CN116402240B - Model input construction method and device for wind power prediction of dispatching side area - Google Patents

Model input construction method and device for wind power prediction of dispatching side area Download PDF

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CN116402240B
CN116402240B CN202310671397.6A CN202310671397A CN116402240B CN 116402240 B CN116402240 B CN 116402240B CN 202310671397 A CN202310671397 A CN 202310671397A CN 116402240 B CN116402240 B CN 116402240B
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time point
wind speed
side area
air pressure
humidity
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CN116402240A (en
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陈明炫
王国旭
张龙飞
耿宗旺
刘会
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BEIJING ZHONGKE FURUI ELECTRIC TECHNOLOGY CO LTD
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BEIJING ZHONGKE FURUI ELECTRIC TECHNOLOGY CO LTD
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    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • G06Q10/067—Enterprise or organisation modelling
    • 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
    • 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
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
    • 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
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation

Abstract

The invention discloses a model input construction method and device for wind power prediction of a dispatching side area, and belongs to the technical field of data processing. According to the method, firstly, historical operation data of a wind power plant are obtained, then the historical operation data are processed, a time point data set is determined, secondly, based on the historical operation data, characteristic values such as wind speed distribution frequency values, wind speed, temperature, air pressure and humidity of a dispatching side area at each time point are calculated, and finally, according to the calculation results, the input characteristics of a wind power prediction model at each time point are determined. According to the method, by combining historical operation data of the wind power plant, numerical weather forecast characteristics of the wind power plant governed by the region can be accurately generated, accurate modeling input data is provided for the model, and the accuracy of model power prediction is effectively ensured. The method solves the technical problems that the prediction result of the model cannot reflect the future power prediction condition of the region due to excessive input number and inaccurate data of the existing wind power prediction model.

Description

Model input construction method and device for wind power prediction of dispatching side area
Technical Field
The invention relates to the technical field of data processing, in particular to a model input construction method and device for wind power prediction of a dispatching side area.
Background
Wind power technology refers to technology for generating electricity by wind power, and wind power generation in a power system is more and more common. After the modeling method of deep learning is popularized gradually, the prediction of wind power by using a wind power prediction model becomes a popular scheme. For example, chinese patent CN113935512a, obtains a wind power predicted value corresponding to a target predicted time by obtaining weather forecast data corresponding to the target predicted time and inputting the weather forecast data into a wind power prediction model, then determines a target wind power section to which the wind power predicted value belongs according to a preset power section dividing condition, and obtains a wind power correction value corresponding to the target wind power section, where the wind power correction value is determined according to a historical wind power predicted value and a historical wind power measured value corresponding to the target wind power section, and finally corrects the wind power predicted value by using the wind power correction value to obtain the target wind power predicted value corresponding to the target predicted time.
However, as the proportion of wind power in the power grid is continuously increased, the number of wind power stations managed by the dispatching side is gradually increased, and the method for independently establishing the power prediction model for each station by the dispatching side for the prediction area consumes excessive resources such as manpower, material resources, time and the like. Particularly, after the modeling method of deep learning is popularized gradually, the cost of single-site modeling is higher. For the dispatching side, single-field modeling is bypassed, modeling is directly carried out from the regional angle, namely, the characteristics of regional numerical weather forecast are directly extracted, and the characteristics are taken as the input component of a model represented by deep learning, so that the method is a scheme worthy of research.
The simplest regional numerical weather forecast feature is a numerical weather forecast feature set of each station, and the method has the advantages that the weather information of each station is contained, the number of inputs is excessive, the current station number is depended, the condition of newly increasing stations is not facilitated, the input data of the wind power prediction model is inaccurate, and the prediction result of the wind power prediction model cannot reflect the future power prediction condition of the region.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a model input construction method and device for wind power prediction of a dispatching side area.
According to one aspect of the invention, there is provided a model input construction method for wind power prediction of a dispatch-side area, comprising:
acquiring various parameters required by model input construction, starting-up capacity historical record data of a wind farm governed by a dispatching side area and historical numerical weather forecast data of the wind farm governed by the dispatching side area;
processing historical numerical weather forecast data of a wind power plant governed by a dispatching side area, and determining a time point data set, wherein the time point data set comprises historical numerical weather forecast data of each time point, and the historical numerical weather forecast data of each time point consists of valid historical numerical weather forecast data of all sites of the wind power plant governed by the dispatching side area;
based on various parameters, starting capacity historical record data and a time point data set, calculating a wind speed distribution frequency value, a wind speed characteristic value, a temperature characteristic value, an air pressure characteristic value and a humidity characteristic value of a scheduling side area at each time point;
and determining the input characteristics of a preset wind power prediction model at each time point according to the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the dispatching side area at each time point.
