CN114140158A - Power distribution network investment demand determination method, device, equipment and storage medium based on combination prediction - Google Patents
Power distribution network investment demand determination method, device, equipment and storage medium based on combination prediction Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a storage medium for determining investment requirements of a power distribution network based on combined prediction, wherein the method comprises the following steps: combing factors influencing the investment requirement of a distribution network based on a fishbone graph method; screening the factors based on a grey correlation degree analysis model to obtain key factors influencing the investment requirement of the distribution network; acquiring medium and low voltage distribution network investment scale and key factor historical data; establishing a distribution network investment demand prediction model by using a support vector machine method, a linear regression method and a gray prediction method; and inputting the key factors, the medium and low voltage distribution network investment scale and the key factor historical data into a distribution network investment demand prediction model to obtain a prediction result. Through the mode, the method and the device can realize accurate prediction of medium-low voltage distribution network investment requirements, and provide decision basis for improving distribution network investment lean level for power grid enterprises.
Description
Technical Field
The application relates to the technical field of medium and low voltage distribution network investment, in particular to a method, a device, equipment and a storage medium for determining the investment requirement of a distribution network based on combination prediction.
Background
In recent years, in order to adapt to the situation of rapid development of economic society, power grid enterprises invest large-scale capital to construct medium and low voltage distribution networks, the medium and low voltage distribution networks in cities and rural areas are improved and upgraded, and rapid increase of power consumption requirements and continuous improvement of power supply reliability and power quality are effectively supported. Under the condition that the investment scale of the medium-low voltage distribution network is continuously kept high, the problems of heavy investment, light income, heavy standing, light management and the like exist to different degrees. The power system innovation leads the power grid enterprise development to pay more attention to the quality and benefit, and limited investment is distributed to the most needed place, but at present, the phenomena of low load rate, super-scale investment and the like caused by large investment are still existed.
The accurate demand prediction is used as one of important links for supporting medium-low voltage distribution network investment decision making, and the investment benefit and efficiency level of a power grid enterprise can be effectively improved. However, at present, deep research on the prediction of the investment demand of the medium and low voltage distribution network at home and abroad is not carried out, and a corresponding prediction method and model support are lacked, so that the prediction precision of the investment demand is insufficient, and the better realization of the investment benefit level of the medium and low voltage distribution network is influenced.
Disclosure of Invention
The application provides a power distribution network investment demand determination method, a power distribution network investment demand determination device, power distribution network investment demand determination equipment and a power distribution network investment demand determination storage medium based on combination prediction, and aims to solve the problem that in the prior art, the investment demand prediction precision is insufficient, and therefore the better realization of the investment benefit level of a medium-low voltage power distribution network is influenced.
In order to solve the technical problem, the application provides a power distribution network investment demand determination method based on combination prediction, which comprises the following steps: combing factors influencing the investment requirement of a distribution network based on a fishbone graph method; screening the factors based on a grey correlation degree analysis model to obtain key factors influencing the investment requirement of the distribution network; acquiring medium and low voltage distribution network investment scale and key factor historical data; establishing a distribution network investment demand prediction model by using a support vector machine method, a linear regression method and a gray prediction method; and inputting the key factors, the medium and low voltage distribution network investment scale and the key factor historical data into a distribution network investment demand prediction model to obtain a prediction result.
Optionally, factors influencing the medium and low voltage distribution network investment requirement are combed based on a fishbone graph method, and the factors comprise: analyzing the investment requirement of the distribution network to obtain extraction factors in the aspects of structure and process; and drawing the extraction factors into a fishbone graph with hierarchy according to the relevance among the factors.
Optionally, the extraction factors include external driving factors and internal self-development factors; the external driving factors comprise power distribution, a power structure, load characteristics, load distribution, per-capita electricity consumption, power demand density, per-capita GDP, an economic structure, urbanization rate, population total and power supply area; the internal self-development factors comprise power supply reliability, line load rate, line loss rate, equipment utilization rate, voltage grade, power supply capacity and voltage qualification rate.
