and fusion of multiple models.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a flow chart of a method for predicting a defective material of a power grid according to a first embodiment of the present application;
FIG. 2 is a schematic diagram showing a time distribution of grid defect materials according to a method for predicting grid defect materials according to a first embodiment of the present application;
FIG. 3 is a schematic diagram showing the comparison of prediction accuracy of a method for predicting defective materials of a power grid according to a second embodiment of the present application;
fig. 4 is a schematic diagram of parallel and serial time comparison of a method for predicting grid defect materials according to a second embodiment of the present application.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present application have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1 to 2, a first embodiment of the present application provides a method for predicting a grid defect material, including:
s1: and starting a plurality of threads based on the core number of the central processing unit, and performing parallel training on each base model by utilizing each thread.
The steps of starting the plurality of threads are as follows:
(1) Defining an implementation class of a Runneable interface, and rewriting a run () method of the Runneable interface;
(2) Creating an instance of a Runneable implementation class;
(3) Calling the parametrized structure of the Thread class, transferring the instance as a parameter, and creating an object of the Thread class;
(4) The start () method of the Thread class object is called.
It should be noted that, the Runnable interface is a thread auxiliary class, only defines a run () method, and the start () method can cause the thread to start executing; the java virtual machine will call the run () method of the thread.
An example procedure for opening multiple threads is as follows:
further, each base model is trained in parallel with each thread, wherein the base model has a kaolinite regression model, a lasso regression model, an elastic network regression model, a gradient lifting tree, an extreme gradient lifting model, and a negative feedback neural network model.
Specifically, solving an optimization target according to a target formula to complete training of a base model;
the target formula is as follows:
wherein ,nsamples For the number of samples, w is the weight coefficient of the vector in each dimension of the sample, X is sample data, y is the amount of material defects, alpha and beta are regular coefficients, and W is 1 、||w|| 2 Respectively, the first-order norm and the second-order norm of the coefficient.
Preferably, the present embodiment improves the speed and predictive speed of training the base model by employing multiple threads.
S2: and predicting the power grid defect materials by using the trained base model respectively to obtain corresponding prediction results.
Predicting power grid defect materials by using a base model to respectively obtain predicted valuesThe electric network defect materials comprise a hardware fitting body, a stay wire body, a concrete pole, a porcelain insulator, a CPU plug-in unit, an overhead conductor, an opening and closing contactor, a charging module, a composite insulator and the like, and the distribution situation of the electric network defect materials is shown in figure 2.
Predictive valueThe method comprises the following steps:
wherein ,the method is characterized in that the method comprises the steps of predicting the power grid defect material values (the units are pieces, the values are positive real numbers) of a ridge regression model, a lasso regression model and an elastic network regression model, x is input characteristic data (the operation date, a manufacturer, the defect occurrence date, the weather temperature, the humidity, whether snowy weather is caused when the defect occurs, and the like) of the power grid material, θ is a weight parameter vector, and T is a transposed symbol.
Predictive valueIn order to achieve this, the first and second,
wherein ,predicting values of defect materials for gradient lifting tree and extreme gradient lifting model, f k For the kth classification regression tree, K is the number of classification regression trees, Γ is the space of the classification regression tree.
Predictive valueIn order to achieve this, the first and second,
wherein ,is the predicted value, w, of the defect material of the negative feedback neural network model 1 Is a parameter of the first layer, sigma is an activation function, w 2 Is a weight parameter of the second layer.
S3: and taking the inverse of the root mean square error of the base model as the weight of the base model, and filtering the fitted base model according to the threshold value of the root mean square error.
It should be noted that, root Mean Square Error (RMSE) is a typical index of the regression model, and is used to indicate how much error the model will generate in prediction, and its calculation formula is as follows:
where n is the number of samples, y i As a result of the fact that the value,is a predicted value.
The root mean square error threshold was set to 0.01.
S4: and carrying out weighted fusion on the prediction result based on the weight of the base model to complete the prediction of the power grid defect materials.
In the embodiment, weighted summation is carried out based on the inverse of the RMSE, and the fitted model is filtered according to the threshold (0.001) of the RMSE, so that the prediction precision is further improved; meanwhile, the multi-thread method is utilized, the core of the CPU is utilized to the maximum extent, and the calculation speed is improved.
Example 2
In order to verify and explain the technical effects adopted in the method, the embodiment selects the traditional technical scheme and adopts the method to carry out comparison test, and the test results are compared by means of scientific demonstration so as to verify the true effects of the method.
The traditional technical scheme is as follows: performing defect material prediction by adopting a single model, for example, performing defect material prediction by using only a base learner; considering the actual situation of the problem, the aim is to realize accurate prediction of various defective materials in each county, and then a single model cannot necessarily realize the aim; because the meteorological data of each region has differences, the damage degree and the space-time distribution of different materials also have differences.
