CN114936683A - Power grid bus load analysis and prediction assessment management method, device and system - Google Patents

Power grid bus load analysis and prediction assessment management method, device and system Download PDF

Info

Publication number
CN114936683A
CN114936683A CN202210506221.0A CN202210506221A CN114936683A CN 114936683 A CN114936683 A CN 114936683A CN 202210506221 A CN202210506221 A CN 202210506221A CN 114936683 A CN114936683 A CN 114936683A
Authority
CN
China
Prior art keywords
load
prediction
analysis
power grid
grid bus
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.)
Pending
Application number
CN202210506221.0A
Other languages
Chinese (zh)
Inventor
薛少华
方磊
马启霞
刘一峰
李会军
杨霄
金萍
丁永瀚
敖园明
李世昭
芦兴
邹雅
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.)
NARI Nanjing Control System Co Ltd
State Grid Ningxia Electric Power Co Ltd
Original Assignee
NARI Nanjing Control System Co Ltd
State Grid Ningxia 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 NARI Nanjing Control System Co Ltd, State Grid Ningxia Electric Power Co Ltd filed Critical NARI Nanjing Control System Co Ltd
Priority to CN202210506221.0A priority Critical patent/CN114936683A/en
Publication of CN114936683A publication Critical patent/CN114936683A/en
Pending legal-status Critical Current

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
    • 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
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Power Engineering (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a method, a device and a system for analyzing, predicting, examining and managing a load of a power grid bus, wherein the method comprises the steps of obtaining load data of the power grid bus, and analyzing load characteristics and stability; and based on the load data, combining different load prediction methods to carry out load prediction, and reporting the load prediction results of the different load prediction methods to the provincial dispatching side so that the provincial dispatching side carries out overall analysis and display based on the received load prediction results. The invention realizes the analysis of the bus load of the power grid and the diversified display of the prediction result, improves the prediction precision of the bus load, promotes the lean management level and improves the working efficiency.