Optionally, the processing the historical numerical weather forecast data of the wind farm governed by the dispatching side area to determine a time point data set includes:
determining a time range of historical numerical weather forecast data;
determining a plurality of candidate time points from a time range;
judging whether historical numerical weather forecast data of all stations of the wind power plant governed by the scheduling side area corresponding to each candidate time point are valid data or not;
according to the judging result, determining each target time point that the historical numerical weather forecast data of all stations are valid data from each candidate time point;
and counting the historical numerical weather forecast data of each target time point to generate a time point data set.
Optionally, the calculating the wind speed distribution frequency value of the scheduling side area at each time point based on each parameter, the startup capacity historical record data and the time point data set includes:
selecting the wind speed at the hub height or at a certain distance from the hub height in each wind field as a processing object;
calculating the wind speed frequency statistic array size based on various parameters;
based on the calculated wind speed frequency statistic array, a wind speed frequency statistic array is established, and initial values of the juxtaposed wind speed frequency statistic arrays are all 0;
Counting the numerical weather forecast wind speed distribution conditions of each station at each time point in the time point data set;
updating a wind speed frequency statistic array according to the numerical weather forecast wind speed distribution condition of each station;
and determining the wind speed distribution frequency value of the dispatching side area at each time point based on the updated wind speed frequency statistic array.
Optionally, the calculating the wind speed characteristic value of the scheduling side area at each time point based on each parameter, the startup capacity historical record data and the time point data set includes:
counting the highest wind speed, the lowest wind speed and the average wind speed of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest wind speed, the counted lowest wind speed and the counted average wind speed based on the highest wind speed threshold value and the counted lowest wind speed threshold value in each parameter, and determining the normalized highest wind speed, the normalized lowest wind speed and the normalized average wind speed as wind speed characteristic values of the dispatching side area at each time point.
Optionally, the calculating the temperature characteristic value of the scheduling side area at each time point based on each parameter, the startup capacity historical record data and the time point data set includes:
Counting the highest temperature, the lowest temperature and the average temperature of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest temperature, the counted lowest temperature and the counted average temperature based on the highest temperature threshold value and the counted lowest temperature threshold value in each parameter, and determining the highest temperature, the counted lowest temperature and the counted average temperature after the normalization processing as the temperature characteristic value of the scheduling side area at each time point.
Optionally, the calculating the air pressure characteristic value of the dispatching side area at each time point based on each parameter, the starting capacity historical record data and the time point data set includes:
counting the highest air pressure, the lowest air pressure and the average air pressure of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest air pressure, the counted lowest air pressure and the counted average air pressure based on the highest air pressure threshold value and the counted lowest air pressure threshold value in each parameter, and determining the normalized highest air pressure, the normalized lowest air pressure and the normalized average air pressure as air pressure characteristic values of the dispatching side area at each time point.
Optionally, the calculating the humidity characteristic value of the scheduling side area at each time point based on each parameter, the startup capacity historical record data and the time point data set includes:
Counting the highest humidity, the lowest humidity and the average humidity of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest humidity, lowest humidity and average humidity based on the highest humidity threshold and the lowest humidity threshold in each parameter, and determining the highest humidity, the lowest humidity and the average humidity after the normalization processing as humidity characteristic values of the dispatching side area at each time point.
Optionally, the determining the input feature of the preset wind power prediction model at each time point according to the wind speed distribution frequency value, the wind speed feature value, the temperature feature value, the air pressure feature value and the humidity feature value includes:
according to a preset arrangement sequence, arranging the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of each time point to form the input characteristic of a preset wind power prediction model at each time point.
According to another aspect of the present invention, there is provided a model input construction device for scheduling-side area wind power prediction, comprising:
the acquisition module is used for acquiring various parameters required by the input construction of the model, the historical record data of the starting-up capacity of the wind power plant governed by the dispatching side area and the historical numerical weather forecast data of the wind power plant governed by the dispatching side area;
The processing module is used for processing the historical numerical weather forecast data of the wind power plant governed by the dispatching side area and determining a time point data set, wherein the time point data set comprises the historical numerical weather forecast data of each time point, and the historical numerical weather forecast data of each time point consists of the historical numerical weather forecast data which is effective for all stations of the wind power plant governed by the dispatching side area;
the calculation module is used for calculating the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the scheduling side area at each time point based on various parameters, the starting capacity historical record data and the time point data set;
the input characteristic determining module is used for determining the input characteristic of the preset wind power prediction model at each time point according to the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the scheduling side area at each time point.
According to a further aspect of the present invention there is provided a computer readable storage medium storing a computer program for performing the method according to any one of the above aspects of the present invention.
