Power grid construction business added value and speed increase prediction method and system

Abstract

The invention provides a power grid construction service added value and accelerated speed prediction method and system, and belongs to the technical field of industrial added value and accelerated speed prediction. Comprising the steps that sample data are acquired, the sample data comprise annual parameter data and an annual power grid construction business added value, and the annual parameter data comprise annual economic data and annual power data; preprocessing each piece of sample data, and inputting into a sample set; according to the sample set, on the basis of a Stacking algorithm, combined with a linear regression algorithm, a decision tree algorithm, a support vector machine, a k-nearest neighbor algorithm, a random forest algorithm, an AdaBoost algorithm, a gradient regression algorithm and a time sequence analysis algorithm, power grid construction business added value and speed increase prediction modeling is carried out, and a prediction model is obtained; using the prediction model to carry out power grid construction business added value and speed increase prediction; the predictive model is evaluated using volatility and accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of industrial value-added growth rate prediction, and in particular to a method and system for predicting the growth rate of value-added of power grid construction business. Background Art

[0002] At present, there are relatively few studies on industrial value-added forecasting, and they are mainly based on a number of selected traditional statistical indicators, such as using the total amount of social consumer snacks and broad money data to forecast industrial value-added, and forecasting industrial value-added based on the constructed private enterprise credit spread index. As the macroeconomic transmission mechanism becomes more complex and the factors affecting industrial value-added become more diverse, the appropriate forecasting model should contain a large number of explanatory variables to make full use of valuable information. It is difficult to obtain accurate forecasts using only a few variables.

[0003] In addition, the lag of traditional statistical data makes it difficult to use current information on economic activities when making predictions, while power big data is closely related to economic activities and is available in real time, which can provide current information on economic activities to make up for the shortcomings of traditional statistical data. Therefore, the comprehensive use of power data and traditional statistical data may result in more accurate industrial added value forecasts.

[0004] The power industry is a basic energy industry of the national economy and plays a vital supporting role in the development of other industrial sectors. In the context of the rapid transformation of the global energy structure to a low-carbon form, intermittent renewable energy such as wind power and photovoltaics have become the main force of new installed capacity. In order to meet the increased industrial electricity demand and low-carbon demand, the power sector is carrying out the construction of a new power system dominated by new energy. Due to the large demand for decentralized renewable energy access and the faster changes in demand for power grid construction than in the past, the existing prediction methods are not suitable for the needs of new power system power grid construction. Summary of the invention

[0005] In view of this, the present application provides a method and system for predicting the growth rate of added value of power grid construction business, which improves the load forecasting accuracy through a combined forecasting method, thereby providing a reference basis for the construction of new power grid systems.

[0006] A method for predicting the growth rate of added value of power grid construction business, comprising:

[0007] Step S1, obtaining sample data, wherein the sample data includes annual parameter data and annual power grid construction business added value, wherein the annual parameter data includes annual economic data and annual power data;

[0008] Step S2, pre-processing each of the sample data and inputting them into a sample set;

[0009] Step S3, based on the sample set, based on the Stacking algorithm, combined with the linear regression algorithm, decision tree algorithm, support vector machine, k nearest neighbor algorithm, random forest algorithm, AdaBoost algorithm, gradient regression algorithm and time series analysis algorithm, perform forecast modeling of the growth rate of added value of power grid construction business, and obtain a forecast model;

[0010] Step S4, using the prediction model to predict the growth rate of added value of power grid construction business;

[0011] Step S5, evaluating the prediction model using volatility and accuracy.

[0012] A system for predicting the growth rate of added value of power grid construction business, comprising:

[0013] An acquisition module, used to acquire sample data, wherein the sample data includes annual parameter data and annual grid construction business added value, wherein the annual parameter data includes annual economic data and annual power data;

[0014] A preprocessing module, used for preprocessing each of the sample data and inputting the preprocessing data into a sample set;

[0015] A model building module is used to perform a prediction model for the growth rate of added value of power grid construction business based on the sample set, based on the Stacking algorithm, combined with a linear regression algorithm, a decision tree algorithm, a support vector machine, a k-nearest neighbor algorithm, a random forest algorithm, an AdaBoost algorithm, a gradient regression algorithm and a time series analysis algorithm, and obtain a prediction model;

[0016] A prediction module, used to predict the growth rate of added value of power grid construction business using the prediction model;

[0017] An evaluation module is used to evaluate the forecasting model using volatility and accuracy.