Description

Power grid bus load analysis and prediction assessment management method, device and system
Technical Field
The invention belongs to the technical field of power dispatching automation, and particularly relates to a power grid bus load analysis and prediction assessment management method, device and system.
Background
In recent years, along with the adjustment of economic structures and the implementation of energy-saving and emission-reducing policies, the electricity utilization characteristics of various users are greatly changed, and new attributes and rules of power loads are generated. The power supply and demand are greatly changed in the year, and are unbalanced in time period and region in the year, and the power consumption quality requirements of users are increasing day by day; particularly, the implementation of energy-saving power generation dispatching makes the tasks of safe, stable and economic operation and reliable power supply of a power grid very difficult. Because the electric energy can not be stored in a large quantity, the generated power is required to track the load change at any time to balance supply and demand, scientific load prediction is the basis and guarantee for decision making, and power load prediction is an important guarantee for the safe, efficient and economic operation of a power grid. The load prediction is used as the basis of power dispatching and power generation plans, and the load prediction must be accurate and credible to ensure that reliable and sufficient power supply is provided for power users.
Disclosure of Invention
Aiming at the problems, the invention provides a power grid bus load analysis and prediction examination management method, device and system, which realize diversified display of power grid bus load analysis and prediction results, improve bus load prediction precision, improve lean management level and improve working efficiency.
In order to achieve the technical purpose and achieve the technical effects, the invention is realized by the following technical scheme:
in a first aspect, the invention provides a power grid bus load analysis and prediction assessment management method, which comprises the following steps:
acquiring load data of a power grid bus, and performing load characteristic analysis and stability analysis;
and based on the load data, combining different load prediction methods to carry out load prediction, and reporting the load prediction results of the different load prediction methods to the provincial dispatching side so that the provincial dispatching side carries out overall analysis and display based on the received load prediction results.
Optionally, the method for analyzing, predicting, assessing and managing the load of the power grid bus further includes:
and acquiring meteorological information, and calculating a correlation coefficient of the load and the meteorological information by using a grey correlation analysis method based on the load data and the meteorological information to complete meteorological correlation analysis.
Optionally, the load characteristic analysis comprises the steps of:
and (5) carrying out daily, weekly, monthly and annual load characteristic analysis to find out the change rule and the influence factors of the load.
Optionally, the stability analysis comprises the steps of:
and analyzing the historical load data by using a time sequence frequency domain analysis method to obtain an upper limit estimation value and a lower limit estimation value of the stability of the historical load.
Optionally, the method for obtaining the upper limit estimation value and the lower limit estimation value of the historical load stability includes:
for specified modeling time D - After performing fourier decomposition on the load time series p (t), reconstructing the load time series p (t) to obtain:
P(t)＝a 0 +D(t)+W(t)+L(t)+H(t)
wherein, a 0 + D (t) is the daily component, W (t) is the weekly component, a 0 + d (t) and w (t) are load components varying at a fixed period, h (t) is a high-frequency load component reflecting random fluctuations of the power load, and l (t) is a low-frequency residual load component;
separating high-frequency components in the time series to estimate the upper limit of the stability of the historical load:
Figure BDA0003637446620000021
wherein P (t) is the original load sequence, H (t) is the high frequency load component;
the lower limit estimation value for estimating the stability of the historical load by separating low-frequency components in the time series is as follows:
Figure BDA0003637446620000022
wherein P (t) is the original payload sequence, and L (t) is the separated payload sequence.
Optionally, the load prediction method includes a time series method, an artificial neural network method, a similar day method, a smoothing coefficient method, and/or a comprehensive prediction method.
Optionally, the orchestration analysis comprises the steps of:
according to the load prediction result, the prediction error is analyzed by combining the actual load curve, and a corresponding prediction error curve is obtained;
sequencing, comparing and analyzing prediction errors obtained by different load prediction methods to obtain the accuracy of each prediction method, selecting the load prediction method with the highest accuracy as a final load prediction method, and reserving a load prediction result corresponding to the load prediction method;
setting a predicted qualified threshold value, and analyzing and predicting the qualified rate;
and (4) counting all the checked equipment to obtain the number of the missing loads, and analyzing the predicted integrity.
In a second aspect, the present invention provides a power grid bus load analysis and prediction assessment management device, including:
the first analysis module is used for acquiring load data of a power grid bus and performing load characteristic analysis and stability analysis;
and the prediction assessment management module is used for developing load prediction by combining different load prediction methods based on the load data, and reporting the load prediction results of the different load prediction methods to the provincial dispatching side so as to enable the provincial dispatching side to carry out overall analysis and display based on the received load prediction results.
Optionally, the power grid bus load analysis and prediction assessment management device further includes:
and the second analysis module is used for acquiring meteorological information, calculating a correlation coefficient of the load and the meteorological information by using a grey correlation analysis method based on the load data and the meteorological information, and completing meteorological correlation analysis.
In a third aspect, the invention provides a power grid bus load analysis and prediction assessment management system, which comprises a processor and a storage medium;
the storage medium is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of the first aspects.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of acquiring a power grid real-time operation model and section information data on line, analyzing various extracted basic data, periodically analyzing load data, calculating the stability condition of historical loads, analyzing the correlation between the loads and meteorological information, considering information such as the historical loads and the like, carrying out load prediction adaptive training, and analyzing prediction errors by combining an actual load curve according to a prediction result to obtain a corresponding prediction error curve; sequencing, comparing and analyzing prediction errors obtained by different load prediction models to obtain the accuracy condition of the prediction models; setting a predicted qualification threshold value, and analyzing the predicted qualification rate; and (4) counting all the checked devices to obtain the number of the missing loads, and analyzing and predicting the integrity rate.
Drawings
In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the present disclosure taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic flow chart of a power grid bus load analysis and prediction assessment management system method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
Example 1