Therefore, the method and the device firstly acquire various parameters required by the input construction of the model, the starting-up capacity historical record data of the wind power plant governed by the dispatching side area and the historical numerical weather forecast data of the wind power plant governed by the dispatching side area, and provide data support for subsequent processing. And then, processing the historical numerical weather forecast data of the wind farm governed by the dispatching side area to determine a time point data set. The time point data set comprises historical numerical weather forecast data of each time point, and the historical numerical weather forecast data of each time point consists of valid historical numerical weather forecast data of all stations of the wind power plant governed by the dispatching side area. By the method, useless data in the historical numerical weather forecast data can be effectively removed, and a foundation is laid for the accuracy of a subsequent calculation result. And secondly, calculating the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the dispatching side area at each time point based on each parameter, the starting capacity historical record data and the time point data set. And finally, determining the input characteristics of a preset wind power prediction model at each time point according to the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the scheduling side area at each time point. According to the method, by combining historical operation data of the wind power plant, numerical weather forecast characteristics of the wind power plant under the control of the region can be accurately generated, accurate modeling input data is provided for integral modeling of regional wind power prediction, and accuracy of regional power prediction facing to a dispatching side can be effectively ensured. The method solves the technical problems that the prediction result of the model cannot reflect the future power prediction condition of the region due to excessive input number and inaccurate data of the existing wind power prediction model.
Drawings
Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
FIG. 1 is a flow chart of a model input construction method for wind power prediction of a dispatch-side-oriented area according to an exemplary embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a model input construction device for wind power prediction for a dispatch side area according to an exemplary embodiment of the present invention;
fig. 3 is a structure of an electronic device provided in an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
It will be appreciated by those of skill in the art that the terms "first," "second," etc. in embodiments of the present invention are used merely to distinguish between different steps, devices or modules, etc., and do not represent any particular technical meaning nor necessarily logical order between them.
It should also be understood that in embodiments of the present invention, "plurality" may refer to two or more, and "at least one" may refer to one, two or more.
It should also be appreciated that any component, data, or structure referred to in an embodiment of the invention may be generally understood as one or more without explicit limitation or the contrary in the context.
In addition, the term "and/or" in the present invention is merely an association relationship describing the association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In the present invention, the character "/" generally indicates that the front and rear related objects are an or relationship.
It should also be understood that the description of the embodiments of the present invention emphasizes the differences between the embodiments, and that the same or similar features may be referred to each other, and for brevity, will not be described in detail.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and apparatus should be considered part of the specification.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations with electronic devices, such as communications terminals, computer systems, servers, etc. Examples of well known communication terminals, computing systems, environments, and/or configurations that may be suitable for use with electronic devices, such as communication terminals, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the foregoing, and the like.
Electronic devices such as communication terminals, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer system/server may be implemented in a distributed cloud computing environment in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Exemplary method
FIG. 1 is a flow chart of a model input construction method for wind power prediction of a dispatch-side area according to an exemplary embodiment of the present invention. As shown in fig. 1, the model input construction method for wind power prediction of a dispatching side region includes the following steps:
step S101: acquiring various parameters required by model input construction, starting-up capacity historical record data of a wind farm governed by a dispatching side area and historical numerical weather forecast data of the wind farm governed by the dispatching side area;
In the embodiment of the invention, various parameters required by the model input construction include, for example and without limitation, a highest wind speed threshold value, a lowest wind speed threshold value, a highest temperature threshold value, a highest humidity threshold value, a lowest humidity threshold value, a highest air pressure threshold value, a lowest air pressure threshold value and the like, starting-up capacity historical record data of the wind power plant governed by the dispatching side area include, for example and without limitation, grid-connected capacities of all the wind power plants at different time points, and historical numerical weather forecast data of the wind power plant governed by the dispatching side area include, for example and without limitation, wind speed, temperature, humidity and air pressure of all the layers of heights. The three types of data can be identified as historical operation data of the wind power plant, and data support is provided for subsequent processing by acquiring the historical operation data of the wind power plant.
Step S102: processing historical numerical weather forecast data of a wind power plant governed by a dispatching side area, and determining a time point data set, wherein the time point data set comprises historical numerical weather forecast data of each time point, and the historical numerical weather forecast data of each time point consists of valid historical numerical weather forecast data of all sites of the wind power plant governed by the dispatching side area;
Optionally, the processing the historical numerical weather forecast data of the wind farm governed by the dispatching side area to determine a time point data set includes: determining a time range of historical numerical weather forecast data; determining a plurality of candidate time points from a time range; judging whether historical numerical weather forecast data of all stations of the wind power plant governed by the scheduling side area corresponding to each candidate time point are valid data or not; according to the judging result, determining each target time point that the historical numerical weather forecast data of all stations are valid data from each candidate time point; and counting the historical numerical weather forecast data of each target time point to generate a time point data set.