Optionally, screening the factors based on a grey correlation analysis model to obtain key factors influencing the distribution network investment requirement, including: and constructing an incidence relation analysis model between different factors and the investment scale, combining the incidence settlement results of the different factors and the investment scale, and judging the factors as key factors when the settlement results are greater than a preset value.
Optionally, the key factors include the per-person GDP, the per-person electricity consumption, the power demand density, the line load rate, and the power supply reliability rate.
Optionally, the method for establishing a distribution network investment demand prediction model by using a support vector machine method, a linear regression method and a gray prediction method includes: constructing a first prediction model based on a support vector machine method; constructing a second prediction model based on a linear regression method; constructing a third prediction model based on a gray prediction method; and establishing a distribution network investment demand prediction model comprising a first prediction model, a second prediction model and a third prediction model.
Optionally, when the settlement result is greater than the preset value, determining that the factor is a key factor, including: and when the settlement result is more than 0.7, judging the factor as a key factor.
In order to solve the above technical problem, the present application provides a power distribution network investment demand determination device based on combination prediction, including: the factor combing module is used for combing factors influencing the distribution network investment demand based on a fishbone graph method; the key factor module is used for screening factors based on the grey correlation degree analysis model to obtain key factors influencing the distribution network investment requirement; the acquisition module is used for acquiring the investment scale of the medium-low voltage distribution network and the historical data of key factors; the prediction model module is used for establishing a distribution network investment demand prediction model by utilizing a support vector machine method, a linear regression method and a gray prediction method; and the prediction result module is used for inputting the key factors, the medium and low voltage distribution network investment scale and the key factor historical data into the distribution network investment demand prediction model to obtain a prediction result.
In order to solve the technical problem, the present application provides an electronic device, which includes a memory and a processor, where the memory is connected to the processor, and the memory stores a computer program, and the computer program is executed by the processor to implement the method for determining the investment requirement of the power distribution network based on the combined prediction.
In order to solve the above technical problem, the present application provides a computer-readable storage medium storing a computer program, and the computer program, when executed, implements the method for determining the investment requirement of the power distribution network based on combinational prediction.
The application provides a method, a device, equipment and a storage medium for determining the investment demand of a power distribution network based on combined prediction, wherein the method comprises the following steps: combing factors influencing the investment requirement of a distribution network based on a fishbone graph method; screening the factors based on a grey correlation degree analysis model to obtain key factors influencing the investment requirement of the distribution network; acquiring medium and low voltage distribution network investment scale and key factor historical data; establishing a distribution network investment demand prediction model by using a support vector machine method, a linear regression method and a gray prediction method; and inputting the key factors, the medium and low voltage distribution network investment scale and the key factor historical data into a distribution network investment demand prediction model to obtain a prediction result. Through the mode, the method and the device can realize accurate prediction of medium-low voltage distribution network investment requirements, and provide decision basis for improving distribution network investment lean level for power grid enterprises.
Drawings
In order to more clearly illustrate 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 that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating an embodiment of a method for determining investment requirements of a power distribution network based on portfolio prediction according to the present application;
FIG. 2 is a schematic diagram illustrating an embodiment of the present application for determining investment requirements of a power distribution network based on portfolio forecasting;
FIG. 3 is a schematic structural diagram of an embodiment of an electronic device of the present application;
FIG. 4 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present application, the method, apparatus, device and storage medium for determining the investment requirement of a power distribution network based on portfolio prediction provided in the present application are further described in detail below with reference to the accompanying drawings and the detailed description.
The application provides a method for determining an investment demand of a power distribution network based on combinatorial prediction, please refer to fig. 1, where fig. 1 is a schematic flowchart of an embodiment of the method for determining an investment demand of a power distribution network based on combinatorial prediction according to the application, and in this embodiment, the method for determining an investment demand of a power distribution network based on combinatorial prediction may include steps S110 to S150, where each step is specifically as follows:
s110: and combing the factors influencing the investment requirement of the distribution network based on a fishbone graph method.