[0018] It can be seen from the above technical scheme that the method and system for predicting the growth rate of added value of power grid construction business provided by the embodiment of the present application first obtain sample data, wherein the sample data includes annual parameter data and annual added value of power grid construction business, and the annual parameter data includes annual economic data and annual power data; pre-process each sample data and input it into the sample set; according to the sample set, based on the Stacking algorithm, combined with the linear regression algorithm, decision tree algorithm, support vector machine, k nearest neighbor algorithm, random forest algorithm, AdaBoost algorithm, gradient regression algorithm and time series analysis algorithm, the growth rate prediction model of the added value of power grid construction business is carried out to obtain the prediction model; the growth rate of added value of power grid construction business is predicted by using the prediction model; and the prediction model is evaluated by using volatility and accuracy. By combining the prediction method, the prediction is completed, the sensitivity of a single algorithm is reduced, and the load prediction accuracy is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flow chart of the method for predicting the growth rate of added value of power grid construction business.

[0020] Figure 2 This is the system framework diagram.

[0021] Figure 3 It is a block diagram of an electronic device used to implement the method for predicting the growth rate of added value of power grid construction business of this embodiment. DETAILED DESCRIPTION

[0022] The technical solution and technical effects of the present application are further elaborated in detail below in conjunction with the drawings of the present application.

[0023] The purpose of this application is to provide a method for predicting the growth rate of added value of power grid construction business. By collecting economic data and power data and cleaning the data, a first-layer model is constructed using a linear regression algorithm, a decision tree algorithm, a support vector machine, a k-nearest neighbor algorithm, a random forest algorithm, an AdaBoost algorithm, a gradient regression algorithm, and a time series analysis algorithm. The model is trained and the prediction results are tested. Then, combined with the prediction accuracy of the algorithm, an improved stacking ensemble learning algorithm is used to construct a comprehensive prediction model.

[0024] like Figure 1 As shown, the method for predicting the growth rate of added value of power grid construction business provided in this application includes the following steps:

[0025] Step S1, obtaining sample data, the sample data including annual parameter data and annual grid construction business added value, the annual parameter data including annual economic data and annual power data, wherein the annual power data includes conventional compound growth, thermal power installed capacity, renewable energy installed capacity, energy storage and other data, wherein renewable energy refers to wind energy, solar energy, hydropower and the like;

[0026] Step S2, preprocessing each sample data and inputting it into the sample set;

[0027] Step S3, based on the sample set, based on the Stacking algorithm, combined with the linear regression algorithm, decision tree algorithm, support vector machine, k nearest neighbor algorithm, random forest algorithm, AdaBoost algorithm, gradient regression algorithm and time series analysis algorithm, perform forecast modeling of the growth rate of added value of power grid construction business, and obtain a forecast model;

[0028] Step S4, using the prediction model to predict the growth rate of added value of power grid construction business;

[0029] Step S5, evaluating the prediction model using volatility and accuracy.

[0030] In the embodiment of the present application, the sample data is recorded as (x i ,yi ), where x i is the annual parameter data, y i is the actual value added of the annual power grid construction business, i is the year, and the characteristic vector x i =[x iA ,x iB ,...,x iG ], x iA ,x iB ,...,x iG They are annual electricity data A, annual resident income and consumption data B, annual social and economic data C, annual basic information of industrial development zones D, annual industrial output value E, annual industrial product price index F, and annual average price of industrial raw materials G; sample data is the basis for building machine learning models, and each sample contains the data required for model training. When collecting samples, in order to meet the model's evaluation of relevance and multiple dimensions, it is not enough to only collect the data to be evaluated, and as much relevant data as possible should also be collected.