In the embodiment of the invention, assuming that the wind farm governed by the dispatching side area has 100 stations in total, the time range of the historical numerical weather forecast data is 24 hours, and a time point is determined every 15 minutes, and 96 candidate time points can be determined from the time range of 24 hours. In 96 candidate time points, not all the available historical numerical weather forecast data of all the stations can be obtained at each time point, for example, but not limited to, in the 2 nd candidate time point, only the available historical numerical weather forecast data of 97 stations can be obtained, and in addition, the historical numerical weather forecast data of 3 stations is empty, so that it can be judged that the historical numerical weather forecast data of all the stations of the wind farm administered by the dispatching side area corresponding to the 2 nd candidate time point is not all the available data, and the candidate time point is not the target time point required by the invention.
Further, under the 3 rd candidate time point, effective historical numerical weather forecast data of 100 stations can be obtained, and it can be judged that the historical numerical weather forecast data of all stations of the wind power plant governed by the scheduling side area corresponding to the 3 rd candidate time point are all effective data, and the candidate time point belongs to the target time point required by the invention. And so on, each target time point that the historical numerical weather forecast data of all stations is valid data can be determined from each candidate time point. For example, but not limited to, 92 target time points are selected from 96 candidate time points, and the historical numerical weather forecast data of all stations at the 92 target time points are valid data. And carrying out statistics on the historical numerical weather forecast data corresponding to the 92 target time points to generate a time point data set. By the method, useless data in the historical numerical weather forecast data can be effectively removed, and a foundation is laid for the accuracy of a subsequent calculation result.
Step S103: based on various parameters, starting capacity historical record data and a time point data set, calculating a wind speed distribution frequency value, a wind speed characteristic value, a temperature characteristic value, an air pressure characteristic value and a humidity characteristic value of a scheduling side area at each time point;
Optionally, the calculating the wind speed distribution frequency value of the scheduling side area at each time point based on each parameter, the startup capacity historical record data and the time point data set includes: selecting the wind speed at the hub height or at a certain distance from the hub height in each wind field as a processing object; calculating the wind speed frequency statistic array size based on various parameters; based on the calculated wind speed frequency statistic array, a wind speed frequency statistic array is established, and initial values of the juxtaposed wind speed frequency statistic arrays are all 0; counting the numerical weather forecast wind speed distribution conditions of each station at each time point in the time point data set; updating a wind speed frequency statistic array according to the numerical weather forecast wind speed distribution condition of each station; and determining the wind speed distribution frequency value of the dispatching side area at each time point based on the updated wind speed frequency statistic array.
In the embodiment of the invention, the specific steps for calculating the wind speed distribution frequency value of the dispatching side area at each time point are as follows:
(1) Each wind field selects the wind speed at or near the hub height as a processing object;
(2) According to the data acquired in the step S101, calculating the size of the wind speed frequency statistic array, ，/>For the maximum wind speed,interval for wind speed statistics;
(3) Establishing a wind speed frequency statistic array F, wherein the size of the array F is N, and initial values of the juxtaposed arrays are all 0;
(4) For each time point T in the time set T, counting the numerical weather forecast wind speed distribution condition of each station, namely updating an array F, and the calculation method is as follows:
4.1 Wind speed for a stationCalculate its and->Ratio of->；
4.2 The grid-connected capacity of the station at the moment is found to be L in the data acquired in the step S101;
4.3 If any)Then->；
4.4 If any)Then->，/>Is a downward integer of R.
(5) After the wind speed statistics of all stations at the moment is finished, normalizing the wind speed frequency statistics array,n is the number of wind fields governed by the area. Thus obtaining an updated array of wind speed frequency statistics. Based on the updated wind speed frequency statistics array, a wind speed distribution frequency value of the scheduling side area at each time point can be determined.
Optionally, the calculating the wind speed characteristic value of the scheduling side area at each time point based on each parameter, the startup capacity historical record data and the time point data set includes: counting the highest wind speed, the lowest wind speed and the average wind speed of all wind fields at each time point in the time point data set; and respectively carrying out normalization processing on the counted highest wind speed, the counted lowest wind speed and the counted average wind speed based on the highest wind speed threshold value and the counted lowest wind speed threshold value in each parameter, and determining the normalized highest wind speed, the normalized lowest wind speed and the normalized average wind speed as wind speed characteristic values of the dispatching side area at each time point.
In the embodiment of the invention, the highest wind speed of all wind fields at each time point in the time point data set is countedMinimum wind speed->Mean wind speed->. Then, the three statistical values are normalized to obtain respectively，/>，/>Wherein->And->The highest wind speed threshold value and the lowest wind speed threshold value acquired for step S101.
Optionally, the calculating the temperature characteristic value of the scheduling side area at each time point based on each parameter, the startup capacity historical record data and the time point data set includes: counting the highest temperature, the lowest temperature and the average temperature of all wind fields at each time point in the time point data set; and respectively carrying out normalization processing on the counted highest temperature, the counted lowest temperature and the counted average temperature based on the highest temperature threshold value and the counted lowest temperature threshold value in each parameter, and determining the highest temperature, the counted lowest temperature and the counted average temperature after the normalization processing as the temperature characteristic value of the scheduling side area at each time point.