Specifically, analyzing the investment requirements of the distribution network to obtain extraction factors in the aspects of structure and process; and drawing the extraction factors into a fishbone graph with hierarchy according to the relevance among the factors.
The extraction factors comprise external driving factors and internal self-development factors; the external driving factors comprise power distribution, a power structure, load characteristics, load distribution, per-capita electricity consumption, power demand density, per-capita GDP, an economic structure, urbanization rate, population total and power supply area; the internal self-development factors comprise power supply reliability, line load rate, line loss rate, equipment utilization rate, voltage grade, power supply capacity and voltage qualification rate.
The fishbone pattern was invented by doctor Shichun, Japan and is therefore also called Shichuan pattern. The basic idea is as follows: the method comprises the steps of extracting factors from the aspects of the structure, the flow and the like of an analyzed object, and then drawing the factors into a graph with clear level and clear order according to the relevance among the factors. Since the figure looks like a fishbone, it is referred to figuratively as a fishbone figure. The fishbone map method is a qualitative research method and is widely applied to the fields of management, technical and economic analysis and the like. In actual operation, the factors are generally divided into three levels, namely a major factor, a middle factor and a minor factor. The application of the fishbone picture is divided into two steps, namely analyzing factors and drawing the fishbone picture.
The specific application steps for identifying the influence on the investment requirement of the medium-low voltage distribution network by using the fishbone map method are as follows:
(1) analysis factors:
1) for the study subjects, the classification mode, i.e. the major factor, was chosen.
2) And respectively finding out all possible factors in various types by using a brain storm method, a Delphi method and the like.
3) And (5) sorting the factors to determine the attributes of the factors.
4) The factors are briefly described.
(2) Drawing a fishbone picture:
at present, a plurality of special software can be used for drawing fishbone images, such as visio, XMind and the like. Simple fishbone maps can also be drawn in Word and Excel. The drawing method comprises the following steps:
1) the problem to be studied is identified on the fish head.
2) Draw out the big bone and fill in the big cause.
3) The middle bone and the small bone are extended from the large bone, and the middle and small factors are filled in respectively.
4) If necessary, the specific elements are briefly described, and the important elements are designated by specific symbols.
Note that: the principle that the more factors are better is adhered to when the factors are found out, and then the factors are simplified; when the drawing is carried out, the major factors generally form an included angle of 60 degrees with the horizontal line, the middle factors generally keep horizontal, and the minor factors generally keep horizontal or form 60 degrees according to the clearness and the attractiveness of the whole graph, so that the drawing is convenient to observe and record.
S120: and screening the factors based on the grey correlation degree analysis model to obtain key factors influencing the distribution network investment requirement.
Optionally, screening the factors based on a grey correlation analysis model to obtain key factors influencing the distribution network investment requirement, including: and constructing an incidence relation analysis model between different factors and the investment scale, combining the incidence settlement results of the different factors and the investment scale, and judging the factors as key factors when the settlement results are greater than a preset value. In some embodiments, the preset value may be set to 0.7. The key factors may include the per-person GDP, per-person electricity usage, power demand density, line load rate, and power supply reliability.
Specifically, the basic principle of the grey correlation analysis model is as follows:
(1) determining the reference sequence and the comparison sequence. The reference sequence is a data sequence reflecting the behavior characteristics of the system, and the comparison sequence is a data sequence reflecting factors affecting the behavior of the system. In the analysis of the influence factors, the reference sequence is a data sequence of the investment of the power distribution network, and the comparison sequence is a data sequence of each influence factor.
(2) And carrying out non-dimensionalization processing on the reference sequence and the comparison sequence. The physical significance of the factors in the system is different, and therefore the influence of the dimension must be eliminated.
(3) And calculating the correlation coefficient. After the dimensionless processing, the reference number sequence is recorded as { x0(t) and the comparison number is { x }i(t) }. The correlation coefficient reflects two sequences X0And XiThe degree of tightness at time j.