[0031] Step S2 preprocesses the data. Since the data are not all structured data, and some data may be missing or delayed, the data needs to be processed. The processing methods are mainly to delete duplicate values ​​and the associated filling method; duplicate or invalid data in the sample can be eliminated; for incomplete, erroneous or inconsistent data, because the required data are basically structured data, approximate data that is highly correlated with the target data can be found, and the data correlation can be used to complete the filling. For example, the growth rates of residents' income and regional GDP are usually consistent. When the residents' income data is missing, the residents' income data can be filled according to the growth of regional GDP data, and the two can complement each other. In addition, the time of the data needs to be consistent. Because the required data are highly correlated with time, all data are sorted according to the same time dimension.

[0032] The prediction model includes a primary learning model and a secondary learning model.

[0033] In the above step S3, economic data, power generation, power consumption, energy type, etc. are used as independent variables, and the added value of power grid construction business is used as the dependent variable, and the added value model of power grid construction business is established one by one by using linear regression algorithm, decision tree algorithm, support vector machine, k nearest neighbor algorithm, random forest algorithm, AdaBoost algorithm, gradient regression algorithm and time series analysis algorithm. The specific process of establishing the prediction model includes:

[0034] Step S31, dividing the sample set into a training set and a test set, wherein the test set is the sample data of this year, and the training set is the sample data of other years;

[0035] Step S32, establish a primary learning model SX The output of each primary learning model is used as the input of the secondary learning model. The primary learning model includes the linear regression algorithm model S 1 , Decision Tree Algorithm Model S 2 , support vector machine model S 3 , k nearest neighbor algorithm model S 4 , Random Forest Algorithm Model S 5 、AdaBoost algorithm model S 6 , Gradient regression algorithm model S 7 and time series analysis algorithm model S 8 , each primary learning model is trained using the training set and tested using the test set;

[0036] Step S33, establish a secondary learning model, which is used to assign weight configurations to the output results of the primary learning model and set the output results of the secondary learning model through the R2 method and genetic algorithm. The output results of the secondary learning model are the output results of the prediction model.

[0037] The improved stacking algorithm is used to fuse multiple machine learning models to improve the overall prediction ability. The traditional stacking algorithm is usually designed in two layers. The first layer is composed of multiple sub-algorithms, and the second layer has only one meta-model. The predicted values ​​and true values ​​obtained by various algorithms in the first layer are trained and assigned corresponding weights to obtain the final result. However, the shortcomings of the stacking algorithm are also prominent, and it is easy to cause overfitting problems. The improved stacking algorithm avoids the problem of overfitting by limiting the maximum value of the weight of the first-layer sub-algorithm. Therefore, the purpose of establishing a secondary learning model in step S33 is to perform algorithm integration, specifically:

[0038] Step S331, with R X The minimum is the goal, and the genetic algorithm is used to find the appropriate individual. The objective function f(x) of the genetic algorithm is:

[0039] f(x)=min(R 1 +R 2 ...+R 8 )

[0040]

[0041]

[0042] Among them, R X It is the primary learning model S X The correction coefficient of the output result, x∈[1,8], n is the element x in the training set iIn this application, the data of the past three years are used as the training set, and there are 12 sets of monthly data each year. Therefore, if the data granularity is monthly, then n = 12*3 = 36, and if the data granularity is annual, then n = 3, R 2 is the linear regression determination coefficient, p is the number of variables. There are 7 variables in this application, namely annual electricity data A, annual resident income and consumption data B, annual social and economic data C, annual industrial development zone basic information D, annual industrial output value E, annual industrial product price index F, and annual average price of industrial raw materials G, so p = 7 in this application. To pass the primary learning model S X Calculate the feature vector x in the training set i The annual forecast of added value of power grid construction business is obtained. Based on the training set, the primary learning model S X Calculate all The mean of

[0043] Step S332, according to the R in f(x) obtained by the genetic algorithm X Calculate each primary learning model S X The maximum weight value Q X :

[0044]

[0045] Step S333, the output result f(y) of the secondary learning model is defined as:

[0046]

[0047] The calculation process of step S331 includes:

[0048] Step S331a, population initialization, read in the original data, and convert (x i ,y i ) and each primary learning model S X Output of the forecasted value added of power grid construction business Transformed into population P, multiple unevolved chromosomes j are set in the genetic population P i , j i By primary learning model S X Output of the forecasted value added of power grid construction business and a set of actual values ​​(x i ,y i ), set the population size N, the maximum genetic generation N max and mutation rate a. Here, the population size can be set to 40, the maximum genetic generation to 50, and the mutation rate to 0.1;

[0049] Step S331b, calculate the gene fitness, using the R X The minimum sum is the objective function, so we need a process to transform the minimum objective function into the maximum fitness function, and ensure that it is non-negative. The fitness function Z(x) is the inverse of the objective function:

[0050]

[0051] in,

[0052]

[0053] Step S331c, genetic selection, for each unevolved chromosome j i Calculate and bring the result into the fitness function Z(x) to get the corresponding fitness value; loop n times to get n fitness values, sort the n fitness values ​​from large to small, replace the last 1 / 3 with the first 1 / 3, and re-form n daughter chromosomes; thus, traverse any two genes in any two chromosomes in the genetic population P to perform gene crossover to obtain evolved daughter chromosomes, and the daughter chromosomes constitute the daughter genetic population P; repeat the genetic selection process until the number of daughter chromosomes in the daughter genetic population P' is the same as the number of unevolved chromosomes in the daughter genetic population P;

[0054] Step S331d, stop the evolution, when the fitness of the offspring chromosome in the offspring genetic population P' is greater than or equal to the recombined offspring chromosome or high fitness chromosome, or the current evolution number reaches the maximum number of iterations N max When , the optimization stops. At this time, f(x) reaches the minimum. According to the minimum f(x), each primary learning model S is calculated. X The maximum weight value Q X .

[0055] Step S5 evaluates the output of the prediction model using both accuracy and volatility, where:

[0056] Accuracy = accurate number / total number

[0057] Volatility = ∑(predicted result - actual result) 2

[0058] The sample set is divided into a training set and a validation set. The training set is used to train the model, and the validation set is used to verify the accuracy of the model. In the validation set, the error range between the predicted results and the actual results is within 5%, which can be considered accurate, and the rest is inaccurate.

[0059] After testing, the combined forecasting model has a higher accuracy rate than most single model predictions, and the volatility of the results is minimal, so it can be used as a method to predict the added value of power grid construction business.

[0060] In this application, the combined prediction method combines different algorithms by weighting to jointly complete the prediction, reduce the sensitivity of a single algorithm, and thus improve the load prediction accuracy.

[0061] This application also provides a power grid construction business value-added growth rate prediction system for implementing Figure 1 The method shown, the system comprises:

[0062] The acquisition module is used to obtain sample data, which includes annual parameter data and annual grid construction business added value. The annual parameter data includes annual economic data and annual power data. Among them, the annual power data includes conventional compound growth, thermal power installed capacity, renewable energy installed capacity, energy storage and other data;

[0063] A preprocessing module is used to preprocess each sample data and input it into the sample set;

[0064] The model building module is used to predict the growth rate of the added value of power grid construction business based on the sample set, based on the Stacking algorithm, combined with the linear regression algorithm, decision tree algorithm, support vector machine, k nearest neighbor algorithm, random forest algorithm, AdaBoost algorithm, gradient regression algorithm and time series analysis algorithm, and obtain the prediction model;

[0065] The prediction module is used to predict the growth rate of added value of power grid construction business using the prediction model;

[0066] Evaluation module for evaluating forecasting models using volatility and accuracy.

[0067] Among them, the sample data is recorded as (x i ,y i ), where x i is the annual parameter data, y i is the actual value added of the annual power grid construction business, i is the year, and the characteristic vector x i =[x iA ,x iB ,...,x iG ], x iA ,x iB ,...,x iG They are annual electricity data A, annual resident income and consumption data B, annual social and economic data C, annual basic information of industrial development zones D, annual industrial output value E, annual industrial product price index F, and annual average price of industrial raw materials G. The prediction model includes primary learning model and secondary learning model.