The traditional technical scheme II is as follows: because of the problems existing in the conventional technical scheme, improvement is needed, and the most common method is to perform multi-model fusion to improve the prediction accuracy; taking the model as a base learner, and then fusing the results of the base learner; the most common fusion mode is average weighted fusion and Stacking fusion; the disadvantage of average weighted fusion is that it is too simple and rough, the disadvantage of Stacking fusion is relatively complex, the accuracy depends on the fusion model of the second stage, and fusion is performed serially during fusion.
In order to verify that the method has simplicity, high prediction accuracy and high prediction speed compared with the traditional technical scheme, in the embodiment, a single model prediction method in the first traditional technical scheme is adopted, and the method of average weighted fusion and Stacking in the second traditional scheme is compared; and carrying out predictive evaluation by utilizing the real power grid defect material data and combining with meteorological data.
In the embodiment, the three schemes are compared and analyzed by utilizing the real power grid defect material data and the meteorological data and utilizing Python to write codes; in the prediction process, a prediction is performed on each type of data of each county (Wudang, yun Yan, pedigree, nanng, shuanglong, urban north, xiaohe, kaiyang, toddalian, qing town, baiyun, huaxi, jinyang and Longli), and then the mean square error (RMSE) of the real data and the prediction result is calculated.
The results are shown in fig. 3 and 4; FIG. 3 is a summary RMSE comparison of the prediction results of each county after the fusion method based on the RMSE weighting, which shows that the prediction accuracy of the method is higher than that of the average weighted fusion method and the method of Stacking; FIG. 4 is a comparison of performance, and it can be seen that the present method predicts much faster than single-threaded training (single-model prediction) and the outcome of the prediction by using multiple threads; under the existing data condition, the single-thread method needs 97s, and the method only needs 90s; and it is expected that the more models, the higher the acceleration achieved by the multi-threading approach; the more cores of the CPU, the better the speed-up ratio can be obtained; the larger the data volume, the more obvious the advantage of multithreading can be realized.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Claims (5)
1. A power grid defect material prediction method is characterized in that: comprising the steps of (a) a step of,
starting a plurality of threads based on the core number of the central processing unit, and performing parallel training on each base model by utilizing each thread;
predicting the power grid defect materials by using the trained base model respectively to obtain corresponding prediction results;
taking the inverse of the root mean square error of the base model as the weight of the base model;
filtering the fitted base model according to a threshold value of the root mean square error;
opening the plurality of threads, including: defining the implementation class of the Runneable interface, and rewriting a run method of the Runneable interface; creating an instance of a Runneable implementation class; calling the parametrized construction of the Thread class, transferring the instance as a parameter, and creating an object of the Thread class; calling a start method of the Thread class object;
the base model comprises a ridge regression model, a lasso regression model, an elastic network regression model, a gradient lifting tree, an extreme gradient lifting model and a negative feedback neural network model;
and carrying out weighted fusion on the prediction result based on the weight of the base model to complete the prediction of the power grid defect materials.
2. The grid defect material prediction method of claim 1, wherein: training the base model in parallel includes,
solving an optimization target according to a target formula to finish training of the base model;
the target formula is as follows:
wherein ,nsamples For the number of samples, w is the weight coefficient of the vector in each dimension of the sample, X is sample data, y is the amount of material defects, alpha and beta are regular coefficients, and W is 1 、||w|| 2 Respectively, the first-order norm and the second-order norm of the coefficient.
3. The grid defect material prediction method of claim 2, wherein: the prediction result includes the result of the prediction,
predicting the power grid defect materials by using the base model to respectively obtain predicted values
The predicted valueThe method comprises the following steps:
wherein ,the method is characterized in that the method comprises the steps of predicting the power grid defect materials of a ridge regression model, a lasso regression model and an elastic network regression model, wherein x is the characteristic data of the input power grid materials, θ is a weight parameter vector, and T is a transposed symbol.
4. A method of predicting a grid defect as set forth in claim 3, wherein: the predicted valueComprising the steps of (a) a step of,
wherein ,predicting values of defect materials for gradient lifting tree and extreme gradient lifting model, f k For the kth classification regression tree, K is the number of classification regression trees, Γ is the space of the classification regression tree.
5. A method of predicting a grid defect as set forth in claim 3, wherein: the predicted valueComprising the steps of (a) a step of,
wherein ,is the predicted value, w, of the defect material of the negative feedback neural network model 1 Is a parameter of the first layer, sigma is an activation function, w 2 Is a weight parameter of the second layer.
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