(4) And (5) calculating the degree of association. And drawing a correlation coefficient curve in a coordinate graph with the abscissa as time t and the ordinate as the correlation coefficient. The area between this curve and the abscissa is called the correlation area. Let the correlation area of the reference sequence (correlation coefficient is 1 everywhere) be S00The correlation area of the comparison sequence is S0i。
A relevancy formula;
(5) and sequencing the relevance of the construction cost by a plurality of factors, wherein the greater the relevance is, the greater the influence degree of the representation factors on the construction cost is. Therefore, by combining the calculation results, the key factors with the relevance degree of more than 0.7 and influencing the investment requirement of the medium and low voltage distribution network are selected to comprise: the GDP is the average power consumption of people, the electricity demand density of people, the comprehensive voltage qualification rate and the power supply reliability.
S130: and obtaining the investment scale of the medium and low voltage distribution network and the historical data of key factors.
S140: and establishing a distribution network investment demand prediction model by using a support vector machine method, a linear regression method and a gray prediction method.
Optionally, constructing a first prediction model based on a support vector machine method; constructing a second prediction model based on a linear regression method; constructing a third prediction model based on a gray prediction method; and establishing a distribution network investment demand prediction model comprising a first prediction model, a second prediction model and a third prediction model.
"support vector" refers to some of the most difficult training points to classify very close to the classification decision surface. "machine" refers to an algorithm, also called a prediction function, a learning function. The support vector machine-SVM refers to a supervised (instructor-based) learning method, in the case of the known class of training points, the corresponding relationship between the training points and the class is found, the training points are separated, and then the class corresponding to the new training point is predicted.
The core idea of the support vector machine is the lifting and linearization. The support vector machine method is also based on the VC dimension theory of the statistical learning theory and the principle of minimizing the structural risk. Based on the limited sample information, the best compromise between the complexity of the model and the learning ability is sought to obtain the best generalization ability. With the application and development of the model, the support vector machine algorithm can be optimized based on Particle Swarm Optimization (PSO), Genetic Algorithm (GA), least square method and other methods. The support vector machine model based on the least square method is mainly used.
The principle of solving nonlinear function optimization by the support vector machine is to perform linear programming solution on a high-dimensional feature space by mapping nonlinearity into the high-dimensional space. Firstly, a decision function (classification hyperplane) is constructed, and sample data is classified. The classification hyperplane can maximize blank areas on two sides of the hyperplane while ensuring classification precision, so that optimal classification of linear separable problems is realized. Linear divisible refers to the division of sample points belonging to different classes by one or several straight lines. The support vector machine aims to find a hyperplane with the largest separation edge, and the hyperplane is the optimal hyperplane (OptimalHyperplane).
In summary, SVM maps the input vector to a high-dimensional feature space through some kind of pre-selected non-linear mapping, and constructs an optimal classification hyperplane in this feature space. In form, the SVM classification function is analogous to a neural network, with the output being a linear combination of intermediate nodes, one support vector for each intermediate node. The number of intermediate nodes of the neural network is selected by experience or contrast experiments, and the difference of logics can greatly influence the network performance; and the number of intermediate nodes of the SVM is automatically determined by calculation.
The multivariate statistical regression model for cost prediction began in 1970, and Kouskoulas predicted investment estimates using multivariate statistical regression analysis models. Regression analysis prediction methods analyze the correlation between independent variables and dependent variables. Independent variables refer to variables that can be controlled or accurately observed (e.g., voltage level, line length, etc.) and have an effect on the prediction, expressed as x (x)iWhere i is 1,2, 3., n), the dependent variable is a variable that changes with the independent variable (e.g., the cost of the power transmission and transformation project), and y is used as y (y)iI ═ 1,2, 3.., n) denotes.
And establishing a linear regression equation between the dependent variable and the independent variable, carrying out hypothesis test on the linear regression equation to prove that the regression equation is satisfactory, and then substituting the regression equation into the known value of the independent variable to predict the corresponding dependent variable by using the regression equation as a prediction model.
