CN112101673A - Power grid development trend prediction method and system based on hidden Markov model - Google Patents

Power grid development trend prediction method and system based on hidden Markov model Download PDF

Info

Publication number
CN112101673A
CN112101673A CN202011000648.0A CN202011000648A CN112101673A CN 112101673 A CN112101673 A CN 112101673A CN 202011000648 A CN202011000648 A CN 202011000648A CN 112101673 A CN112101673 A CN 112101673A
Authority
CN
China
Prior art keywords
power grid
grid development
membership
trend
target index
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011000648.0A
Other languages
Chinese (zh)
Other versions
CN112101673B (en
Inventor
艾欣
胡寰宇
王智冬
彭冬
赵朗
薛雅玮
王雪莹
刘宏杨
张天琪
李一铮
刘汇川
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
North China Electric Power University
State Grid Economic and Technological Research Institute
Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
Original Assignee
North China Electric Power University
State Grid Economic and Technological Research Institute
Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by North China Electric Power University, State Grid Economic and Technological Research Institute, Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd filed Critical North China Electric Power University
Priority to CN202011000648.0A priority Critical patent/CN112101673B/en
Publication of CN112101673A publication Critical patent/CN112101673A/en
Application granted granted Critical
Publication of CN112101673B publication Critical patent/CN112101673B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Health & Medical Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Software Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Development Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Mathematical Optimization (AREA)
  • Quality & Reliability (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Operations Research (AREA)
  • Computing Systems (AREA)
  • Pure & Applied Mathematics (AREA)
  • Educational Administration (AREA)
  • Mathematical Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Game Theory and Decision Science (AREA)
  • Algebra (AREA)
  • Artificial Intelligence (AREA)
  • Computational Mathematics (AREA)
  • Molecular Biology (AREA)
  • Fuzzy Systems (AREA)
  • Biomedical Technology (AREA)
  • Automation & Control Theory (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Primary Health Care (AREA)
  • Water Supply & Treatment (AREA)
  • Public Health (AREA)
  • Probability & Statistics with Applications (AREA)

Abstract

The invention discloses a power grid development trend prediction method and system based on a hidden Markov model. The method comprises the following steps: calculating a target index value corresponding to the target index type by adopting a principal component analysis method; determining a power grid development dynamic trend feature vector according to the target index value; solving parameters of a membership function model according to the power grid development dynamic trend eigenvector and the optimization model and generating a membership function; substituting the power grid development dynamic trend feature vector into a membership function to generate a power grid development dynamic trend membership vector; generating a membership matrix according to the membership vector of the dynamic trend of power grid development, and clustering the membership matrix; and training the hidden Markov model according to the clustered membership matrix, and predicting the development trend grade of the power grid by adopting a Vibity algorithm according to the trained hidden Markov model. By adopting the method and the system, the power grid development trend can be reasonably judged so as to assist in adjusting various decision guidance of future power grid development and deployment.