The embodiment of the invention provides a power grid bus load analysis and prediction assessment management method, which comprises the following steps:
(1) acquiring load data of a power grid bus, and performing load characteristic analysis and stability analysis; preferably, before analysis, abnormal data identification and correction need to be performed on the acquired load data, and the abnormal data identification and correction can be realized by adopting the prior art;
(2) based on the load data, combining different load prediction methods to carry out load prediction, and reporting the load prediction results of the different load prediction methods to the provincial dispatching side so that the provincial dispatching side carries out overall analysis and display based on the received load prediction results; in the specific implementation process, different load prediction methods exist in the form of a load prediction model library.
In a specific implementation manner of the embodiment of the present invention, the method for analyzing, predicting, assessing and managing the load of the power grid bus further includes:
and acquiring meteorological information, and calculating a correlation coefficient of the load and the meteorological information by using a grey correlation analysis method based on the load data and the meteorological information to finish meteorological correlation analysis. Preferably, before analysis, anomaly data identification and correction are required to be carried out on the acquired meteorological information, and the anomaly data identification and correction can be realized by adopting the prior art.
In a specific implementation manner of the embodiment of the present invention, the load characteristic analysis includes the following steps:
and (5) carrying out daily, weekly, monthly and annual load characteristic analysis to find out the change rule and the influence factors of the load. In a specific implementation process, the load characteristic analysis includes: day, week, month, year maximum minimum load value/average/load curve/peak-to-valley difference/load rate (average load to maximum load percentage)/minimum compliance rate (minimum load to maximum load ratio).
In a specific implementation manner of the embodiment of the present invention, the stability analysis includes the following steps:
and analyzing the historical load data by using a time sequence frequency domain analysis method to obtain an upper limit estimation value and a lower limit estimation value of the stability of the historical load. In a specific implementation process, the method for obtaining the upper limit estimation value and the lower limit estimation value of the historical load stability includes:
for specified modeling time D - The load time sequence P (t) is subjected to Fourier decomposition and is reconstructed to obtain:
P(t)＝a 0 +D(t)+W(t)+L(t)+H(t)
wherein, a 0 + D (t) is the daily component, W (t) is the weekly component, a 0 + D (t) and W (t) are load components varying in a fixed period, H (t) is a high frequency load component reflecting random fluctuations of the power load, L (t) is a low frequency residualA load component;
separating high-frequency components in the time series to estimate the upper limit of the stability of the historical load:
Figure BDA0003637446620000041
wherein P (t) is the original load sequence, H (t) is the high frequency load component;
the lower limit estimation value for estimating the stability of the historical load by separating low-frequency components in the time series is as follows:
Figure BDA0003637446620000042
wherein P (t) is the original payload sequence, and L (t) is the separated payload sequence.
In a specific implementation manner of the embodiment of the present invention, the load prediction method includes a time series method, an artificial neural network method, a similar day method, a smoothing coefficient method and/or a comprehensive prediction method.
In a specific implementation manner of the embodiment of the present invention, the overall analysis includes the following steps:
according to the load prediction result, the prediction error is analyzed by combining the actual load curve to obtain a corresponding prediction error curve;
sequencing, comparing and analyzing the prediction errors obtained by different load prediction methods to obtain the accuracy of each prediction method; selecting a load prediction method with the highest accuracy as a final load prediction method, and reserving a load prediction result corresponding to the load prediction method;
setting a predicted qualified threshold value, and analyzing and predicting the qualified rate;
and (4) counting all the checked equipment to obtain the number of the missing loads, and analyzing the predicted integrity.
Example 2
Based on the same inventive concept as embodiment 1, the embodiment of the present invention provides a power grid bus load analysis and prediction assessment management device, including:
the first analysis module is used for acquiring load data of the power grid bus and performing load characteristic analysis and stability analysis;
the forecasting, examining and managing module is used for developing load forecasting by considering the load data and combining different load forecasting methods, and reporting the load forecasting results of the different load forecasting methods to the provincial dispatching side so as to ensure that the provincial dispatching side carries out overall analysis based on the received load forecasting results; in the specific implementation process, different load prediction methods exist in the form of a load prediction model library.
In a specific implementation manner of the embodiment of the present invention, the power grid bus load analysis and prediction assessment management apparatus further includes:
and the second analysis module is used for acquiring meteorological information, and calculating a correlation coefficient of the load and the meteorological information by using a grey correlation analysis method based on the load data and the meteorological information to finish meteorological correlation analysis.
In a specific implementation manner of the embodiment of the present invention, the load characteristic analysis includes the following steps:
and (5) carrying out daily, weekly, monthly and annual load characteristic analysis to find out the change rule and the influence factors of the load. In a specific implementation process, the load characteristic analysis includes: day, week, month, year maximum minimum load value/average/load curve/peak-to-valley difference/load rate (average load to maximum load percentage)/minimum compliance rate (minimum load to maximum load ratio).
In a specific implementation manner of the embodiment of the present invention, the stability analysis includes the following steps:
and analyzing historical load data by using a time sequence frequency domain analysis method to obtain an upper limit estimated value and a lower limit estimated value of the stability of the historical load. In a specific implementation process, the method for obtaining the upper limit estimation value and the lower limit estimation value of the historical load stability comprises the following steps:
for specified modeling time D - After Fourier decomposition, the load time sequence P (t) is reconstructed to obtain:
P(t)＝a 0 +D(t)+W(t)+L(t)+H(t)
wherein, a 0 + D (t) is the daily component, W (t) is the weekly component, a 0 + d (t) and w (t) are load components that vary at fixed periods, h (t) is a high frequency load component that reflects random fluctuations in the power load, and l (t) is a low frequency residual load component;
separating high-frequency components in the time series to estimate the upper limit of the stability of the historical load:
Figure BDA0003637446620000061
wherein P (t) is the original load sequence, H (t) is the high frequency load component;
the lower limit estimation value for estimating the stability of the historical load by separating low-frequency components in the time series is as follows:
Figure BDA0003637446620000062
wherein P (t) is the original payload sequence, and L (t) is the separated payload sequence.
In a specific implementation manner of the embodiment of the present invention, the load prediction method includes a time series method, an artificial neural network method, a similar day method, a smoothing coefficient method, and/or a comprehensive prediction method.
