CN111931999A - Power grid rainstorm disaster long-term prediction method, device and system - Google Patents

Power grid rainstorm disaster long-term prediction method, device and system Download PDF

Info

Publication number
CN111931999A
CN111931999A CN202010734016.0A CN202010734016A CN111931999A CN 111931999 A CN111931999 A CN 111931999A CN 202010734016 A CN202010734016 A CN 202010734016A CN 111931999 A CN111931999 A CN 111931999A
Authority
CN
China
Prior art keywords
power grid
data sequence
rainfall
mode
precipitation
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
CN202010734016.0A
Other languages
Chinese (zh)
Inventor
简洲
叶钰
徐勋建
郭俊
李丽
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.)
State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan 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 State Grid Corp of China SGCC, State Grid Hunan Electric Power Co Ltd, Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202010734016.0A priority Critical patent/CN111931999A/en
Publication of CN111931999A publication Critical patent/CN111931999A/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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

Abstract

The disclosure relates to a power grid rainstorm disaster long-term prediction method, device and system. The method comprises the following steps: selecting a historical power grid precipitation data sequence of N months before a month to be tested in a target area, wherein N is greater than or equal to 4; decomposing the historical power grid rainfall data sequence into J power grid rainfall data sequences under different modes through wavelet decomposition, wherein J is log2An integer portion of N; predicting the power grid precipitation data sequence under each mode through an autoregressive moving average model; and restoring the power grid rainfall data sequence under each mode into the power grid rainfall prediction data sequence of the month to be detected through wavelet reconstruction. The embodiment of the disclosure performs wavelet decomposition, autoregressive moving average operation and wavelet reconstruction on the historical power grid rainfall data sequence of N months before the month to be measured,and a power grid rainfall prediction data sequence is generated, so that the power grid rainstorm disaster can be predicted for a long time.