Description

Power grid development trend prediction method and system based on hidden Markov model
Technical Field
The invention relates to the technical field of power grid development trend prediction, in particular to a power grid development trend prediction method and system based on a hidden Markov model.
Background
With the maturity of new technologies such as artificial intelligence and the improvement of the permeability of various types of new energy, the development brings huge changes to the power grid. The embodiment of the power grid development state cannot be distinguished from national construction, enterprise development, market construction, the power grid, and even from each view of user service. The quantification of the power grid development state is the basic work for smoothly developing power grid development diagnosis research, understanding of the power grid development situation, development dynamic evaluation and the basis for realizing power grid development trend prediction.
Based on power grid development diagnosis work, few methods for predicting and early warning the development trend of the power grid are available. In the dynamic process of accelerated development of the power grid, the power grid development result obtained by historical data evaluation is slightly delayed for the future development planning of the power grid, and the requirement of power grid development guidance cannot be met. Therefore, it is necessary to establish an effective prediction method to reasonably judge the power grid development trend so as to assist in adjusting the decision guidance of various future power grid development and deployment.
Disclosure of Invention
The invention aims to provide a power grid development trend prediction method and system based on a hidden Markov model, which can reasonably judge the power grid development trend so as to assist in adjusting various decision guidance of future power grid development and deployment.
In order to achieve the purpose, the invention provides the following scheme:
a power grid development trend prediction method comprises the following steps:
acquiring a target index set of a power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes;
carrying out standardization processing on each basic index to obtain a standardized basic index value;
calculating a target index value corresponding to each target index type by adopting a principal component analysis method according to the standardized basic index value;
determining a power grid development dynamic trend characteristic vector corresponding to each target index type according to the target index values;
acquiring a power grid development trend early warning grade, and determining a membership function model according to the power grid development trend early warning grade;
solving parameters of the membership function model according to the power grid development dynamic trend eigenvector and the optimization model, and generating a membership function according to the solved parameters; the optimization model is constructed according to the fuzzy entropy;
substituting the power grid development dynamic trend feature vector into the membership function to generate a power grid development dynamic trend membership vector;
generating a membership matrix according to the power grid development dynamic trend membership vector, and clustering the membership matrix to obtain a clustered membership matrix;
training a hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model;
and predicting the power grid development trend grade by adopting a Vibitry algorithm according to the trained hidden Markov model.
Alternatively to this, the first and second parts may,
the target index type specifically includes:
speed and scale of development, safety and quality of development, efficiency and benefit of development, and development of operations and policies;
the basic indexes of the development speed and the scale specifically comprise:
GDP acceleration, power supply installation acceleration, load acceleration, variable capacitance load ratio, line capacity load ratio, per-household distribution capacity, line length and interconnection rate;
the basic indexes for developing safety and quality specifically include:
n-1 passing rate, potential safety hazard number, power supply reliability, equipment availability factor, voltage qualification rate, equipment average service life, automation coverage rate, cabling rate and intelligent electric meter coverage rate;
the basic indexes of development efficiency and benefit specifically include:
the method comprises the following steps of (1) line maximum load rate, average load rate, transformer substation maximum load rate, line loss rate, per-capita business number, per-capita transformation capacity, per-capita line length, renewable energy access proportion, energy conservation and emission reduction and power grid investment proportion;
the basic indexes for developing the operation and policy specifically include:
unit power grid asset power supply load, unit power grid asset power selling amount, net profit, asset liability rate, income growth rate and purchase-sale price difference increment ratio.
Optionally, the normalizing each basic index to obtain a normalized basic index value specifically includes:
judging which type of the basic indexes is to obtain a first judgment result;
if the first judgment result is a maximum index, standardizing the basic index according to the following formula:
Figure BDA0002694183290000031
if the first judgment result is an extremely small index, standardizing the basic index according to the following formula:
Figure BDA0002694183290000032
if the first judgment result is a qualified index, standardizing the basic index according to the following formula:
Figure BDA0002694183290000033
in the formula (I), the compound is shown in the specification,
Figure BDA0002694183290000034
the base index value after the normalization is represented,
Figure BDA0002694183290000035
indicates a basic index value before conversion, and xi indicates a standard typeThe safety threshold value of the index, i represents the target index type, and j represents the basic index.
Optionally, the calculating, according to the normalized basic index value, a target index value corresponding to each target index type by using a principal component analysis method specifically includes:
selecting standardized basic index values in the same target index type within the full time sequence length to generate a sample matrix; the full-time length is the length between the beginning year and the ending year, the rows of the sample matrix represent the years, and the columns of the sample matrix represent the basic indexes;
calculating a correlation coefficient of each element in the sample matrix, and generating a correlation coefficient matrix according to the correlation coefficient;
solving a plurality of eigenvalues of the correlation coefficient matrix, and arranging all eigenvalues in descending order;
calculating the variance contribution rate of each characteristic value; the variance contribution rate of the characteristic value is the ratio of the selected characteristic value to the sum of all the characteristic values;
sequentially accumulating the variance contribution rates of one characteristic value from the variance contribution rate corresponding to the maximum characteristic value according to the sequence of the variance contribution rates from large to small, and taking the characteristic value corresponding to the accumulated variance contribution rate as a principal component when the accumulated value of the variance contribution rates exceeds a preset contribution rate value for the first time;