In the embodiment of the invention, the highest temperatures of all wind fields at each time point in the time point data set are countedMinimum temperature->Average temperature- >. Then, normalizing the three values obtained by statistics to obtain respectively，/>，/>Wherein->And->The highest temperature threshold and the lowest temperature threshold acquired for step S101.
Optionally, the calculating the air pressure characteristic value of the dispatching side area at each time point based on each parameter, the starting capacity historical record data and the time point data set includes: counting the highest air pressure, the lowest air pressure and the average air pressure of all wind fields at each time point in the time point data set; and respectively carrying out normalization processing on the counted highest air pressure, the counted lowest air pressure and the counted average air pressure based on the highest air pressure threshold value and the counted lowest air pressure threshold value in each parameter, and determining the normalized highest air pressure, the normalized lowest air pressure and the normalized average air pressure as air pressure characteristic values of the dispatching side area at each time point.
In the embodiment of the invention, the most of all wind fields at each time point in the time point data set is countedHigh air pressureMinimum air pressure->Average barometric pressure->. Then, normalizing the three values obtained by statistics to obtain respectively，/>，/>Wherein->And->The highest air pressure threshold value and the lowest air pressure threshold value acquired in step S101.
Optionally, the calculating the humidity characteristic value of the scheduling side area at each time point based on each parameter, the startup capacity historical record data and the time point data set includes: counting the highest humidity, the lowest humidity and the average humidity of all wind fields at each time point in the time point data set; and respectively carrying out normalization processing on the counted highest humidity, lowest humidity and average humidity based on the highest humidity threshold and the lowest humidity threshold in each parameter, and determining the highest humidity, the lowest humidity and the average humidity after the normalization processing as humidity characteristic values of the dispatching side area at each time point.
In the embodiment of the invention, the highest humidity of all wind fields at each time point in the time point data set is countedMinimum humidity->Average humidity->. Then, normalizing the three values obtained by statistics to obtain respectively，/>，/>Wherein->And->The highest humidity threshold and the lowest humidity threshold acquired for step S101.
Step S104: and determining the input characteristics of a preset wind power prediction model at each time point according to the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the dispatching side area at each time point.
Optionally, the determining the input feature of the preset wind power prediction model at each time point according to the wind speed distribution frequency value, the wind speed feature value, the temperature feature value, the air pressure feature value and the humidity feature value includes: according to a preset arrangement sequence, arranging the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of each time point to form the input characteristic of a preset wind power prediction model at each time point.
In the embodiment of the invention, the input characteristics of the wind power prediction model at a certain moment are as follows:
the invention provides a model input construction method for regional wind power prediction of a dispatching side, which supports the rapid and direct establishment of regional wind power prediction models, avoids huge resources consumed by single-field modeling, and improves the modeling efficiency of regional wind power prediction of the dispatching side.
In summary, the method and the device firstly acquire various parameters required by the input construction of the model, the starting-up capacity historical record data of the wind power plant managed by the dispatching side area and the historical numerical weather forecast data of the wind power plant managed by the dispatching side area, and provide data support for subsequent processing. And then, processing the historical numerical weather forecast data of the wind farm governed by the dispatching side area to determine a time point data set. The time point data set comprises historical numerical weather forecast data of each time point, and the historical numerical weather forecast data of each time point consists of valid historical numerical weather forecast data of all stations of the wind power plant governed by the dispatching side area. By the method, useless data in the historical numerical weather forecast data can be effectively removed, and a foundation is laid for the accuracy of a subsequent calculation result. And secondly, calculating the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the dispatching side area at each time point based on each parameter, the starting capacity historical record data and the time point data set. And finally, determining the input characteristics of a preset wind power prediction model at each time point according to the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the scheduling side area at each time point. According to the method, by combining historical operation data of the wind power plant, numerical weather forecast characteristics of the wind power plant under the control of the region can be accurately generated, accurate modeling input data is provided for integral modeling of regional wind power prediction, and accuracy of regional power prediction facing to a dispatching side can be effectively ensured. The method solves the technical problems that the prediction result of the model cannot reflect the future power prediction condition of the region due to excessive input number and inaccurate data of the existing wind power prediction model.
