CN110807490A - Intelligent prediction method for construction cost of power transmission line based on single-base tower - Google Patents
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Abstract
The application provides a single-base-tower-based intelligent prediction method for construction cost of power transmission line engineering, which comprises the following steps: an empirical parameter prediction model is constructed by combining empirical parameters of a least square support vector machine model; optimizing parameters in the prediction model by utilizing a particle swarm optimization algorithm to obtain a particle swarm optimization least square support vector machine prediction model; and inputting the original data serving as an input variable into the optimized vector machine prediction model, and training the vector machine prediction model to obtain the cost prediction value of the single-base tower. According to the method, the index is subjected to dimension reduction by using a principal component analysis method, the particle swarm algorithm is innovatively introduced to perform parameter optimization on the least square support vector machine model to obtain the optimal parameter, the obtained principal component data are respectively introduced into the empirical parameter prediction model and the optimized parameter prediction model to be trained and predicted, the accuracy of cost prediction can be improved, and the refinement level of cost management is improved.
Description
Technical Field
The application relates to the technical field of electric power engineering construction, in particular to an intelligent prediction method for construction cost of a power transmission line based on a single-base tower.
Background
In recent years, with the continuous expansion of the investment scale and the construction scale of power grid construction projects in China, the strengthening of power grid project cost management and the realization of lean management of power grid investment become important research subjects in the development process of power grid enterprises. The method has important significance for improving the cost control levels of power grid construction projects, such as the ground estimation, the initial approximate calculation, the construction drawing budget, the completion settlement and the like, improving the cost management of the whole power grid construction process and improving the lean management level of the power grid construction investment by effectively calculating the actual cost of each base tower and constructing the single-base-tower-mode pricing frame system and the pricing method.
The profit mode of the national power grid company is thoroughly changed by a new cycle of power system reform taking power transmission and distribution price reformation as a core, the power transmission and distribution price is more strictly and transparently regulated by the government, and the profit mode of the company is also greatly influenced. Therefore, the power grid cost management is taken as the core work for controlling the construction cost of the company, and the development principle of high quality and high efficiency must be followed. In the future, the investment and cost of the power grid must be controlled as a hand grip to strengthen the accurate investment and accurate control of capital construction. In order to meet the strategic development requirements of national grid companies, project construction and management modes must be innovated, a new idea of grid project cost management in a new era must be explored, and the project cost control capability and level are comprehensively improved by taking service grid construction as a starting point.
The overhead line engineering is used as an important production, operation and maintenance carrier of a company, the cost is influenced by a plurality of factors, and the large cost difference is caused by the difference of factors such as terrain, geology, weather, transportation conditions and the like. For a long time, the engineering budget, completion settlement and financial settlement of the overhead transmission line are all priced according to the whole line, the engineering construction management and asset collection do not form fine management, independent pricing is not carried out according to the actual investment of different towers, the actual cost level of each tower cannot be reflected, and the cost accuracy is to be further improved. Therefore, a pricing model must be innovated, the construction cost management of the power grid project is enhanced, the project investment is reasonably controlled, and the power grid construction benefit is improved.
Disclosure of Invention
The application provides a single-base-tower-based intelligent prediction method for the construction cost of a power transmission line, which is used for solving the problems of unscientific prediction method and low prediction precision of the existing prediction method.
The technical scheme adopted by the application for solving the technical problems is as follows:
a power transmission line engineering cost intelligent prediction method based on a single-base tower comprises the following steps:
an empirical parameter prediction model is constructed by combining empirical parameters of a least square support vector machine model;
optimizing parameters in the prediction model by utilizing a particle swarm optimization algorithm to obtain a particle swarm optimization least square support vector machine prediction model;
and inputting the original data serving as an input variable into the optimized vector machine prediction model, and training the vector machine prediction model to obtain the cost prediction value of the single-base tower.
Optionally, before the raw data is input to the optimized vector machine prediction model as an input variable, the method further includes:
and collecting historical single-base tower construction cost data of different types of projects, and carrying out standardized processing on the historical single-base tower construction cost data to obtain the original data.
Optionally, the original data is historical basic data related to construction cost of different types of transmission lines.
Optionally, the collecting historical data of the single-base tower construction costs of different types of projects and performing standardized processing on the historical data of the single-base tower construction costs includes:
using principal component analysis to reduce dimension of historical data, and outputting a new sample, comprising: firstly, standardizing the historical data to obtain standardized data; then, calculating a correlation coefficient matrix, and a characteristic value and a characteristic vector thereof according to the standardized data; and finally, calculating the variance contribution rate of each principal component, and selecting the first n principal components with the accumulated variance contribution rate reaching a preset value as new samples to be output.
Optionally, the constructing an empirical parameter prediction model by combining the empirical parameters of the least squares support vector machine model includes:
and (3) giving empirical parameters of the least square support vector machine model, and constructing an empirical parameter prediction model.