taking the sum of products of the principal component and the variance contribution rate corresponding to the principal component as a target index value;
wherein the content of the first and second substances,
the sample matrix is:
Figure BDA0002694183290000041
wherein X is a sample matrix, t represents year, XtjA normalized base index value representing a jth base index in the tth year;
the target index value is:
Ii＝α1λ1+α2λ2+...+αmλm
in the formula IiIs the target index value of the ith target index type, alphamRepresents the variance contribution rate, lambda, corresponding to the mth principal componentmRepresenting the mth principal component.
Optionally, the determining, according to the target index value, a power grid development dynamic trend feature vector corresponding to each target index type specifically includes:
the index change rate f is calculated according to the following formula:
f＝Ii(t+1)-Ii(t)
calculating the dynamic trend characteristic RES of the power grid development according to the following formula:
Figure BDA0002694183290000042
wherein the content of the first and second substances,
Figure BDA0002694183290000043
Figure BDA0002694183290000044
in the formula Ii(t +1) represents the target index value of the ith target index type in the t +1 th year, Ii(t) a target index value of an ith target index type in the t year, delta t represents a prediction step length, f (x) represents a line graph function of the target index value on a time sequence, g (x) represents a power grid development tie trend function, and x represents a time variable;
generating a power grid development dynamic trend feature vector according to the index change rate and the power grid development dynamic trend feature;
the power grid development dynamic trend feature vector Q is as follows:
Figure BDA0002694183290000051
in the formula, REStA dynamic trend characteristic representing the grid development in the t year,
Figure BDA0002694183290000052
indicating the index change rate in the t-th year.
Optionally, the determining a membership function model according to the power grid development trend early warning level specifically includes:
determining a membership function model according to the following formula:
Figure BDA0002694183290000053
Figure BDA0002694183290000054
Figure BDA0002694183290000055
in the formula, when the power grid development trend early warning grade S is 1,2,3, S is 1, the early warning is shown, when S is 2, the secondary early warning is shown, when S is 3, the health is shown, r is1(fet) represents a membership function corresponding to S1, r2(fet) represents a membership function corresponding to S2, r3(fet) represents a membership function corresponding to S ═ 3, and Q ═ fet1,fet2,...,fettH, fet represents a column vector of a power grid development dynamic trend characteristic vector Q, a1Denotes a first parameter, a2Representing the second parameter.
Optionally, the solving parameters of the membership function model according to the power grid development dynamic trend eigenvector and the optimization model, and generating the membership function according to the solved parameters specifically include:
establishing an optimization model; the optimization model is as follows:
Figure BDA0002694183290000061
Figure BDA0002694183290000062
wherein u is 1,2,3, r1＝r1(fet)，r2＝r2(fet)，r3＝r3(fet)，H(r1,r2,r3) Represents the optimization function, k represents the total number of target index types, k is 4, s (r)u) Is an intermediate function;
substituting the power grid development dynamic trend eigenvector into the optimization model and then carrying out optimization operation to obtain parameters of the membership function model;
and generating a membership function according to the parameters of the membership function model.
Optionally, the clustering the membership matrix to obtain a clustered membership matrix specifically includes:
clustering the membership matrix according to the following formula:
Figure BDA0002694183290000063
wherein E represents a sum of squares, r represents a cluster type, C represents a total number of clusters, and m represents a number classified into CrGrid development dynamic trend membership vector, CrRepresenting the cluster center and M representing the membership matrix.
The invention also provides a power grid development trend prediction system, which comprises:
the data acquisition module is used for acquiring a target index set of the power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes;
the standardization processing module is used for carrying out standardization processing on each basic index to obtain a standardized basic index value;
the principal component analysis module is used for calculating a target index value corresponding to each target index type by adopting a principal component analysis method according to the standardized basic index value;
the characteristic vector determination module is used for determining a power grid development dynamic trend characteristic vector corresponding to each target index type according to the target index values;
the membership function model establishing module is used for acquiring a power grid development trend early warning grade and determining a membership function model according to the power grid development trend early warning grade;
the membership function generation module is used for solving parameters of the membership function model according to the power grid development dynamic trend characteristic vector and the optimization model and generating a membership function according to the solved parameters; the optimization model is constructed according to the fuzzy entropy;
the membership degree vector generation module is used for substituting the power grid development dynamic trend feature vector into the membership degree function to generate a power grid development dynamic trend membership degree vector;
the clustering module is used for generating a membership matrix according to the power grid development dynamic trend membership vector and clustering the membership matrix to obtain a clustered membership matrix;
the hidden Markov model training module is used for training the hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model;
and the power grid development trend grade prediction module is used for predicting the power grid development trend grade by adopting a Vibity algorithm according to the trained hidden Markov model.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a power grid development trend prediction method and system based on a hidden Markov model, which adopts a principal component analysis method to calculate a target index value corresponding to each target index type; determining a power grid development dynamic trend characteristic vector corresponding to each target index type according to the target index values; solving parameters of a membership function model according to the power grid development dynamic trend eigenvector and the optimization model, and generating a membership function according to the parameters obtained by solving; substituting the power grid development dynamic trend feature vector into a membership function to generate a power grid development dynamic trend membership vector; generating a membership matrix according to the membership vector of the dynamic trend of power grid development, and clustering the membership matrix to obtain a clustered membership matrix; and training the hidden Markov model according to the clustered membership matrix, predicting the development trend grade of the power grid by adopting a Vibert algorithm according to the trained hidden Markov model, and reasonably judging the development trend of the power grid so as to assist in adjusting various decision guidance of future power grid development and deployment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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 invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flowchart of a power grid development trend prediction method based on a hidden Markov model according to an embodiment of the present invention;