Exemplary apparatus
Fig. 2 is a schematic structural diagram of a model input construction device 200 for wind power prediction for a dispatch-side area according to an exemplary embodiment of the present invention. As shown in fig. 2, the apparatus includes:
the obtaining module 210 is configured to obtain various parameters required by the model input configuration, starting-up capacity historical record data of the wind farm governed by the scheduling side area, and historical numerical weather forecast data of the wind farm governed by the scheduling side area;
the processing module 220 is configured to process historical numerical weather forecast data of a wind farm governed by the dispatching side area, and determine a time point data set, where the time point data set includes historical numerical weather forecast data of each time point, and the historical numerical weather forecast data of each time point is composed of valid historical numerical weather forecast data of all sites of the wind farm governed by the dispatching side area;
the calculating module 230 is configured to calculate a wind speed distribution frequency value, a wind speed characteristic value, a temperature characteristic value, an air pressure characteristic value and a humidity characteristic value of the scheduling side area at each time point based on each parameter, the startup capacity history data and the time point data set;
the input feature determining module 240 is configured to determine input features of a preset wind power prediction model at each time point according to a wind speed distribution frequency value, a wind speed feature value, a temperature feature value, an air pressure feature value and a humidity feature value of the scheduling side area at each time point.
Optionally, the processing module 220 is specifically configured to:
determining a time range of historical numerical weather forecast data;
determining a plurality of candidate time points from a time range;
judging whether historical numerical weather forecast data of all stations of the wind power plant governed by the scheduling side area corresponding to each candidate time point are valid data or not;
according to the judging result, determining each target time point that the historical numerical weather forecast data of all stations are valid data from each candidate time point;
and counting the historical numerical weather forecast data of each target time point to generate a time point data set.
Optionally, the computing module 230 is specifically configured to:
selecting the wind speed at the hub height or at a certain distance from the hub height in each wind field as a processing object;
calculating the wind speed frequency statistic array size based on various parameters;
based on the calculated wind speed frequency statistic array, a wind speed frequency statistic array is established, and initial values of the juxtaposed wind speed frequency statistic arrays are all 0;
counting the numerical weather forecast wind speed distribution conditions of each station at each time point in the time point data set;
updating a wind speed frequency statistic array according to the numerical weather forecast wind speed distribution condition of each station;
And determining the wind speed distribution frequency value of the dispatching side area at each time point based on the updated wind speed frequency statistic array.
Optionally, the computing module 230 is further specifically configured to:
counting the highest wind speed, the lowest wind speed and the average wind speed of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest wind speed, the counted lowest wind speed and the counted average wind speed based on the highest wind speed threshold value and the counted lowest wind speed threshold value in each parameter, and determining the normalized highest wind speed, the normalized lowest wind speed and the normalized average wind speed as wind speed characteristic values of the dispatching side area at each time point.
Optionally, the calculating the temperature characteristic value of the scheduling side area at each time point based on each parameter, the startup capacity historical record data and the time point data set includes:
counting the highest temperature, the lowest temperature and the average temperature of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest temperature, the counted lowest temperature and the counted average temperature based on the highest temperature threshold value and the counted lowest temperature threshold value in each parameter, and determining the highest temperature, the counted lowest temperature and the counted average temperature after the normalization processing as the temperature characteristic value of the scheduling side area at each time point.
Optionally, the computing module 230 is further specifically configured to:
counting the highest air pressure, the lowest air pressure and the average air pressure of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest air pressure, the counted lowest air pressure and the counted average air pressure based on the highest air pressure threshold value and the counted lowest air pressure threshold value in each parameter, and determining the normalized highest air pressure, the normalized lowest air pressure and the normalized average air pressure as air pressure characteristic values of the dispatching side area at each time point.
Optionally, the computing module 230 is further specifically configured to:
counting the highest humidity, the lowest humidity and the average humidity of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest humidity, lowest humidity and average humidity based on the highest humidity threshold and the lowest humidity threshold in each parameter, and determining the highest humidity, the lowest humidity and the average humidity after the normalization processing as humidity characteristic values of the dispatching side area at each time point.
Optionally, the input feature determining module 240 is specifically configured to:
according to a preset arrangement sequence, arranging the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of each time point to form the input characteristic of a preset wind power prediction model at each time point.
The model input construction device for wind power prediction of a dispatching side area in the embodiment of the invention corresponds to the model input construction method for wind power prediction of a dispatching side area in another embodiment of the invention, and is not described herein.
Exemplary electronic device
Fig. 3 is a structure of an electronic device provided in an exemplary embodiment of the present invention. As shown in fig. 3, the electronic device 30 includes one or more processors 31 and memory 32.
The processor 31 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
Memory 32 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) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 31 to implement the methods of the software programs of the various embodiments of the present invention described above and/or other desired functions. In one example, the electronic device may further include: an input device 33 and an output device 34, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
In addition, the input device 33 may also include, for example, a keyboard, a mouse, and the like.
The output device 34 can output various information to the outside. The output device 34 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device that are relevant to the present invention are shown in fig. 3 for simplicity, components such as buses, input/output interfaces, etc. being omitted. In addition, the electronic device may include any other suitable components depending on the particular application.
Exemplary computer program product and computer readable storage Medium
In addition to the methods and apparatus described above, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the invention described in the "exemplary methods" section of this specification.