Optionally, the optimizing the parameters in the prediction model by using the particle swarm optimization algorithm to obtain the particle swarm optimization least squares support vector machine prediction model includes:
firstly, acquiring the speed and the position of an initialized particle, calculating the fitness of the current particle according to the speed and the position of the particle, selecting the position corresponding to the minimum value of the fitness of the current particle as an individual extreme value of the particle, comparing the fitness of the individual extreme value of the current particle with the fitness of the individual extreme value of the previous particle, and selecting the position corresponding to the particle with low fitness as a global extreme value; then updating the speed and the position of the particle, and repeatedly executing the step of calculating the current particle fitness according to the speed and the position of the particle until the iteration times reach the maximum iteration times or the precision reaches the preset precision, outputting a global optimal position, namely the parameter of the optimized least square support vector machine; and finally, constructing an optimized parameter prediction model according to the optimized parameters.
Optionally, the inputting the original data as an input variable into the optimized vector machine prediction model, and training the vector machine prediction model to obtain the cost prediction value of the single-base tower includes:
establishing a standard LSSVM model, substituting data into the model for training, selecting an empirical parameter C of 100, and selecting a basic parameter penalty coefficient and a kernel function parameter of the LSSVM prediction model20.4 kernel function K (x, x)i) Choosing a radial basis (radialbis) kernel function:
optionally, the machine prediction model performs a training step to obtain a final prediction result.
The technical scheme provided by the application comprises the following beneficial technical effects:
the application provides a single-base-tower-based intelligent prediction method for construction cost of power transmission line, which comprises the following steps: an empirical parameter prediction model is constructed by combining empirical parameters of a least square support vector machine model; optimizing parameters in the prediction model by utilizing a particle swarm optimization algorithm to obtain a particle swarm optimization least square support vector machine prediction model; and inputting the original data serving as an input variable into the optimized vector machine prediction model, and training the vector machine prediction model to obtain the cost prediction value of the single-base tower. The method provided by the application carries out dimension reduction processing on the indexes by using a principal component analysis method, creatively introduces a particle swarm algorithm to carry out parameter optimization on a least square support vector machine model to obtain optimal parameters, and respectively introduces the obtained principal component data into an empirical parameter prediction model and an optimized parameter prediction model to train and predict, so that the accuracy of cost prediction can be improved, the refinement level of cost management is improved, and the problems of unscientific performance and low prediction accuracy of the existing prediction method are solved.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.
Fig. 1 is a flowchart of an intelligent prediction method for the construction cost of a power transmission line based on a single-base tower according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions in the present application better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application; it is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a flowchart of an intelligent prediction method for construction cost of a power transmission line based on a single-base tower according to an embodiment of the present application, and as shown in fig. 1, the intelligent prediction method for construction cost of a power transmission line based on a single-base tower according to the embodiment of the present application includes the following steps:
s1: and (4) combining the empirical parameters of the least square support vector machine model to construct an empirical parameter prediction model.
The method specifically comprises the following steps:
(1) firstly, the methodUsing a non-linear mappingMapping the sample from original space to high-dimensional feature space, and converting the non-linear estimation function into linear estimation function of high-dimensional feature space
Respectively representing the weight vector and the offset of the regression function by using omega and b, and searching omega and b to minimize according to the structural risk minimization principle, namely:
in the formula: | ω | non-calculation2To control the complexity of the model; c is a regularization parameter, which controls the punishment degree of the exceeding error samples; rempThe LSSVM selects the error ξ in optimizing the objective for an error control function, i.e., an ε -insensitive loss functioniAs a loss function, the optimization problem is:
formula (III) ξiIs a relaxation factor. The corresponding Lagrange method is established to solve the problem, and the method comprises the following steps:
formula (III) αi(i ═ 1,2, …, L) is the lagrange multiplier according to the KKT optimization conditions, i.e. solving L for ω, b, ξ, respectivelyiThe partial derivative of α, and let it equal 0, can be found:
(2) α and b are obtained by a least square method, and finally a decision function for carrying out regression analysis on the nonlinear function by applying the LSSVM is obtained as follows:
s2: and optimizing parameters in the prediction model by utilizing a particle swarm optimization algorithm to obtain a particle swarm optimization least square support vector machine prediction model.