fig. 2 is a structural diagram of a power grid development trend prediction system based on a hidden markov model in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
The invention aims to provide a power grid development trend prediction method and system based on a hidden Markov model, which can reasonably judge the power grid development trend so as to assist in adjusting various decision guidance of future power grid development and deployment.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Examples
Fig. 1 is a flowchart of a power grid development trend prediction method based on a hidden markov model in an embodiment of the present invention, and as shown in fig. 1, a power grid development trend prediction method based on a hidden markov model includes:
step 101: acquiring a target index set of a power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes.
The target index types specifically include:
speed and scale of development, safety and quality of development, efficiency and benefit of development, and development of operations and policies;
the basic indexes of development speed and scale specifically include:
GDP acceleration, power supply installation acceleration, load acceleration, variable capacitance load ratio, line capacity load ratio, per-household distribution capacity, line length and interconnection rate;
basic indexes for developing safety and quality specifically include:
n-1 passing rate, potential safety hazard number, power supply reliability, equipment availability factor, voltage qualification rate, equipment average service life, automation coverage rate, cabling rate and intelligent electric meter coverage rate;
the basic indexes of development efficiency and benefit specifically include:
the method comprises the following steps of (1) line maximum load rate, average load rate, transformer substation maximum load rate, line loss rate, per-capita business number, per-capita transformation capacity, per-capita line length, renewable energy access proportion, energy conservation and emission reduction and power grid investment proportion;
the basic indexes for developing operation and policy specifically include:
unit power grid asset power supply load, unit power grid asset power selling amount, net profit, asset liability rate, income growth rate and purchase-sale price difference increment ratio.
Step 102: and carrying out standardization processing on each basic index to obtain a standardized basic index value.
Step 102, specifically comprising:
and judging which type of the basic indexes is to obtain a first judgment result.
{ GDP acceleration rate, power supply installation acceleration rate, load acceleration rate, average household power distribution capacity, line length, interconnection rate, N-1 passing rate, power supply reliability } and the like belong to the maximum indexes; { potential safety hazard number, line loss rate, asset liability rate } and the like belong to extremely small indexes; { variable capacitance-to-load ratio, line capacitance-to-load ratio } and the like belong to qualified indexes.
If the first judgment result is a maximum index, standardizing the basic index according to the following formula:
Figure BDA0002694183290000091
if the first judgment result is an extremely small index, the basic index is standardized according to the following formula:
Figure BDA0002694183290000092
if the first judgment result is a qualified index, the basic index is standardized according to the following formula:
Figure BDA0002694183290000093
in the formula (I), the compound is shown in the specification,
Figure BDA0002694183290000094
the base index value after the normalization is represented,
Figure BDA0002694183290000095
indicating a basic index value before conversion, ξ indicating a safety threshold for a qualified index, i indicating a target index type, j indicating a basic index.
Step 103: and calculating the target index value corresponding to each target index type by adopting a principal component analysis method according to the standardized basic index value.
Step 103, specifically comprising:
selecting standardized basic index values in the same target index type within the full time sequence length to generate a sample matrix; the full-time length is the length between the beginning year and the ending year, the rows of the sample matrix represent the years, and the columns of the sample matrix represent the basic indexes;
calculating a correlation coefficient of each element in the sample matrix, and generating a correlation coefficient matrix according to the correlation coefficient;
solving a plurality of eigenvalues of the correlation coefficient matrix, and arranging all eigenvalues in a descending order;
calculating the variance contribution rate of each characteristic value; the variance contribution rate of the characteristic value is the ratio of the selected characteristic value to the sum of all the characteristic values;
sequentially accumulating the variance contribution rates of one characteristic value from the variance contribution rate corresponding to the maximum characteristic value according to the sequence of the variance contribution rates from large to small, and taking the characteristic value corresponding to the accumulated variance contribution rate as a principal component when the accumulated value of the variance contribution rates exceeds a preset contribution rate value for the first time;
taking the sum of products of the principal component and the variance contribution rate corresponding to the principal component as a target index value;
wherein the content of the first and second substances,
the sample matrix is:
Figure BDA0002694183290000101
wherein X is a sample matrix, t represents year, XtjShowing the normalization of the jth base index in the tth yearThe latter basic index value;
the target index value is:
Ii＝α1λ1+α2λ2+...+αmλm
in the formula IiIs the target index value of the ith target index type, alphamRepresents the variance contribution rate, lambda, corresponding to the mth principal componentmRepresenting the mth principal component.
Step 104: and determining a power grid development dynamic trend characteristic vector corresponding to each target index type according to the target index value.
Step 104, specifically comprising:
the index change rate f is calculated according to the following formula:
f＝Ii(t+1)-Ii(t)
calculating the dynamic trend characteristic RES of the power grid development according to the following formula:
Figure BDA0002694183290000111
wherein the content of the first and second substances,
Figure BDA0002694183290000112
Figure BDA0002694183290000113