Description

Power grid rainstorm disaster long-term prediction method, device and system
Technical Field
The disclosure relates to the technical field of power grids, in particular to a power grid rainstorm disaster long-term prediction method, device and system.
Background
A power grid rainstorm disaster can bring a large amount of rainfall in a short time, so that important electric facilities such as tower foundations and transformer substations and the like are soaked in water for a long time, and a power grid line is tripped and important electric equipment is damaged; the method is easy to cause secondary disasters such as flood, landslide and the like, so that the transmission tower is inclined and inverted, and a large-area power failure accident can be caused in serious cases. The summer rainstorm is not only an important hidden danger of safe operation of a power grid, but also a key factor of hydropower economy and safe operation, the longer the forecast time is, the more sufficient the rainstorm disaster can be dealt with, the long-term forecast can forecast the occurrence condition of the whole summer rainstorm in the future by 1 month, the advance deployment of countermeasures can be guided, the rainstorm loss of the power grid is reduced, and the economical efficiency of reservoir operation is improved. Therefore, the research on the long-term prediction of the power grid rainstorm disaster has important significance and engineering practical value.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, the present disclosure provides a power grid rainstorm disaster long-term prediction method, device and system.
The invention provides a power grid rainstorm disaster long-term prediction method, which comprises the following steps:
selecting a historical power grid precipitation data sequence of N months before a month to be tested in a target area, wherein N is greater than or equal to 4;
decomposing the historical power grid rainfall data sequence through wavelet decompositionForming J power grid precipitation data sequences under different modes, wherein J is log2An integer portion of N;
predicting the power grid precipitation data sequence under each mode through an autoregressive moving average model;
and restoring the power grid rainfall data sequence under each mode into the power grid rainfall prediction data sequence of the month to be detected through wavelet reconstruction.
Optionally, predicting the power grid precipitation data sequence in each mode through an autoregressive moving average model includes:
performing autoregressive analysis on the power grid rainfall data sequence under each mode;
and carrying out moving average analysis on the power grid precipitation data sequence under each mode.
Optionally, the calculation formula of the wavelet decomposition and the wavelet reconstruction is as follows:
Figure BDA0002604276960000021
wherein f (x) is a historical power grid rainfall data sequence or a power grid rainfall prediction data sequence psij,k(x) J is 0,1,2, and J-1, k is 0,1,2, for the power grid precipitation data sequence in different modes or the predicted value of the power grid precipitation data sequence in different modesj-1，
Figure BDA0002604276960000022
Optionally, the calculation formula for predicting the power grid precipitation data sequence in each mode is as follows:
Figure BDA0002604276960000023
wherein the content of the first and second substances,tis white noise and has an average value of 0, c is a constant, phiiIs an autoregressive coefficient, mu is a constant, thetaiIs the moving average coefficient.
The utility model provides a power grid rainstorm disaster long-term prediction device, include:
the rainfall data sequence selecting module is used for selecting a historical power grid rainfall data sequence of N months before the month to be tested in the target area, wherein N is greater than or equal to 4;
and the precipitation data sequence decomposition module is used for decomposing the historical power grid precipitation data sequence into J power grid precipitation data sequences under different modes through wavelet decomposition, wherein J is log2An integer portion of N;
the rainfall data sequence prediction module is used for predicting the power grid rainfall data sequence under each mode through an autoregressive moving average model;
and the precipitation data sequence reduction module is used for reducing the power grid precipitation data sequence under each mode into the power grid precipitation prediction data sequence of the month to be detected through wavelet reconstruction.
Optionally, the precipitation data sequence prediction module includes:
the autoregressive analysis unit is used for carrying out autoregressive analysis on the power grid rainfall data sequence under each mode;
and the moving average analysis unit is used for performing moving average analysis on the power grid rainfall data sequence under each mode.
Optionally, the calculation formula of the wavelet decomposition and the wavelet reconstruction is as follows:
Figure BDA0002604276960000031
wherein f (x) is a historical power grid rainfall data sequence or a power grid rainfall prediction data sequence psij,k(x) J is 0,1,2, and J-1, k is 0,1,2, for the power grid precipitation data sequence in different modes or the predicted value of the power grid precipitation data sequence in different modesj-1，
Figure BDA0002604276960000032
Optionally, the calculation formula for predicting the power grid precipitation data sequence in each mode is as follows:
Figure BDA0002604276960000033
wherein the content of the first and second substances,tis white noise and has an average value of 0, c is a constant, phiiIs an autoregressive coefficient, mu is a constant, thetaiIs the moving average coefficient.
The utility model provides a power grid rainstorm disaster long-term prediction system, include:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for reading the executable instructions from the memory and executing the instructions to realize the power grid rainstorm disaster long-term prediction method provided by the disclosure.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