The computer program product may write program code for performing operations of embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the invention may also be a computer-readable storage medium, having stored thereon computer program instructions, which when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the invention described in the "exemplary method" section of the description above.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present invention. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the invention is not necessarily limited to practice with the above described specific details.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, systems, apparatuses, systems according to the present invention are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, systems, apparatuses, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
It is also noted that in the systems, devices and methods of the present invention, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the invention to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.

Claims (5)

1. A model input construction method for wind power prediction of a dispatching side area is characterized by comprising the following steps:
acquiring various parameters required by model input construction, starting-up capacity historical record data of a wind farm governed by a dispatching side area and historical numerical weather forecast data of the wind farm governed by the dispatching side area;
processing historical numerical weather forecast data of a wind power plant governed by a dispatching side area, and determining a time point data set, wherein the time point data set comprises historical numerical weather forecast data of each time point, and the historical numerical weather forecast data of each time point consists of valid historical numerical weather forecast data of all sites of the wind power plant governed by the dispatching side area;
based on various parameters, starting capacity historical record data and a time point data set, calculating a wind speed distribution frequency value, a wind speed characteristic value, a temperature characteristic value, an air pressure characteristic value and a humidity characteristic value of a scheduling side area at each time point;
Determining input characteristics of a preset wind power prediction model at each time point according to a wind speed distribution frequency value, a wind speed characteristic value, a temperature characteristic value, an air pressure characteristic value and a humidity characteristic value of the scheduling side area at each time point; wherein the method comprises the steps of
Based on each parameter, the starting-up capacity historical record data and the time point data set, calculating the wind speed distribution frequency value of the dispatching side area at each time point comprises the following steps:
selecting the wind speed at the hub height or at a certain distance from the hub height in each wind field as a processing object;
calculating the wind speed frequency statistic array size based on various parameters;
based on the calculated wind speed frequency statistic array, a wind speed frequency statistic array is established, and initial values of the juxtaposed wind speed frequency statistic arrays are all 0;
counting the numerical weather forecast wind speed distribution conditions of each station at each time point in the time point data set;
updating a wind speed frequency statistic array according to the numerical weather forecast wind speed distribution condition of each station;
based on the updated wind speed frequency statistic array, determining a wind speed distribution frequency value of the scheduling side area at each time point;
based on each parameter, the starting capacity historical record data and the time point data set, calculating the wind speed characteristic value of the dispatching side area at each time point comprises the following steps:
Counting the highest wind speed, the lowest wind speed and the average wind speed of all wind fields at each time point in the time point data set;
respectively carrying out normalization processing on the counted highest wind speed, the counted lowest wind speed and the counted average wind speed based on the highest wind speed threshold value and the counted lowest wind speed threshold value in each parameter, and determining the highest wind speed, the counted lowest wind speed and the counted average wind speed after the normalization processing as wind speed characteristic values of a dispatching side area at each time point;
based on each parameter, the starting capacity historical record data and the time point data set, calculating the temperature characteristic value of the dispatching side area at each time point comprises the following steps:
counting the highest temperature, the lowest temperature and the average temperature of all wind fields at each time point in the time point data set;
respectively carrying out normalization processing on the counted highest temperature, the counted lowest temperature and the counted average temperature based on the highest temperature threshold value and the counted lowest temperature threshold value in each parameter, and determining the highest temperature, the counted lowest temperature and the counted average temperature after the normalization processing as a temperature characteristic value of a scheduling side area at each time point;
based on each parameter, the starting capacity historical record data and the time point data set, calculating the air pressure characteristic value of the dispatching side area at each time point comprises the following steps:
Counting the highest air pressure, the lowest air pressure and the average air pressure of all wind fields at each time point in the time point data set;
based on the highest air pressure threshold value and the lowest air pressure threshold value in each parameter, respectively carrying out normalization processing on the counted highest air pressure, the counted lowest air pressure and the counted average air pressure, and determining the normalized highest air pressure, the normalized lowest air pressure and the normalized average air pressure as air pressure characteristic values of the dispatching side area at each time point;
based on each parameter, the starting capacity historical record data and the time point data set, calculating the humidity characteristic value of the dispatching side area at each time point comprises the following steps:
counting the highest humidity, the lowest humidity and the average humidity of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest humidity, lowest humidity and average humidity based on the highest humidity threshold and the lowest humidity threshold in each parameter, and determining the highest humidity, the lowest humidity and the average humidity after the normalization processing as humidity characteristic values of the dispatching side area at each time point.
2. The method of claim 1, wherein the processing historical numerical weather forecast data for the wind farm under the control of the scheduling side area to determine the set of time points comprises:
Determining a time range of historical numerical weather forecast data;
determining a plurality of candidate time points from a time range;
judging whether historical numerical weather forecast data of all stations of the wind power plant governed by the scheduling side area corresponding to each candidate time point are valid data or not;
according to the judging result, determining each target time point that the historical numerical weather forecast data of all stations are valid data from each candidate time point;
and counting the historical numerical weather forecast data of each target time point to generate a time point data set.