The gray prediction method is a method for predicting a system containing uncertain factors, and is characterized in that a gray prediction model is constructed by using a series of quantitative values of reaction prediction object features observed at equal time intervals, and the feature quantity at a certain future time or the time for reaching a certain feature quantity is predicted. The grey prediction is used for searching the rule of system change by identifying the different degrees of development trends among system factors, generating a data sequence with strong regularity and then establishing a corresponding differential equation model so as to predict the future development trend of the object.
The grey prediction is to identify the different degrees of the development trends among the system factors, namely, to perform correlation analysis, and to perform generation processing on the original data to find the rule of system change, to generate a data sequence with strong regularity, and then to establish a corresponding differential equation model, so as to predict the condition of the future development trend of the object. A gray prediction model is constructed by using a series of characteristic quantity values of a prediction object observed at equal time intervals, and the characteristic quantity of a certain future moment or the time reaching a certain characteristic quantity is predicted.
The multiple linear regression prediction technology mainly researches the correlation between a dependent variable and a plurality of independent variables. A phenomenon is often associated with multiple factors, and predicting or estimating a dependent variable from an optimal combination of multiple independent variables together is more efficient and more practical than predicting or estimating with only one independent variable.
Let variables x1, x2, …, xp be p (p >1) linearly independent controllable variables, y be random variables, wherein x1, x2, …, xp are respectively the per-capita GDP, per-capita electricity consumption, power demand density, comprehensive voltage qualification rate, and power supply reliability of a certain area. y is the historical investment requirement of a corresponding certain area, wherein the relationship between the two is as follows:
in the formula: b0,b1,…,bp，σ2Are unknown parameters to be solved, and epsilon is a random error, which is a p-element linear regression model.
N independent observations of the variables x1, x2, …, xp, and y, resulted in one sample with a capacity of n:
(xi1,xi2…,xip,yi)(i＝1,2,…,n)；
in load prediction, these constants are past history data and are derived from p-linear regression equations:
for the convenience of mathematical processing, the above formula is expressed in a matrix form. Recording:
the linear regression model can be rewritten as:
Y＝XB+ε；
the estimated vector of BETA is
Thus, it is possible to obtain:
will obtain
Substituting p element linear regression relations to obtain:
this equation is called a p-element linear regression equation.
Referred to as the coefficients of the regression equation.
For example, the following steps are carried out:
(1) setting a time sequence X(0)With n observations, x(0)＝{x(0)(1),x(0)(2),x(0)(3),…,x(0)(n) generating a new sequence x by accumulation(1)＝{x(1)(1),x(1)(2),x(1)(3),…,x(1)(n), the corresponding differential equation of the GM (1,1) model is:
wherein: a is called development ash number; u is called endogenous control ash number.
(2) Is provided with
For the parameter vector to be estimated, the least square method is utilized to obtain:
solving the differential equation can obtain a prediction model as follows:
(3) model inspection
The gray prediction test generally comprises a residual test, a relevance test and a posterior difference test, and the model prediction effect is verified through the model test so as to carry out model correction.
S150: and inputting the key factors, the medium and low voltage distribution network investment scale and the key factor historical data into a distribution network investment demand prediction model to obtain a prediction result.
Let y (k) (1, 2., n) be the actual value of the grid investment in the k year, fi(k) And (I ═ 1,2.., I) is a k-th-year power grid investment predicted value obtained by the ith prediction model, and wi is the weight of the ith prediction model. e.g. of the typei(k) For the k-year power grid investment prediction error of the ith prediction model, the following are provided:
and if y ^ (k) is the predicted value of the kth year obtained by utilizing the power grid investment portfolio model, the following steps are carried out:
constructing a power grid investment portfolio prediction model with the aim of minimizing the sum of squares of prediction errors as follows:
the target function is most widely applied in various fields and is simple to calculate. And finally, obtaining a final prediction result through comprehensive calculation.
The method for determining the investment demand of the power distribution network based on the combined prediction can realize the accurate prediction of the scale of the investment demand of medium and low voltage distribution networks and provide decision basis for improving the lean level of the distribution network investment for power grid enterprises.
Based on the foregoing method for determining power distribution network investment demand based on portfolio prediction, the present application provides a device for determining power distribution network investment demand based on portfolio prediction, please refer to fig. 2, fig. 2 is a schematic structural diagram of an embodiment of the device for determining power distribution network investment demand based on portfolio prediction, in this embodiment, the determining of power distribution network investment demand based on portfolio prediction may include:
the factor combing module is used for combing factors influencing the distribution network investment demand based on a fishbone graph method;
the key factor module is used for screening factors based on the grey correlation degree analysis model to obtain key factors influencing the distribution network investment requirement;
the acquisition module is used for acquiring the investment scale of the medium-low voltage distribution network and the historical data of key factors;
the prediction model module is used for establishing a distribution network investment demand prediction model by utilizing a support vector machine method, a linear regression method and a gray prediction method;
and the prediction result module is used for inputting the key factors, the medium and low voltage distribution network investment scale and the key factor historical data into the distribution network investment demand prediction model to obtain a prediction result.
Based on the method for determining the investment requirement of the power distribution network based on the combined prediction, the present application further provides an electronic device, as shown in fig. 3, where fig. 3 is a schematic structural diagram of an embodiment of the electronic device of the present application. The electronic device 300 may comprise a memory 31 and a processor 32, the memory 31 being connected to the processor 32, the memory 31 having stored therein a computer program, the computer program implementing the method of any of the above embodiments when executed by the processor 32. The steps and principles thereof have been described in detail in the above method and will not be described in detail herein.
In the present embodiment, the processor 32 may also be referred to as a Central Processing Unit (CPU). The processor 32 may be an integrated circuit chip having signal processing capabilities. The processor 32 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Based on the method for determining the investment demand of the power distribution network based on the combination prediction, the application also provides a computer readable storage medium. Referring to fig. 4, fig. 4 is a schematic structural diagram of an embodiment of a computer-readable storage medium according to the present application. The computer-readable storage medium 400 has stored thereon a computer program 41, the computer program 41 implementing the method of any of the above embodiments when executed by a processor. The steps and principles thereof have been described in detail in the above method and will not be described in detail herein.
Further, the computer-readable storage medium 400 may be various media that can store program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic tape, or an optical disk.
The application provides a method, a device, equipment and a storage medium for determining the investment demand of a power distribution network based on combined prediction, wherein the method comprises the following steps: combing factors influencing the investment requirement of a distribution network based on a fishbone graph method; screening the factors based on a grey correlation degree analysis model to obtain key factors influencing the investment requirement of the distribution network; acquiring medium and low voltage distribution network investment scale and key factor historical data; establishing a distribution network investment demand prediction model by using a support vector machine method, a linear regression method and a gray prediction method; and inputting the key factors, the medium and low voltage distribution network investment scale and the key factor historical data into a distribution network investment demand prediction model to obtain a prediction result. Through the mode, the method and the device can realize accurate prediction of medium-low voltage distribution network investment requirements, and provide decision basis for improving distribution network investment lean level for power grid enterprises.
It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. In addition, for convenience of description, only a part of structures related to the present application, not all of the structures, are shown in the drawings. The step numbers used herein are also for convenience of description only and are not intended as limitations on the order in which the steps are performed. 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.
The terms "first", "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. A power distribution network investment demand determination method based on combination prediction is characterized by comprising the following steps:
combing factors influencing the investment requirement of a distribution network based on a fishbone graph method;
screening the factors based on a grey correlation degree analysis model to obtain key factors influencing the investment requirement of the distribution network;
acquiring medium and low voltage distribution network investment scale and key factor historical data;
establishing a distribution network investment demand prediction model by using a support vector machine method, a linear regression method and a gray prediction method;
and inputting the key factors, the medium and low voltage distribution network investment scale and the key factor historical data into the distribution network investment demand prediction model to obtain a prediction result.
2. The method for determining the investment demand of the power distribution network based on the combined forecast as claimed in claim 1, wherein the fishbone-based method for combing the factors influencing the investment demand of the medium and low voltage distribution network comprises:
analyzing the distribution network investment requirement to obtain extraction factors in the aspects of structure and process;
and drawing the extraction factors into a fishbone graph with hierarchy according to the relevance among the factors.
3. The method for determining investment requirements of a power distribution network based on portfolio prediction as recited in claim 2,
the extraction factors comprise external driving factors and internal self-development factors;
the external driving factors comprise power distribution, a power structure, load characteristics, load distribution, per-capita electricity consumption, power demand density, per-capita GDP, an economic structure, a urbanization rate, population total and a power supply area;
the internal self-development factors comprise power supply reliability, line load rate, line loss rate, equipment utilization rate, voltage level, power supply capacity and voltage qualification rate.
4. The method for determining the investment demand of the power distribution network based on the combined prediction as claimed in claim 1, wherein the step of screening the factors based on the grey correlation degree analysis model to obtain key factors influencing the investment demand of the power distribution network comprises:
and constructing an incidence relation analysis model between different factors and the investment scale, combining the incidence settlement results of the different factors and the investment scale, and judging the factors as key factors when the settlement results are greater than a preset value.
5. The method for determining investment requirements of a power distribution network based on portfolio prediction as recited in claim 4,
the key factors comprise the average human GDP, the average human power consumption, the power demand density, the line load rate and the power supply reliability.
6. The method for determining the investment demand of the power distribution network based on the combined prediction as claimed in claim 1, wherein the establishing of the prediction model of the investment demand of the power distribution network by using a support vector machine method, a linear regression method and a gray prediction method comprises:
constructing a first prediction model based on a support vector machine method;
constructing a second prediction model based on a linear regression method;
constructing a third prediction model based on a gray prediction method;
and establishing a distribution network investment demand prediction model comprising the first prediction model, the second prediction model and the third prediction model.
7. The method for determining the investment demand of the power distribution network based on the combined forecast as claimed in claim 4, wherein when the settlement result is greater than a preset value, the factor is determined as a key factor, and the method comprises the following steps:
and when the settlement result is more than 0.7, judging the factor as a key factor.
8. A power distribution network investment demand determination device based on combination prediction is characterized by comprising the following components:
the factor combing module is used for combing factors influencing the distribution network investment demand based on a fishbone graph method;
the key factor module is used for screening the factors based on the grey correlation degree analysis model to obtain key factors influencing the investment requirement of the distribution network;
the acquisition module is used for acquiring the investment scale of the medium-low voltage distribution network and the historical data of key factors;
the prediction model module is used for establishing a distribution network investment demand prediction model by utilizing a support vector machine method, a linear regression method and a gray prediction method;
and the prediction result module is used for inputting the key factors, the medium and low voltage distribution network investment scale and the key factor historical data into the distribution network investment demand prediction model to obtain a prediction result.
9. An electronic device, comprising a memory and a processor, wherein the memory is connected to the processor, and the memory stores a computer program, and the computer program is executed by the processor to implement the combined forecast based power distribution network investment requirement determining method according to any of claims 1-7.
10. A computer-readable storage medium, characterized in that a computer program is stored, which when executed implements the combined prediction based distribution network investment demand determination method according to any one of claims 1-7.
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| CN116502771A (en) * | 2023-06-21 | 2023-07-28 | 国网浙江省电力有限公司宁波供电公司 | Power distribution method and system based on electric power material prediction |
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| CN116502771A (en) * | 2023-06-21 | 2023-07-28 | 国网浙江省电力有限公司宁波供电公司 | Power distribution method and system based on electric power material prediction |
| CN116502771B (en) * | 2023-06-21 | 2023-12-01 | 国网浙江省电力有限公司宁波供电公司 | Power distribution method and system based on electric power material prediction |
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