the technical scheme provided by the embodiment of the disclosure selects a historical power grid precipitation data sequence of N months before a target area month to be tested, wherein N is greater than or equal to 4; decomposing the historical power grid rainfall data sequence into J power grid rainfall data sequences under different modes through wavelet decomposition, wherein J is log2An integer portion of N; predicting the power grid precipitation data sequence under each mode through an autoregressive moving average model; and restoring the power grid rainfall data sequence under each mode into the power grid rainfall prediction data sequence of the month to be detected through wavelet reconstruction. Therefore, the power grid rainfall prediction data sequence is generated by performing wavelet decomposition, autoregressive moving average operation and wavelet reconstruction on the historical power grid rainfall data sequence of N months before the month to be measured, and long-term prediction of the power grid rainstorm disaster is achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a power grid rainstorm disaster long-term prediction method according to an embodiment of the present disclosure;
fig. 2 is a sequence diagram of historical grid precipitation data provided by an embodiment of the present disclosure;
3-5 are diagrams of power grid precipitation data sequence decomposition into 3 different modes according to an embodiment of the present disclosure;
6-8 are power grid precipitation data sequence diagrams predicted from power grid precipitation data sequences in different modes in 3 according to the embodiment of the present disclosure;
fig. 9 is a sequence diagram of power grid precipitation prediction data provided by an embodiment of the present disclosure;
fig. 10 is a block diagram of a long-term power grid rainstorm disaster prediction apparatus according to an embodiment of the present disclosure;
fig. 11 is a schematic structural diagram of a power grid rainstorm disaster long-term prediction system provided in an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
Fig. 1 is a schematic flow chart of a power grid rainstorm disaster long-term prediction method provided in an embodiment of the present disclosure. The power grid rainstorm disaster long-term prediction method is suitable for the power grid rainstorm disaster long-term prediction situation and can be executed by a power grid rainstorm disaster long-term prediction device, wherein the power grid rainstorm disaster long-term prediction device can be realized in a software and/or hardware mode and can be generally integrated in a power grid rainstorm disaster long-term prediction system. As shown in fig. 1, the present disclosure provides a power grid rainstorm disaster long-term prediction method, including:
and 110, selecting a historical power grid precipitation data sequence of N months before the month to be measured in the target area.
Wherein N is greater than or equal to 4. Fig. 2 schematically shows a historical grid precipitation data sequence diagram, which is formed by connecting lines of the historical grid precipitation data sequence of N months. As an example, if the month to be measured is 7 months, N months before the month to be measured are 6 months, 5 months, 4 months, 3 months, 2 months and 1 month, and the historical grid precipitation data sequence is denoted as f (x), where x is 0,1, 2.
And 120, decomposing the historical power grid precipitation data sequence into J power grid precipitation data sequences in different modes through wavelet decomposition.
Wherein J is log2The integer part of N.
The calculation formula of the wavelet decomposition is as follows:
Figure BDA0002604276960000051
wherein f (x) is a historical power grid precipitation data sequence psij,k(x) J-0, 1,2, J-1, k-0, 1,2 for the sequence of the grid precipitation data in different modesj-1，
Figure BDA0002604276960000061
For example, referring to fig. 3-5, the historical grid precipitation data sequence is decomposed into 3 different modal grid precipitation data sequences.
And step 130, predicting the power grid precipitation data sequence under each mode through an autoregressive moving average model.
Specifically, the method for predicting the power grid precipitation data sequence under each mode through the autoregressive moving average model comprises the following steps:
1. performing autoregressive analysis on the power grid precipitation data sequence under each mode:
Figure BDA0002604276960000062
wherein the content of the first and second substances,tis white noise and has an average value of 0, c is a constant, phiiIs an autoregressive coefficient;
2. carrying out moving average analysis on the power grid precipitation data sequence under each mode:
Figure BDA0002604276960000063
where μ is a constant and θiIs the moving average coefficient.
Finally, a calculation formula for predicting the power grid precipitation data sequence under each mode is obtained as follows:
Figure BDA0002604276960000064
for example, referring to fig. 6 to 8, the power grid precipitation data sequences in 3 different modes corresponding to fig. 3 to 5 are predicted through an autoregressive moving average model.
And 140, restoring the power grid rainfall data sequence in each mode into a power grid rainfall prediction data sequence of the month to be detected through wavelet reconstruction.
Based on the steps, the calculation formula of the wavelet reconstruction is as follows:
Figure BDA0002604276960000071
wherein f (x) isPrediction data sequence of electric network precipitation psij,k(x) J is a predicted value of the power grid precipitation data sequence under different modes, J is 0,1,2j-1，
Figure BDA0002604276960000072