In a specific implementation manner of the embodiment of the present invention, the overall analysis includes the following steps:
according to the load prediction result, the prediction error is analyzed by combining the actual load curve to obtain a corresponding prediction error curve;
sequencing, comparing and analyzing the prediction errors obtained by different load prediction methods to obtain the accuracy of each prediction method;
setting a predicted qualified threshold value, and analyzing the predicted qualified rate;
and (4) counting all the checked equipment to obtain the missing load number, and analyzing the prediction integrity rate.
Furthermore, after the overall analysis is completed, the provincial side and the dispatching side can display the overall analysis result.
Example 3
Based on the same inventive concept as the embodiment 1, the embodiment of the invention provides a power grid bus load analysis and prediction assessment management system, which comprises a processor and a storage medium, wherein the processor is used for processing the power grid bus load analysis and prediction assessment management;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any of embodiment 1.
In summary, the invention analyzes various extracted basic data by acquiring a power grid real-time operation model and section information data on line, periodically analyzes load data, calculates the stability condition of historical load, simultaneously analyzes the correlation between the load and meteorological information, considers information such as historical load and the like, carries out load prediction adaptive training, and analyzes prediction errors by combining an actual load curve according to a prediction result to obtain a corresponding prediction error curve; sequencing, comparing and analyzing prediction errors obtained by different load prediction models to obtain the accuracy condition of the prediction models; setting a predicted qualification threshold value, and analyzing the predicted qualification rate; and (4) counting all the checked devices to obtain the number of the missing loads, and analyzing and predicting the integrity rate.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A power grid bus load analysis and prediction assessment management method is characterized by comprising the following steps:
acquiring load data of a power grid bus, and performing load characteristic analysis and stability analysis;
and based on the load data, combining different load prediction methods to carry out load prediction, and reporting the load prediction results of the different load prediction methods to the provincial dispatching side so that the provincial dispatching side carries out overall analysis and display based on the received load prediction results.
2. The power grid bus load analysis and prediction assessment management method according to claim 1, characterized in that: the power grid bus load analysis and prediction assessment management method further comprises the following steps:
and acquiring meteorological information, and calculating a correlation coefficient of the load and the meteorological information by using a grey correlation analysis method based on the load data and the meteorological information to complete meteorological correlation analysis.
3. The power grid bus load analysis and prediction assessment management method according to claim 1, characterized in that: the load characteristic analysis comprises the following steps:
and (5) carrying out daily, weekly, monthly and annual load characteristic analysis to find out the change rule and the influence factors of the load.
4. The power grid bus load analysis and prediction assessment management method according to claim 1, characterized in that: the stability analysis comprises the following steps:
and analyzing the historical load data by using a time sequence frequency domain analysis method to obtain an upper limit estimation value and a lower limit estimation value of the stability of the historical load.
5. The power grid bus load analysis and prediction assessment management method according to claim 1, characterized in that: the method for obtaining the upper limit estimation value and the lower limit estimation value of the historical load stability comprises the following steps:
for specified modeling time D - After performing fourier decomposition on the load time series p (t), reconstructing the load time series p (t) to obtain:
P(t)＝a 0 +D(t)+W(t)+L(t)+H(t)
wherein, a 0 + D (t) is the daily period component, W (t) is the period component, a 0 + d (t) and w (t) are load components that vary at fixed periods, h (t) is a high frequency load component that reflects random fluctuations in the power load, and l (t) is a low frequency residual load component;
separating high-frequency components in the time series to estimate the upper limit of the stability of the historical load:
Figure FDA0003637446610000011
wherein P (t) is the original load sequence, H (t) is the high frequency load component;
the lower limit estimation value for estimating the stability of the historical load by separating low-frequency components in the time series is as follows:
Figure FDA0003637446610000021
wherein P (t) is the original payload sequence, and L (t) is the separated payload sequence.
6. The power grid bus load analysis and prediction assessment management method according to claim 1, characterized in that: the load prediction method comprises a time sequence method, an artificial neural network method, a similar day method, a smooth coefficient method and a comprehensive prediction method.
7. The power grid bus load analysis and prediction assessment management method according to claim 1, characterized in that: the overall analysis comprises the following steps:
according to the load prediction result, the prediction error is analyzed by combining the actual load curve to obtain a corresponding prediction error curve;
sequencing, comparing and analyzing prediction errors obtained by different load prediction methods to obtain the accuracy of each prediction method, selecting the load prediction method with the highest accuracy as a final load prediction method, and reserving a load prediction result corresponding to the load prediction method;
setting a predicted qualified threshold value, and analyzing and predicting the qualified rate;
and (4) counting all the checked equipment to obtain the missing load number, and analyzing the prediction integrity rate.
8. A power grid bus load analysis and prediction examination management device is characterized by comprising:
the first analysis module is used for acquiring load data of a power grid bus and performing load characteristic analysis and stability analysis;
and the prediction assessment management module is used for developing load prediction by combining different load prediction methods based on the load data, and reporting the load prediction results of the different load prediction methods to the provincial dispatching side so as to enable the provincial dispatching side to carry out overall analysis and display based on the received load prediction results.
9. The power grid bus load analysis and prediction assessment management device according to claim 8, further comprising:
and the second analysis module is used for acquiring meteorological information, calculating a correlation coefficient of the load and the meteorological information by using a grey correlation analysis method based on the load data and the meteorological information, and completing meteorological correlation analysis.
10. A power grid bus load analysis and prediction examination management system is characterized in that: comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1-7.
CN202210506221.0A 2022-05-11 2022-05-11 Power grid bus load analysis and prediction assessment management method, device and system Pending CN114936683A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210506221.0A CN114936683A (en) 2022-05-11 2022-05-11 Power grid bus load analysis and prediction assessment management method, device and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210506221.0A CN114936683A (en) 2022-05-11 2022-05-11 Power grid bus load analysis and prediction assessment management method, device and system