3. The method according to claim 1, wherein the determining the input features of the preset wind power prediction model at each time point according to the wind speed distribution frequency value, the wind speed feature value, the temperature feature value, the air pressure feature value and the humidity feature value of the scheduling side area at each time point comprises:
according to a preset arrangement sequence, arranging the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of each time point to form the input characteristic of a preset wind power prediction model at each time point.
4. A model input construction device facing to wind power prediction of a dispatching side area is characterized by comprising:
The acquisition module is used for acquiring various parameters required by the input construction of the model, the historical record data of the starting-up capacity of the wind power plant governed by the dispatching side area and the historical numerical weather forecast data of the wind power plant governed by the dispatching side area;
the processing module is used for processing the historical numerical weather forecast data of the wind power plant governed by the dispatching side area and determining a time point data set, wherein the time point data set comprises the historical numerical weather forecast data of each time point, and the historical numerical weather forecast data of each time point consists of the historical numerical weather forecast data which is effective for all stations of the wind power plant governed by the dispatching side area;
the calculation module is used for calculating the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the scheduling side area at each time point based on various parameters, the starting capacity historical record data and the time point data set;
the input characteristic determining module is used for determining the input characteristic of a preset wind power prediction model at each time point according to the wind speed distribution frequency value, the wind speed characteristic value, the temperature characteristic value, the air pressure characteristic value and the humidity characteristic value of the scheduling side area at each time point; wherein the method comprises the steps of
Based on each parameter, the starting-up capacity historical record data and the time point data set, calculating the wind speed distribution frequency value of the dispatching side area at each time point comprises the following steps:
selecting the wind speed at the hub height or at a certain distance from the hub height in each wind field as a processing object;
calculating the wind speed frequency statistic array size based on various parameters;
based on the calculated wind speed frequency statistic array, a wind speed frequency statistic array is established, and initial values of the juxtaposed wind speed frequency statistic arrays are all 0;
counting the numerical weather forecast wind speed distribution conditions of each station at each time point in the time point data set;
updating a wind speed frequency statistic array according to the numerical weather forecast wind speed distribution condition of each station;
based on the updated wind speed frequency statistic array, determining a wind speed distribution frequency value of the scheduling side area at each time point;
based on each parameter, the starting capacity historical record data and the time point data set, calculating the wind speed characteristic value of the dispatching side area at each time point comprises the following steps:
counting the highest wind speed, the lowest wind speed and the average wind speed of all wind fields at each time point in the time point data set;
Respectively carrying out normalization processing on the counted highest wind speed, the counted lowest wind speed and the counted average wind speed based on the highest wind speed threshold value and the counted lowest wind speed threshold value in each parameter, and determining the highest wind speed, the counted lowest wind speed and the counted average wind speed after the normalization processing as wind speed characteristic values of a dispatching side area at each time point;
based on each parameter, the starting capacity historical record data and the time point data set, calculating the temperature characteristic value of the dispatching side area at each time point comprises the following steps:
counting the highest temperature, the lowest temperature and the average temperature of all wind fields at each time point in the time point data set;
respectively carrying out normalization processing on the counted highest temperature, the counted lowest temperature and the counted average temperature based on the highest temperature threshold value and the counted lowest temperature threshold value in each parameter, and determining the highest temperature, the counted lowest temperature and the counted average temperature after the normalization processing as a temperature characteristic value of a scheduling side area at each time point;
based on each parameter, the starting capacity historical record data and the time point data set, calculating the air pressure characteristic value of the dispatching side area at each time point comprises the following steps:
counting the highest air pressure, the lowest air pressure and the average air pressure of all wind fields at each time point in the time point data set;
Based on the highest air pressure threshold value and the lowest air pressure threshold value in each parameter, respectively carrying out normalization processing on the counted highest air pressure, the counted lowest air pressure and the counted average air pressure, and determining the normalized highest air pressure, the normalized lowest air pressure and the normalized average air pressure as air pressure characteristic values of the dispatching side area at each time point;
based on each parameter, the starting capacity historical record data and the time point data set, calculating the humidity characteristic value of the dispatching side area at each time point comprises the following steps:
counting the highest humidity, the lowest humidity and the average humidity of all wind fields at each time point in the time point data set;
and respectively carrying out normalization processing on the counted highest humidity, lowest humidity and average humidity based on the highest humidity threshold and the lowest humidity threshold in each parameter, and determining the highest humidity, the lowest humidity and the average humidity after the normalization processing as humidity characteristic values of the dispatching side area at each time point.
5. A computer readable storage medium storing a computer program for performing the method of any one of the preceding claims 1-3.
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