Therefore, according to the embodiment of the disclosure, the historical power grid rainfall data sequence is decomposed in a plurality of different modes, the power grid rainfall data sequences in different modes are extrapolated through an autoregressive moving average model, and are synthesized through wavelet reconstruction to obtain a power grid rainfall prediction data sequence (see fig. 9), so that the power grid rainfall prediction value of the month to be measured is obtained, and the accuracy of power grid rainfall prediction is improved compared with the power grid rainfall prediction value obtained through direct extrapolation.
The technical scheme provided by the embodiment of the disclosure selects a historical power grid precipitation data sequence of N months before a target area month to be tested, wherein N is greater than or equal to 4; decomposing the historical power grid rainfall data sequence into J power grid rainfall data sequences under different modes through wavelet decomposition, wherein J is log2An integer portion of N; predicting the power grid precipitation data sequence under each mode through an autoregressive moving average model; and reducing the power grid rainfall data sequence under each mode into a power grid rainfall prediction data sequence of the month to be detected through wavelet reconstruction. Therefore, the power grid rainfall prediction data sequence is generated by performing wavelet decomposition, autoregressive moving average operation and wavelet reconstruction on the historical power grid rainfall data sequence of N months before the month to be measured, and long-term prediction of the power grid rainstorm disaster is achieved.
Fig. 10 is a block diagram of a structure of a power grid rainstorm disaster long-term prediction apparatus according to an embodiment of the present disclosure. As shown in fig. 10, an apparatus for predicting a power grid storm disaster for a long time according to an embodiment of the present disclosure includes:
the rainfall data sequence selection module 201 is used for selecting a historical power grid rainfall data sequence of N months before the month to be tested in the target area, wherein N is greater than or equal to 4;
a precipitation data sequence decomposition module 202, configured to decompose the historical power grid precipitation data sequence into J power grid precipitation data sequences in different modes through wavelet decomposition, where J is log2An integer portion of N;
the rainfall data sequence prediction module 203 is used for predicting the power grid rainfall data sequence under each mode through an autoregressive moving average model;
and the precipitation data sequence reduction module 204 is used for reducing the power grid precipitation data sequence in each mode into a power grid precipitation prediction data sequence of the month to be detected through wavelet reconstruction.
Optionally, the precipitation data sequence prediction module includes:
the autoregressive analysis unit is used for carrying out autoregressive analysis on the power grid rainfall data sequence under each mode;
and the moving average analysis unit is used for performing moving average analysis on the power grid rainfall data sequence under each mode.
Optionally, the calculation formula of the wavelet decomposition and the wavelet reconstruction is as follows:
Figure BDA0002604276960000081
wherein f (x) is a historical power grid rainfall data sequence or a power grid rainfall prediction data sequence psij,k(x) J is 0,1,2, and J-1, k is 0,1,2, for the power grid precipitation data sequence in different modes or the predicted value of the power grid precipitation data sequence in different modesj-1，
Figure BDA0002604276960000082
Optionally, the calculation formula for predicting the power grid precipitation data sequence in each mode is as follows:
Figure BDA0002604276960000083
wherein the content of the first and second substances,tis white noise and has an average value of 0, c is a constant, phiiIs an autoregressive coefficient, mu is a constant, thetaiIs the moving average coefficient.
The power grid rainstorm disaster long-term prediction device provided by the embodiment of the disclosure can execute the power grid rainstorm disaster long-term prediction method provided by any embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the execution method.
In addition, the present disclosure provides a power grid rainstorm disaster long-term prediction system, including: a processor; a memory for storing processor-executable instructions; and the processor is used for reading the executable instructions from the memory and executing the instructions to realize the power grid rainstorm disaster long-term prediction method provided by the disclosure.
Fig. 11 is a schematic structural diagram of a power grid rainstorm disaster long-term prediction system according to an embodiment of the present disclosure. As shown in fig. 11, the grid storm disaster long term prediction system 300 includes one or more processors 301 and memory 302.
The processor 301 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the grid storm disaster long term prediction system 300 to perform desired functions.
Memory 302 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 301 to implement the power grid rainstorm disaster long-term prediction method of the embodiments of the present disclosure described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
In one example, the power grid rainstorm disaster long-term prediction system 300 may further include: an input device 303 and an output device 304, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input device 303 may also include, for example, a keyboard, a mouse, and the like.
The output device 304 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 304 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the grid storm disaster long term prediction system 300 relevant to the present disclosure are shown in fig. 5, omitting components such as buses, input/output interfaces, and the like. In addition, the grid storm disaster long-term prediction system 300 may include any other suitable components according to the specific application.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (9)