[0068] The model building module includes:

[0069] The sample set processing unit divides the sample set into a training set and a test set, wherein the test set is the sample data of this year and the training set is the sample data of other years;

[0070] Establish units and build primary learning models X The output of each primary learning model is used as the input of the secondary learning model. The primary learning model includes the linear regression algorithm model S 1 , Decision Tree Algorithm Model S 2 , support vector machine model S 3 , k nearest neighbor algorithm model S 4 , Random Forest Algorithm Model S 5 、AdaBoost algorithm model S 6 , Gradient regression algorithm model S 7 and time series analysis algorithm model S 8 , each primary learning model is trained using the training set and tested using the test set;

[0071] A unit is established to establish a secondary learning model. The secondary learning model is used to assign weight configurations to the output results of the primary learning model through the R2 method and genetic algorithm, and to set the output results of the secondary learning model. The output results of the secondary learning model are the output results of the prediction model.

[0072] For further information, please also see Figure 3 , the electronic device 300 is used to implement Figure 1 The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0073] like Figure 3 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0074] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0075] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as the pruning method of the machine learning model.

[0076] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0077] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0078] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

Claims

1. A method for predicting the growth rate of added value of power grid construction business, It is characterized in that include: Step S1, obtaining sample data, the sample data including annual parameter data and annual power grid construction business added value, the annual parameter data including annual economic data and annual power data; Step S2, preprocessing each sample data and inputting it into the sample set; Step S3, based on the sample set, based on the Stacking algorithm, combined with the linear regression algorithm, decision tree algorithm, support vector machine, k nearest neighbor algorithm, random forest algorithm, AdaBoost algorithm, gradient regression algorithm and time series analysis algorithm, perform forecast modeling of the growth rate of added value of power grid construction business, and obtain a forecast model; Step S4, using the prediction model to predict the growth rate of added value of power grid construction business; Step S5, evaluating the prediction model using volatility and accuracy.

2. The method for predicting the growth rate of added value of power grid construction business according to claim 1, It is characterized in that The sample data is recorded as (x i ,y i ), where x i is the annual parameter data, y i is the actual value added of the annual power grid construction business, i is the year, and the characteristic vector x i =[x iA ,x iB ,...,x iG ], x iA ,x iB ,...,x iG They are annual electricity data A, annual residents' income and consumption data B, annual socio-economic data C, annual basic information of industrial development zones D, annual industrial output value E, annual industrial product price index F, and annual average price of industrial raw materials G.

3. The method for predicting the growth rate of added value of power grid construction business as claimed in claim 2, It is characterized in that The prediction model includes a primary learning model and a secondary learning model, and step S3 includes: Step S31, dividing the sample set into a training set and a test set, wherein the test set is the sample data of this year, and the training set is the sample data of other years; Step S32, establishing the primary learning model S X , and use the output of each primary learning model as the input of the secondary learning model, wherein the primary learning model includes a linear regression algorithm model S 1 , Decision Tree Algorithm Model S 2 , support vector machine model S 3 , k nearest neighbor algorithm model S 4 , Random Forest Algorithm Model S 5 、AdaBoost algorithm model S 6 , Gradient regression algorithm model S 7 and time series analysis algorithm model S 8 , each of the primary learning models is trained by the training set and tested by the test set; Step S33, establish the secondary learning model, which is used to assign weight configuration to the output result of the primary learning model and set the output result of the secondary learning model through the R2 method and genetic algorithm. The output result of the secondary learning model is the output result of the prediction model.

4. The method for predicting the growth rate of added value of power grid construction business as claimed in claim 3, It is characterized in that The step S33 comprises: Step S331, with R X The minimum is the goal, and the genetic algorithm is used to find the appropriate individual. The objective function f(x) of the genetic algorithm is: f(x)=min(R 1 +R 2 ...+R 8 ) Among them, R X It is the primary learning model S X The correction coefficient of the output result, x∈[1,8], n is the element x in the training set i Number, R 2 is the linear regression coefficient of determination, p is the number of variables, To pass the primary learning model S X Calculate the feature vector x in the training set i The annual forecast of added value of power grid construction business is obtained. Based on the training set, the model S is learned after the initial stage X Calculate all the above The mean of Step S332, according to the R in f(x) obtained by the genetic algorithm X Calculate each of the primary learning models S X The maximum weight value Q X : Step S333, the output result f(y) of the secondary learning model is defined as:

5. The method for predicting the growth rate of added value of power grid construction business as claimed in claim 4, It is characterized in that The calculation process of step S331 includes: Step S331a, population initialization, read in the original data, and convert (x i ,y i ) and each primary learning model S X Output of the forecasted value added of power grid construction business Transformed into population P, multiple unevolved chromosomes j are set in the genetic population P i , j i By primary learning model S X Output of the forecasted value added of power grid construction business and a set of actual values ​​(x i ,y i ), set the population size N, the maximum genetic generation N max and mutation rate a; Step S331b, calculate the gene fitness, using the R X The minimum sum is the objective function, and the fitness function Z(x) is the inverse of the objective function: in: Step S331c, genetic selection, for each of the non-evolved chromosomes j i Perform calculations, and bring the results into the fitness function Z(x) to obtain the corresponding fitness value; repeat n times to obtain n fitness values, sort the obtained n fitness values ​​from large to small, and replace the last 1 / 3 with the first 1 / 3 to re-form n daughter chromosomes; thereby, traverse any two genes in any two chromosomes in the genetic population P to perform gene crossover to obtain evolved daughter chromosomes, and the daughter chromosomes constitute the daughter genetic population P; repeat the genetic selection process until the number of daughter chromosomes in the daughter genetic population P' is the same as the number of unevolved chromosomes in the daughter genetic population P; Step S331d, stop the evolution, when the fitness of the offspring chromosome in the offspring genetic population P' is greater than or equal to the recombined offspring chromosome or high fitness chromosome, or the current evolution number reaches the maximum number of iterations N max When , the optimization is stopped. At this time, f(x) reaches the minimum. The primary learning models S are calculated according to the minimum f(x). X The maximum weight value Q X .

6. A system for predicting the growth rate of added value of power grid construction business. It is characterized in that include: An acquisition module is used to acquire sample data, the sample data includes annual parameter data and annual power grid construction business added value, the annual parameter data includes annual economic data and annual power data; A preprocessing module, used for preprocessing each of the sample data and inputting the preprocessing data into a sample set; A model building module is used to perform industrial added value growth rate forecasting modeling based on the sample set, based on the Stacking algorithm, combined with the linear regression algorithm, decision tree algorithm, support vector machine, k nearest neighbor algorithm, random forest algorithm, AdaBoost algorithm, gradient regression algorithm and time series analysis algorithm to obtain a forecasting model; A prediction module, used to predict the growth rate of industrial added value using the prediction model; An evaluation module is used to evaluate the forecasting model using volatility and accuracy.

7. The power grid construction business added value growth rate prediction system according to claim 6, It is characterized in that The sample data is recorded as (x i ,y i ), where x i is the annual parameter data, y i is the actual value added of the annual power grid construction business, i is the year, and the characteristic vector x i =[x iA ,x iB ,...,x iG ], x iA ,x iB ,...,x iG They are annual electricity data A, annual residents' income and consumption data B, annual socio-economic data C, annual basic information of industrial development zones D, annual industrial output value E, annual industrial product price index F, and annual average price of industrial raw materials G.

8. The power grid construction business added value growth rate prediction system according to claim 7, It is characterized in that The prediction model includes a primary learning model and a secondary learning model, and the model building module includes: A sample set processing unit, which divides the sample set into a training set and a test set, wherein the test set is the sample data of this year, and the training set is the sample data of other years; Establishing unit, establishing the primary learning model S X , and use the output of each primary learning model as the input of the secondary learning model, wherein the primary learning model includes a linear regression algorithm model S 1 , Decision Tree Algorithm Model S 2 , support vector machine model S 3 , k nearest neighbor algorithm model S 4 , Random Forest Algorithm Model S 5 、AdaBoost algorithm model S 6 , Gradient regression algorithm model S 7 and time series analysis algorithm model S 8 , each of the primary learning models is trained by the training set and tested by the test set; The establishing unit establishes the secondary learning model, and the secondary learning model is used to assign weight configuration to the output result of the primary learning model through the R2 method and genetic algorithm, and to set the output result of the secondary learning model. The output result of the secondary learning model is the output result of the prediction model.

Images