in the formula Ii(t +1) represents the target index value of the ith target index type in the t +1 th year, Ii(t) represents the target index value of the ith target index type in the t year, delta t represents the prediction step length, f (x) represents a line graph function of the target index value on a time sequence, g (x) represents a power grid development tie trend function, and x represents a time variable.
And generating a power grid development dynamic trend feature vector according to the index change rate and the power grid development dynamic trend feature.
The power grid development dynamic trend feature vector Q is as follows:
Figure BDA0002694183290000114
in the formula, REStA dynamic trend characteristic representing the grid development in the t year,
Figure BDA0002694183290000115
indicating the index change rate in the t-th year.
Step 105: and acquiring a power grid development trend early warning grade, and determining a membership function model according to the power grid development trend early warning grade.
Step 105, specifically comprising:
determining a membership function model (selecting ridge type membership function model) according to the following formula:
Figure BDA0002694183290000116
Figure BDA0002694183290000121
Figure BDA0002694183290000122
in the formula, when the power grid development trend early warning grade S is 1,2,3, S is 1, the early warning is shown, when S is 2, the secondary early warning is shown, when S is 3, the health is shown, r is1(fet) represents a membership function corresponding to S1, r2(fet) represents a membership function corresponding to S2, r3(fet) represents a membership function corresponding to S3, XK＝{fet1,fet2,...,fetTH, fet represents a column vector of a power grid development dynamic trend characteristic vector Q, a1Denotes a first parameter, a2Representing the second parameter.
Step 106: solving parameters of a membership function model according to the power grid development dynamic trend eigenvector and the optimization model, and generating a membership function according to the parameters obtained by solving; and constructing an optimization model according to the fuzzy entropy.
Step 106, specifically comprising:
set domain XK＝{fet1,fet2,...,fetTState field R ═ R1,r2,r3And then, according to the Fuzzy Entropy (FEI) as a measure of the ambiguity, an optimization model can be established:
establishing an optimization model; the optimization model is as follows:
Figure BDA0002694183290000123
Figure BDA0002694183290000124
wherein u is 1,2,3, r1＝r1(fet)，r2＝r2(fet)，r3＝r3(fet)，H(r1,r2,r3) Represents the optimization function, k represents the total number of target index types, k is 4, s (r)u) Is an intermediate function;
substituting the power grid development dynamic trend eigenvectors into the optimization model and then carrying out optimization operation to obtain parameters of the membership function model;
and generating a membership function according to the parameters of the membership function model.
Step 107: and substituting the power grid development dynamic trend feature vector into the membership function to generate a power grid development dynamic trend membership vector.
Step 108: and generating a membership matrix according to the membership vector of the dynamic trend of the power grid development, and clustering the membership matrix to obtain a clustered membership matrix.
Step 108, specifically comprising:
clustering the membership matrix according to the following formula:
Figure BDA0002694183290000131
wherein E represents a sum of squares, r represents a cluster type, C represents a total number of clusters, and m represents a number classified into CrGrid development dynamic trend membership vector, CrAnd representing a clustering center, wherein M represents a membership matrix, M is a 4 x 3 matrix, and rows of the matrix M represent target index types.
Step 109: and training the hidden Markov model according to the clustered membership matrix to obtain the trained hidden Markov model.
Step 110: and predicting the power grid development trend grade by adopting a Vibity algorithm according to the trained hidden Markov model.
And inputting a sequence to be predicted, and calculating a power grid development trend grade sequence by using a Vibity algorithm.
Analyzing a power grid development weak link by combining the trend grade discrimination result and the index value; the development trend parameters are used for participating in power grid development diagnosis and evaluation work with different meanings, and power grid situation perception and power grid comprehensive evaluation work are achieved in an auxiliary mode.
Fig. 2 is a structural diagram of a power grid development trend prediction system based on a hidden markov model in an embodiment of the present invention. As shown in fig. 2, a power grid development trend prediction system based on hidden markov model includes:
the data acquisition module 201 is used for acquiring a target index set of a power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes.
And the standardization processing module 202 is configured to perform standardization processing on each basic index to obtain a standardized basic index value.
And the principal component analysis module 203 is used for calculating the target index value corresponding to each target index type by adopting a principal component analysis method according to the standardized basic index value.
And the feature vector determination module 204 is configured to determine, according to the target index value, a power grid development dynamic trend feature vector corresponding to each target index type.
And the membership function model establishing module 205 is used for acquiring the power grid development trend early warning grade and determining a membership function model according to the power grid development trend early warning grade.
The membership function generating module 206 is configured to solve parameters of a membership function model according to the grid development dynamic trend eigenvector and the optimization model, and generate a membership function according to the parameters obtained through the solving; and constructing an optimization model according to the fuzzy entropy.
And the membership degree vector generating module 207 is used for substituting the power grid development dynamic trend feature vector into a membership degree function to generate a power grid development dynamic trend membership degree vector.
And the clustering module 208 is configured to generate a membership matrix according to the power grid development dynamic trend membership vector, and perform clustering on the membership matrix to obtain a clustered membership matrix.
And the hidden Markov model training module 209 is used for training the hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model.
And the power grid development trend grade prediction module 210 is used for predicting the power grid development trend grade by adopting a Vibity algorithm according to the trained hidden Markov model.
For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The power grid development trend prediction method and system based on the hidden Markov model can evaluate the future development trend of a power grid based on the current basic indexes and models, and provide effective support for a power grid department to develop power grid planning in advance and deal with weak links.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In summary, this summary should not be construed to limit the present invention.