1. A power grid rainstorm disaster long-term prediction method is characterized by comprising the following steps:
selecting a historical power grid precipitation data sequence of N months before a month to be tested in a target area, wherein N is greater than or equal to 4;
decomposing the historical power grid rainfall data sequence into J power grid rainfall data sequences under different modes through wavelet decomposition, wherein J is log2An integer portion of N;
predicting the power grid precipitation data sequence under each mode through an autoregressive moving average model;
and restoring the power grid rainfall data sequence under each mode into the power grid rainfall prediction data sequence of the month to be detected through wavelet reconstruction.
2. The method for long-term prediction of power grid rainstorm disaster according to claim 1, wherein the prediction of the power grid rainfall data sequence in each mode through an autoregressive moving average model comprises:
performing autoregressive analysis on the power grid rainfall data sequence under each mode;
and carrying out moving average analysis on the power grid precipitation data sequence under each mode.
3. The power grid rainstorm disaster long-term prediction method according to claim 1, wherein the calculation formula of wavelet decomposition and wavelet reconstruction is as follows:
Figure FDA0002604276950000011
wherein f (x) is a calendarHistorical grid precipitation data sequence or grid precipitation prediction data sequence psij,k(x) J is 0,1,2, and J-1, k is 0,1,2, for the power grid precipitation data sequence in different modes or the predicted value of the power grid precipitation data sequence in different modesj-1，
Figure FDA0002604276950000012
4. The method for long-term prediction of power grid rainstorm disaster according to claim 1, wherein a calculation formula for predicting the power grid rainfall data sequence under each mode is as follows:
Figure FDA0002604276950000021
wherein the content of the first and second substances,tis white noise and has an average value of 0, c is a constant, phiiIs an autoregressive coefficient, mu is a constant, thetaiIs the moving average coefficient.
5. A power grid rainstorm disaster long-term prediction device is characterized by comprising:
the rainfall data sequence selecting module is used for selecting a historical power grid rainfall data sequence of N months before the month to be tested in the target area, wherein N is greater than or equal to 4;
and the precipitation data sequence decomposition module is used for decomposing the historical power grid precipitation data sequence into J power grid precipitation data sequences under different modes through wavelet decomposition, wherein J is log2An integer portion of N;
the rainfall data sequence prediction module is used for predicting the power grid rainfall data sequence under each mode through an autoregressive moving average model;
and the precipitation data sequence reduction module is used for reducing the power grid precipitation data sequence under each mode into the power grid precipitation prediction data sequence of the month to be detected through wavelet reconstruction.
6. The grid rainstorm disaster long-term prediction device of claim 5, wherein the precipitation data sequence prediction module comprises:
the autoregressive analysis unit is used for carrying out autoregressive analysis on the power grid rainfall data sequence under each mode;
and the moving average analysis unit is used for performing moving average analysis on the power grid rainfall data sequence under each mode.
7. The power grid rainstorm disaster long-term prediction device according to claim 1, wherein the calculation formula of wavelet decomposition and wavelet reconstruction is as follows:
Figure FDA0002604276950000022
wherein f (x) is a historical power grid rainfall data sequence or a power grid rainfall prediction data sequence psij,k(x) J is 0,1,2, and J-1, k is 0,1,2, for the power grid precipitation data sequence in different modes or the predicted value of the power grid precipitation data sequence in different modesj-1，
Figure FDA0002604276950000031
8. The power grid rainstorm disaster long-term prediction device according to claim 1, wherein a calculation formula for predicting the power grid rainfall data sequence in each mode is as follows:
Figure FDA0002604276950000032
wherein the content of the first and second substances,tis white noise and has an average value of 0, c is a constant, phiiIs an autoregressive coefficient, mu is a constant, thetaiIs the moving average coefficient.
9. A power grid rainstorm disaster long-term prediction system is characterized by comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for reading the executable instructions from the memory and executing the instructions to realize the power grid rainstorm disaster long-term prediction method of any one of the claims 1-4.
CN202010734016.0A 2020-07-27 2020-07-27 Power grid rainstorm disaster long-term prediction method, device and system Pending CN111931999A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010734016.0A CN111931999A (en) 2020-07-27 2020-07-27 Power grid rainstorm disaster long-term prediction method, device and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010734016.0A CN111931999A (en) 2020-07-27 2020-07-27 Power grid rainstorm disaster long-term prediction method, device and system