The method specifically comprises the following steps:
(1) in a d-dimensional search space, m particles representing possible solutions to the problem constitute X ═ X1,X2,L,XmIn which X isi＝{xi1,xi2,L,xidDenotes the position of the ith particle, d is the number of LSSVM parameters, where d is 2. Calculating the mean square error generated by each LSSVM on the training set, and constructing a fitness function as follows for calculating the fitness of the individual:
f(x)＝MSE(x)
(2) the flying speed of the particle in d-dimensional space is defined as Vi＝{vi1,vi2,L,vidWith Pi＝{Pi1,Pi2,L,PidDenotes the best position P searched by the particle itselfbest(corresponding fitness minimum), by Pg＝{Pg1,Pg2,L,PgdDenotes the best position G of the whole populationbestThen the velocity and position update for the ith particle is determined according to the following equation:
in the formula, omega is an inertia weight factor; t is the number of iterations; c. C1And c2The step length of the particle flying to the optimal position and the overall optimal position is represented as an acceleration factor; rand () is in the interval [0,1 ]]Uniformly distributed random numbers.
(3) And if the iteration times reach the maximum iteration times or the precision reaches the preset precision, exiting the iteration cycle and returning to the global optimal parameters. The kernel function parameters and the punishment coefficients in the LSSVM model can be optimized by utilizing the particle swarm optimization, artificial exhaustive trial and error are avoided, and a better fitting effect can be obtained.
S3: and inputting the original data serving as an input variable into the optimized vector machine prediction model, and training the vector machine prediction model to obtain the cost prediction value of the single-base tower.
Optionally, before the raw data is input to the optimized vector machine prediction model as an input variable, the method further includes:
and collecting historical single-base tower construction cost data of different types of projects, and carrying out standardized processing on the historical single-base tower construction cost data to obtain the original data.
The historical data of the construction cost of the single-base tower is that the construction cost data of the engineering single-base tower is sorted and decomposed by taking 20 groups of 110kV transmission line engineering historical data actually settled in a certain region of China in 2018 as samples.
The collected historical data is subjected to standardization processing, and the method comprises the following steps:
(1) standardizing historical data
The historical data is normalized according to the following formula:
wherein,
(2) calculating a sample correlation coefficient matrix
Assuming that the historical data is still represented by X after being normalized, the correlation coefficient of the normalized data is:
wherein,
(3) calculating the eigenvalue (lambda) of the correlation coefficient matrix R1,λ2,L,λp) And corresponding feature vector ai＝(ai1,ai2,L,aip),i＝1,2,L,p
(4) Selecting important principal components and obtaining principal component expression
P principal components can be obtained through principal component analysis, but because the variance of each principal component decreases and the amount of information contained in the principal component decreases correspondingly, in practical analysis, the first k principal components are generally selected according to the magnitude of the cumulative contribution rate of each principal component (namely, the proportion of the variance of a certain principal component to the total variance), and the cumulative contribution rate of the first k principal components is generally required to reach more than 85%.
Further, the original data are historical basic data related to different types of transmission line construction cost.
Further, the collecting historical data of the single-base tower construction cost of different types of projects and carrying out standardization processing on the historical data of the single-base tower construction cost comprises the following steps:
using principal component analysis to reduce dimension of historical data, and outputting a new sample, comprising: firstly, standardizing the historical data to obtain standardized data; then, calculating a correlation coefficient matrix, and a characteristic value and a characteristic vector thereof according to the standardized data; and finally, calculating the variance contribution rate of each principal component, and selecting the first n principal components with the accumulated variance contribution rate reaching a preset value as new samples to be output.
Further, the building an empirical parameter prediction model by combining empirical parameters of the least squares support vector machine model includes:
and (3) giving empirical parameters of the least square support vector machine model, and constructing an empirical parameter prediction model.
Optionally, the optimizing the parameters in the prediction model by using the particle swarm optimization algorithm to obtain the particle swarm optimization least squares support vector machine prediction model includes:
firstly, acquiring the speed and the position of an initialized particle, calculating the fitness of the current particle according to the speed and the position of the particle, selecting the position corresponding to the minimum value of the fitness of the current particle as an individual extreme value of the particle, comparing the fitness of the individual extreme value of the current particle with the fitness of the individual extreme value of the previous particle, and selecting the position corresponding to the particle with low fitness as a global extreme value; then updating the speed and the position of the particle, and repeatedly executing the step of calculating the current particle fitness according to the speed and the position of the particle until the iteration times reach the maximum iteration times or the precision reaches the preset precision, outputting a global optimal position, namely the parameter of the optimized least square support vector machine; and finally, constructing an optimized parameter prediction model according to the optimized parameters.
Optionally, the inputting the original data as an input variable into the optimized vector machine prediction model, and training the vector machine prediction model to obtain the cost prediction value of the single-base tower includes:
establishing a standard LSSVM model, substituting data into the model for training, selecting an empirical parameter C of 100, and selecting a basic parameter penalty coefficient and a kernel function parameter of the LSSVM prediction model20.4 kernel function K (x, x)i) Choosing a radial basis (radialbis) kernel function:
further, the machine prediction model is trained to obtain the final prediction result.