Claims (9)

1. A power grid development trend prediction method is characterized by comprising the following steps:
acquiring a target index set of a power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes;
carrying out standardization processing on each basic index to obtain a standardized basic index value;
calculating a target index value corresponding to each target index type by adopting a principal component analysis method according to the standardized basic index value;
determining a power grid development dynamic trend characteristic vector corresponding to each target index type according to the target index values;
acquiring a power grid development trend early warning grade, and determining a membership function model according to the power grid development trend early warning grade;
solving parameters of the membership function model according to the power grid development dynamic trend eigenvector and the optimization model, and generating a membership function according to the solved parameters; the optimization model is constructed according to the fuzzy entropy;
substituting the power grid development dynamic trend feature vector into the membership function to generate a power grid development dynamic trend membership vector;
generating a membership matrix according to the power grid development dynamic trend membership vector, and clustering the membership matrix to obtain a clustered membership matrix;
training a hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model;
and predicting the power grid development trend grade by adopting a Vibitry algorithm according to the trained hidden Markov model.
2. The grid development trend prediction method according to claim 1,
the target index type specifically includes:
speed and scale of development, safety and quality of development, efficiency and benefit of development, and development of operations and policies;
the basic indexes of the development speed and the scale specifically comprise:
GDP acceleration, power supply installation acceleration, load acceleration, variable capacitance load ratio, line capacity load ratio, per-household distribution capacity, line length and interconnection rate;
the basic indexes for developing safety and quality specifically include:
n-1 passing rate, potential safety hazard number, power supply reliability, equipment availability factor, voltage qualification rate, equipment average service life, automation coverage rate, cabling rate and intelligent electric meter coverage rate;
the basic indexes of development efficiency and benefit specifically include:
the method comprises the following steps of (1) line maximum load rate, average load rate, transformer substation maximum load rate, line loss rate, per-capita business number, per-capita transformation capacity, per-capita line length, renewable energy access proportion, energy conservation and emission reduction and power grid investment proportion;
the basic indexes for developing the operation and policy specifically include:
unit power grid asset power supply load, unit power grid asset power selling amount, net profit, asset liability rate, income growth rate and purchase-sale price difference increment ratio.
3. The method according to claim 1, wherein the step of normalizing each basic index to obtain a normalized basic index value specifically includes:
judging which type of the basic indexes is to obtain a first judgment result;
if the first judgment result is a maximum index, standardizing the basic index according to the following formula:
Figure FDA0002694183280000025
if the first judgment result is an extremely small index, standardizing the basic index according to the following formula:
Figure FDA0002694183280000021
if the first judgment result is a qualified index, standardizing the basic index according to the following formula:
Figure FDA0002694183280000022
in the formula (I), the compound is shown in the specification,
Figure FDA0002694183280000023
the base index value after the normalization is represented,
Figure FDA0002694183280000024
indicating a basic index value before conversion, ξ indicating a safety threshold for a qualified index, i indicating a target index type, j indicating a basic index.
4. The method according to claim 3, wherein the calculating of the target index value corresponding to each target index type by using a principal component analysis method according to the standardized basic index value specifically includes:
selecting standardized basic index values in the same target index type within the full time sequence length to generate a sample matrix; the full-time length is the length between the beginning year and the ending year, the rows of the sample matrix represent the years, and the columns of the sample matrix represent the basic indexes;
calculating a correlation coefficient of each element in the sample matrix, and generating a correlation coefficient matrix according to the correlation coefficient;
solving a plurality of eigenvalues of the correlation coefficient matrix, and arranging all eigenvalues in descending order;
calculating the variance contribution rate of each characteristic value; the variance contribution rate of the characteristic value is the ratio of the selected characteristic value to the sum of all the characteristic values;
sequentially accumulating the variance contribution rates of one characteristic value from the variance contribution rate corresponding to the maximum characteristic value according to the sequence of the variance contribution rates from large to small, and taking the characteristic value corresponding to the accumulated variance contribution rate as a principal component when the accumulated value of the variance contribution rates exceeds a preset contribution rate value for the first time;
taking the sum of products of the principal component and the variance contribution rate corresponding to the principal component as a target index value;
wherein the content of the first and second substances,
the sample matrix is:
Figure FDA0002694183280000031
wherein X is a sample matrix, t represents year, XtjA normalized base index value representing a jth base index in the tth year;
the target index value is:
Ii＝α1λ1+α2λ2+...+αmλm
in the formula IiIs the target index value of the ith target index type, alphamRepresents the variance contribution rate, lambda, corresponding to the mth principal componentmRepresenting the mth principal component.
5. The power grid development trend prediction method according to claim 4, wherein the determining, according to the target index values, a power grid development dynamic trend feature vector corresponding to each target index type specifically includes:
the index change rate f is calculated according to the following formula:
f＝Ii(t+1)-Ii(t)
calculating the dynamic trend characteristic RES of the power grid development according to the following formula:
Figure FDA0002694183280000041
wherein the content of the first and second substances,
Figure FDA0002694183280000042
Figure FDA0002694183280000043
in the formula Ii(t +1) represents the target index value of the ith target index type in the t +1 th year, Ii(t) a target index value of an ith target index type in the t year, delta t represents a prediction step length, f (x) represents a line graph function of the target index value on a time sequence, g (x) represents a power grid development tie trend function, and x represents a time variable;
generating a power grid development dynamic trend feature vector according to the index change rate and the power grid development dynamic trend feature;
the power grid development dynamic trend feature vector Q is as follows:
Figure FDA0002694183280000044
in the formula, REStA dynamic trend characteristic representing the grid development in the t year,
Figure FDA0002694183280000045
indicating the index change rate in the t-th year.
6. The power grid development trend prediction method according to claim 5, wherein the determining a membership function model according to the power grid development trend early warning level specifically comprises:
determining a membership function model according to the following formula:
Figure FDA0002694183280000046
Figure FDA0002694183280000047
Figure FDA0002694183280000051
in the formula, when the power grid development trend early warning grade S is 1,2,3, S is 1, the early warning is shown, when S is 2, the secondary early warning is shown, when S is 3, the health is shown, r is1(fet) represents a membership function corresponding to S1, r2(fet) represents a membership function corresponding to S2, r3(fet) represents a membership function corresponding to S ═ 3, and Q ═ fet1,fet2,...,fettH, fet represents a column vector of a power grid development dynamic trend characteristic vector Q, a1Denotes a first parameter, a2Representing the second parameter.
7. The power grid development trend prediction method according to claim 6, wherein solving parameters of the membership function model according to the power grid development dynamic trend eigenvector and the optimization model, and generating a membership function according to the solved parameters specifically comprises:
establishing an optimization model; the optimization model is as follows:
Figure FDA0002694183280000052
Figure FDA0002694183280000053
wherein u is 1,2,3, r1＝r1(fet)，r2＝r2(fet)，r3＝r3(fet)，H(r1,r2,r3) Represents the optimization function, k represents the total number of target index types, k is 4, s (r)u) Is an intermediate function;
substituting the power grid development dynamic trend eigenvector into the optimization model and then carrying out optimization operation to obtain parameters of the membership function model;
and generating a membership function according to the parameters of the membership function model.
8. The power grid development trend prediction method according to claim 7, wherein the clustering the membership matrix to obtain a clustered membership matrix specifically comprises:
clustering the membership matrix according to the following formula:
Figure FDA0002694183280000054
wherein E represents a sum of squares, r represents a cluster type, C represents a total number of clusters, and m represents a number classified into CrGrid development dynamic trend membership vector, CrRepresenting the cluster center and M representing the membership matrix.
9. A power grid development trend prediction system, comprising:
the data acquisition module is used for acquiring a target index set of the power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes;
the standardization processing module is used for carrying out standardization processing on each basic index to obtain a standardized basic index value;
the principal component analysis module is used for calculating a target index value corresponding to each target index type by adopting a principal component analysis method according to the standardized basic index value;
the characteristic vector determination module is used for determining a power grid development dynamic trend characteristic vector corresponding to each target index type according to the target index values;
the membership function model establishing module is used for acquiring a power grid development trend early warning grade and determining a membership function model according to the power grid development trend early warning grade;
the membership function generation module is used for solving parameters of the membership function model according to the power grid development dynamic trend characteristic vector and the optimization model and generating a membership function according to the solved parameters; the optimization model is constructed according to the fuzzy entropy;
the membership degree vector generation module is used for substituting the power grid development dynamic trend feature vector into the membership degree function to generate a power grid development dynamic trend membership degree vector;
the clustering module is used for generating a membership matrix according to the power grid development dynamic trend membership vector and clustering the membership matrix to obtain a clustered membership matrix;
the hidden Markov model training module is used for training the hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model;
and the power grid development trend grade prediction module is used for predicting the power grid development trend grade by adopting a Vibity algorithm according to the trained hidden Markov model.
CN202011000648.0A 2020-09-22 2020-09-22 Power grid development trend prediction method and system based on hidden Markov model Active CN112101673B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011000648.0A CN112101673B (en) 2020-09-22 2020-09-22 Power grid development trend prediction method and system based on hidden Markov model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011000648.0A CN112101673B (en) 2020-09-22 2020-09-22 Power grid development trend prediction method and system based on hidden Markov model