Publications (1)

Publication Number Publication Date
CN111931999A true CN111931999A (en) 2020-11-13

Family

ID=73314218

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010734016.0A Pending CN111931999A (en) 2020-07-27 2020-07-27 Power grid rainstorm disaster long-term prediction method, device and system

Country Status (1)

Country Link
CN (1) CN111931999A (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102880755A (en) * 2012-09-25 2013-01-16 河海大学 Method and system for quantitatively forecasting extreme rainfall
CN107102969A (en) * 2017-04-28 2017-08-29 湘潭大学 The Forecasting Methodology and system of a kind of time series data
CN107885951A (en) * 2017-11-27 2018-04-06 河海大学 A kind of Time series hydrological forecasting method based on built-up pattern

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102880755A (en) * 2012-09-25 2013-01-16 河海大学 Method and system for quantitatively forecasting extreme rainfall
CN107102969A (en) * 2017-04-28 2017-08-29 湘潭大学 The Forecasting Methodology and system of a kind of time series data
CN107885951A (en) * 2017-11-27 2018-04-06 河海大学 A kind of Time series hydrological forecasting method based on built-up pattern

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
周惠成等: "基于小波分解的月径流预测校正模型研究", 《系统仿真学报》 *
张洪波等: "基于经验模态分解的非平稳水文序列预测研究", 《水利发电学报》 *

Similar Documents

Publication Publication Date Title
Adebayo et al. Determinants of CO2 emissions: Empirical evidence from Egypt
Klügel Seismic hazard analysis—Quo vadis?
Haselsteiner et al. Deriving environmental contours from highest density regions
Yao et al. Wavelet transform and neural networks for short-term electrical load forecasting
US20200212681A1 (en) Method, apparatus and storage medium for transmission network expansion planning considering extremely large amounts of operation scenarios
Bivona et al. Stochastic models for wind speed forecasting
Kreiss et al. Regression-type inference in nonparametric autoregression
Zoaka et al. Will financial development and clean energy utilization rejuvenate the environment in BRICS economies?
US20210398048A1 (en) Method and system for predicting building energy consumption based on holt-winters and extreme learning machine
Gaidai et al. SEM-REV offshore energy site wind-wave bivariate statistics by hindcast
CN113988477A (en) Photovoltaic power short-term prediction method and device based on machine learning and storage medium
Wang et al. A clustering-based analytical method for hybrid probabilistic and interval power flow
Mazaheri et al. A linearized transmission expansion planning model under N− 1 criterion for enhancing grid‐scale system flexibility via compressed air energy storage integration
Lin et al. Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics
El Beshbichi et al. Prediction of long-term extreme response of two-rotor floating wind turbine concept using the modified environmental contour method
CN113408804B (en) Electricity stealing behavior detection method, system, terminal equipment and storage medium
Wu et al. Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks
CN111931999A (en) Power grid rainstorm disaster long-term prediction method, device and system
CN114330110A (en) Model training method, photovoltaic power generation power prediction method and system
CN104239689B (en) Short-term wind speed forecasting method based on optimization Method Using Relevance Vector Machine
Zhao et al. Solar energy investment, technological innovation and carbon emission reduction: Evidence from China
Ullah et al. Natural resources, renewable energy-environment nexus for Pakistan: A policy perspective
Ariyarathne et al. Change point detection-based simulation of nonstationary sub-hourly wind time series
Eckert-Gallup et al. Modified Inverse First Order Reliability Method (I-FORM) for Predicting Extreme Sea States.
Hu et al. Determinants of renewable stock returns: The role of global supply chain pressure

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20201113