The method provided by the application carries out dimension reduction processing on the indexes by using a principal component analysis method, creatively introduces a particle swarm algorithm to carry out parameter optimization on a least square support vector machine model to obtain optimal parameters, and respectively introduces the obtained principal component data into an empirical parameter prediction model and an optimized parameter prediction model to train and predict, so that the accuracy of cost prediction can be improved, the refinement level of cost management is improved, and the problems of unscientific performance and low prediction accuracy of the existing prediction method are solved.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be understood that the present application is not limited to what has been described above and shown in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (8)
1. A power transmission line engineering cost intelligent prediction method based on a single-base tower is characterized by comprising the following steps:
an empirical parameter prediction model is constructed by combining empirical parameters of a least square support vector machine model;
optimizing parameters in the prediction model by utilizing a particle swarm optimization algorithm to obtain a particle swarm optimization least square support vector machine prediction model;
and inputting the original data serving as an input variable into the optimized vector machine prediction model, and training the vector machine prediction model to obtain the cost prediction value of the single-base tower.
2. The intelligent single-base-tower-based power transmission line construction cost prediction method according to claim 1, wherein before the original data is input to the optimized vector machine prediction model as an input variable, the method further comprises:
and collecting historical single-base tower construction cost data of different types of projects, and carrying out standardized processing on the historical single-base tower construction cost data to obtain the original data.
3. The intelligent single-base-tower-based power transmission line construction cost prediction method according to claim 1, wherein the original data is historical basic data related to different types of power transmission line construction costs.
4. The intelligent prediction method for the construction cost of the power transmission line based on the single-base tower as claimed in claim 2, wherein the collecting historical data of the construction cost of the single-base tower of different types of projects and the standardizing the historical data of the construction cost of the single-base tower comprises:
using principal component analysis to reduce dimension of historical data, and outputting a new sample, comprising: firstly, standardizing the historical data to obtain standardized data; then, calculating a correlation coefficient matrix, and a characteristic value and a characteristic vector thereof according to the standardized data; and finally, calculating the variance contribution rate of each principal component, and selecting the first n principal components with the accumulated variance contribution rate reaching a preset value as new samples to be output.
5. The intelligent single-base-tower-based power transmission line engineering cost prediction method according to claim 1, wherein the construction of the empirical parameter prediction model by combining empirical parameters of a least squares support vector machine model comprises:
and (3) giving empirical parameters of the least square support vector machine model, and constructing an empirical parameter prediction model.
6. The intelligent single-base-tower-based power transmission line engineering cost prediction method according to claim 1, wherein the parameters in the prediction model are optimized by using a particle swarm optimization algorithm to obtain a particle swarm optimization least squares support vector machine prediction model, and the method comprises the following steps:
firstly, acquiring the speed and the position of an initialized particle, calculating the fitness of the current particle according to the speed and the position of the particle, selecting the position corresponding to the minimum value of the fitness of the current particle as an individual extreme value of the particle, comparing the fitness of the individual extreme value of the current particle with the fitness of the individual extreme value of the previous particle, and selecting the position corresponding to the particle with low fitness as a global extreme value; then updating the speed and the position of the particle, and repeatedly executing the step of calculating the current particle fitness according to the speed and the position of the particle until the iteration times reach the maximum iteration times or the precision reaches the preset precision, outputting a global optimal position, namely the parameter of the optimized least square support vector machine; and finally, constructing an optimized parameter prediction model according to the optimized parameters.
7. The intelligent single-base-tower-based power transmission line engineering cost prediction method according to claim 1, wherein the step of inputting the original data as input variables into the optimized vector machine prediction model and training the vector machine prediction model to obtain the predicted single-base-tower cost value comprises the steps of:
establishing a standard LSSVM model, substituting data into the model for training, selecting an empirical parameter C of 100, and selecting a basic parameter penalty coefficient and a kernel function parameter of the LSSVM prediction model20.4 kernel function K (x, x)i) Choosing a Radial Basis kernel function:
8. the intelligent single-base-tower-based power transmission line engineering cost prediction method according to claim 7, characterized by repeating the steps of inputting the original data as an input variable into the optimized vector machine prediction model, and training the vector machine prediction model to obtain a final prediction result.
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| CN112508512A (en) * | 2020-11-26 | 2021-03-16 | 国网河北省电力有限公司经济技术研究院 | Power grid engineering cost data management method and device and terminal equipment |
| CN113657459A (en) * | 2021-07-28 | 2021-11-16 | 合肥国轩高科动力能源有限公司 | Battery SOC prediction method and medium based on combination of principal component and support vector machine |
| CN116308579A (en) * | 2023-02-23 | 2023-06-23 | 葛洲坝集团交通投资有限公司 | Particle swarm-SVM-based engineering cost determination method, system and product |
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| CN116308579A (en) * | 2023-02-23 | 2023-06-23 | 葛洲坝集团交通投资有限公司 | Particle swarm-SVM-based engineering cost determination method, system and product |
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