Publications (2)

Publication Number Publication Date
CN112101673A true CN112101673A (en) 2020-12-18
CN112101673B CN112101673B (en) 2024-01-16

Family

ID=73754825

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011000648.0A Active CN112101673B (en) 2020-09-22 2020-09-22 Power grid development trend prediction method and system based on hidden Markov model

Country Status (1)

Country Link
CN (1) CN112101673B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113705874A (en) * 2021-08-19 2021-11-26 国家电网有限公司 New energy power grid evolution prediction method and device, computer equipment and storage medium
CN116757337A (en) * 2023-08-18 2023-09-15 克拉玛依市鼎泰建设(集团)有限公司 House construction progress prediction system based on artificial intelligence

Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103326903A (en) * 2013-07-05 2013-09-25 华北电力大学 Hidden-Markov-based Internet network delay forecasting method
WO2013174146A1 (en) * 2012-05-23 2013-11-28 国家电网公司 Optimized evaluation method for wind power output power under multi-constraint condition for reducing transmission loss
CN105976257A (en) * 2015-12-17 2016-09-28 国家电网公司 Power grid vulnerability evaluation method based on membership function fuzzy comprehensive evaluation method
CN106372799A (en) * 2016-08-31 2017-02-01 全球能源互联网研究院 Power grid security risk prediction method
US20170068760A1 (en) * 2015-09-04 2017-03-09 Panasonic Intellectual Property Corporation Of America Method for estimating use state of power of electric devices
CN106709821A (en) * 2017-03-10 2017-05-24 东北电力大学 Main component analysis-based power supply reliability evaluation method of medium-voltage power distribution network
CN107274047A (en) * 2017-04-24 2017-10-20 国网江西省电力公司赣州供电分公司 A kind of power network method of post project evaluation based on multilevel extension assessment method
US20170351288A1 (en) * 2016-06-07 2017-12-07 Shenzhen Zhidian Energy Technology LTD Non-invasive online real-time electric load identification method and identification system
CN108154256A (en) * 2017-11-27 2018-06-12 国网北京市电力公司 The determining method and device of forecasting risk value, storage medium
CN109902265A (en) * 2019-02-28 2019-06-18 西南石油大学 A kind of underground method for early warning based on hidden Markov model
CN110336768A (en) * 2019-01-22 2019-10-15 西北大学 A kind of Tendency Prediction method based on joint hidden Markov model and genetic algorithm
CN111027732A (en) * 2018-10-09 2020-04-17 中国电力科学研究院有限公司 Method and system for generating multi-wind-farm output scene
US20200209841A1 (en) * 2018-12-27 2020-07-02 Utopus Insights, Inc. System and method for proactive handling of multiple faults and failure modes in an electrical network of energy assets
CN111582571A (en) * 2020-04-30 2020-08-25 中国电力科学研究院有限公司 Power grid operation situation sensing method and system with model driving and data driving integrated