Publications (1)

Publication Number Publication Date
CN114936683A true CN114936683A (en) 2022-08-23

Family

ID=82865213

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210506221.0A Pending CN114936683A (en) 2022-05-11 2022-05-11 Power grid bus load analysis and prediction assessment management method, device and system

Country Status (1)

Country Link
CN (1) CN114936683A (en)

Similar Documents

Publication Publication Date Title
CN110610280B (en) Short-term prediction method, model, device and system for power load
CN110400021B (en) Bank branch cash usage prediction method and device
Hargreaves et al. REFLEX: An adapted production simulation methodology for flexible capacity planning
CN106549772A (en) Resource prediction method, system and capacity management device
Paoletti et al. Load forecasting for active distribution networks
CN108491982A (en) A kind of short-term load forecasting method and system based on echo state network
CN111754024A (en) Power industry time sequence prediction method and device based on regression analysis
CN112257928A (en) Short-term power load probability prediction method based on CNN and quantile regression
CN116805198A (en) Method and system for power grid planning dynamics based on reinforcement learning and predictive analysis
CN115882456B (en) Power control method and system based on large-scale power grid tide
CN108984304A (en) Server expansion calculation method and device based on regression equation
CN111738507A (en) Bank clearing position fund payment amount prediction method, device, equipment and medium
CN111864728B (en) Important equipment identification method and system for reconfigurable power distribution network
CN114936683A (en) Power grid bus load analysis and prediction assessment management method, device and system
CN113449933B (en) Regional medium-term load prediction method and device based on clustering electric quantity curve decomposition
CN112668249B (en) Online construction method and system for power grid primary equipment overhaul technical modification scheme
CN110659442A (en) System, short-term data prediction method and device thereof, and storage medium
CN117371662A (en) Evaluation system and method for adjustment capability of virtual power plant
CN113361960B (en) Method, medium, device and equipment for quantifying large-scale demand response capacity
CN116341727A (en) Wind power determination method and device based on probability prediction
CN112348575A (en) Hyper-parameter optimization method and device of sales prediction model
Li et al. Research on power plant inventory control methods based on coal consumption prediction
CN117911078A (en) Power data processing method and device, electronic equipment and storage medium
CN114296878A (en) Method for predicting container resource usage
CN115660228A (en) Power generation load prediction model training method, prediction method, device and storage medium

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