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013174146A1 (en) * 2012-05-23 2013-11-28 国家电网公司 Optimized evaluation method for wind power output power under multi-constraint condition for reducing transmission loss
CN103326903A (en) * 2013-07-05 2013-09-25 华北电力大学 Hidden-Markov-based Internet network delay forecasting method
US20170068760A1 (en) * 2015-09-04 2017-03-09 Panasonic Intellectual Property Corporation Of America Method for estimating use state of power of electric devices
CN105976257A (en) * 2015-12-17 2016-09-28 国家电网公司 Power grid vulnerability evaluation method based on membership function fuzzy comprehensive evaluation method
US20170351288A1 (en) * 2016-06-07 2017-12-07 Shenzhen Zhidian Energy Technology LTD Non-invasive online real-time electric load identification method and identification system
CN106372799A (en) * 2016-08-31 2017-02-01 全球能源互联网研究院 Power grid security risk prediction method
CN106709821A (en) * 2017-03-10 2017-05-24 东北电力大学 Main component analysis-based power supply reliability evaluation method of medium-voltage power distribution network
CN107274047A (en) * 2017-04-24 2017-10-20 国网江西省电力公司赣州供电分公司 A kind of power network method of post project evaluation based on multilevel extension assessment method
CN108154256A (en) * 2017-11-27 2018-06-12 国网北京市电力公司 The determining method and device of forecasting risk value, storage medium
CN111027732A (en) * 2018-10-09 2020-04-17 中国电力科学研究院有限公司 Method and system for generating multi-wind-farm output scene
US20200209841A1 (en) * 2018-12-27 2020-07-02 Utopus Insights, Inc. System and method for proactive handling of multiple faults and failure modes in an electrical network of energy assets
CN110336768A (en) * 2019-01-22 2019-10-15 西北大学 A kind of Tendency Prediction method based on joint hidden Markov model and genetic algorithm
CN109902265A (en) * 2019-02-28 2019-06-18 西南石油大学 A kind of underground method for early warning based on hidden Markov model
CN111582571A (en) * 2020-04-30 2020-08-25 中国电力科学研究院有限公司 Power grid operation situation sensing method and system with model driving and data driving integrated

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
ALAN CARLIN 等: "Higher Automated Learning through Principal Component Analysis and Markov Models", ARTIFICIAL INTELLIGENCE IN EDUCATION, pages 661 - 665 *
DATAANATOLI PAUL ULMEANU 等: "Hidden Markov Models revealing the household thermal profiling from smart meter data", ENERGY AND BUILDINGS, vol. 154, pages 127 - 140, XP085209274, DOI: 10.1016/j.enbuild.2017.08.036 *
INÊS L. ROÇA 等: "Solving Ill-Conditioned State-Estimation Problems in Distribution Grids With Hidden-Markov Models of Load Dynamics", IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 35, no. 1, pages 1 - 9 *
秦欢 等: "基于隐马尔科夫和主成分分析的电网数据词典构建", 电力大数据, vol. 22, no. 1, pages 16 - 21 *
艾欣 等: "电网发展诊断的改进模糊评价模型与评级", 中国电力, vol. 55, no. 5, pages 66 - 75 *
郑敏嘉;卢洵;程鑫;: "基于HMM模型的电力负荷预测模型研究", 机电工程技术, no. 11, pages 170 - 173 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113705874A (en) * 2021-08-19 2021-11-26 国家电网有限公司 New energy power grid evolution prediction method and device, computer equipment and storage medium
CN113705874B (en) * 2021-08-19 2024-03-15 国家电网有限公司 New energy power grid evolution prediction method, device, computer equipment and storage medium
CN116757337A (en) * 2023-08-18 2023-09-15 克拉玛依市鼎泰建设(集团)有限公司 House construction progress prediction system based on artificial intelligence
CN116757337B (en) * 2023-08-18 2023-11-21 克拉玛依市鼎泰建设(集团)有限公司 House construction progress prediction system based on artificial intelligence

Also Published As

Publication number Publication date
CN112101673B (en) 2024-01-16

Similar Documents

Publication Publication Date Title
CN110232203B (en) Knowledge distillation optimization RNN short-term power failure prediction method, storage medium and equipment
CN105117602B (en) A kind of metering device running status method for early warning
CN110097297A (en) A kind of various dimensions stealing situation Intellisense method, system, equipment and medium
CN107506868B (en) Method and device for predicting short-time power load
CN111080072A (en) Distribution transformer health index evaluation method, device and system
CN113408869A (en) Power distribution network construction target risk assessment method
CN110837915B (en) Low-voltage load point prediction and probability prediction method for power system based on hybrid integrated deep learning
CN104331773A (en) Comprehensive assessment method for power network planning schemes
CN112308427A (en) New energy consumption restriction factor evaluation method and system based on combined empowerment-grey correlation
CN112101673A (en) Power grid development trend prediction method and system based on hidden Markov model
CN114325395A (en) Method and device for determining battery state
CN110298369A (en) A kind of discrimination method and system of electric system bad data
CN112001644A (en) Power distribution network operation reliability detection method, device, terminal and storage medium
CN113298318A (en) Novel overload prediction method for distribution transformer
CN115856756A (en) Fault assessment method for electric energy metering box
CN113591322A (en) Low-voltage transformer area line loss rate prediction method based on extreme gradient lifting decision tree
CN107274025B (en) System and method for realizing intelligent identification and management of power consumption mode
CN112508254A (en) Method for determining investment prediction data of transformer substation engineering project
Zhang et al. User power interaction behavior clustering analysis that is based on the self-organizing-center K-means algorithm
CN111797924B (en) Three-dimensional garden portrait method and system based on clustering algorithm
CN115965119A (en) Method for power prediction optimization of distributed energy storage system
CN115146715A (en) Power utilization potential safety hazard diagnosis method, device, equipment and storage medium
CN114118587A (en) Electric energy quality evaluation method, system, equipment and storage medium for distributed photovoltaic
CN113919599A (en) Medium-and-long-term load prediction method
CN114266593A (en) Power consumption prediction method based on KPCA and linear regression

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant