CN114638423A - Method, device, equipment and storage medium for forecasting park electrical load - Google Patents

Method, device, equipment and storage medium for forecasting park electrical load Download PDF

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CN114638423A
CN114638423A CN202210292246.5A CN202210292246A CN114638423A CN 114638423 A CN114638423 A CN 114638423A CN 202210292246 A CN202210292246 A CN 202210292246A CN 114638423 A CN114638423 A CN 114638423A
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黄杨珏
姚瑶
陈鹏
金杨
林一峰
夏英男
罗威
谢志文
尹海庆
李歆蔚
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Guangdong Power Grid Co Ltd
Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Abstract

The application is applicable to the technical field of power grid operation, and discloses a forecasting method, a device, equipment and a storage medium for a park electric load, which are characterized in that a plurality of bays are obtained by acquiring land planning index data of a target park and partitioning the target park according to the land planning index data, a multilayer partition space load forecasting algorithm is utilized to calculate the land area, the building volume ratio and the preset unit area load density, a target load set of each bay is forecasted, a saturated year load total amount of the target park is generated according to the target load sets of the bays, finally the saturated year load total amount and the development time sequence of the target park are comprehensively analyzed, the middle year load total amount of the target park is determined, and an actual load result of the park in each development stage is obtained, the accuracy of the load preset result is improved.

Description

Method, device, equipment and storage medium for forecasting park electrical load
Technical Field
The application relates to the technical field of power grid load prediction, in particular to a method, a device, equipment and a storage medium for predicting park electrical loads.
Background
Load forecasting is the primary basis for power planning. If the load prediction lags behind the actual demand, the power supply load is stressed, and even the condition of switching off and limiting the power is caused; if the load prediction excessively leads to the actual demand, the utilization rate of the power supply system will be low, and the situation of serious resource waste will be caused.
At present, the load prediction method mainly comprises an electric quantity prediction method and a load density method. The electric quantity prediction method obtains a maximum load value by predicting annual power consumption and combining maximum load utilization duration, specifically a unit consumption method, an electric elasticity coefficient method, a regression analysis method, a time series extrapolation method and the like; the load density method is used for predicting a total load value according to the existing load density and the partition area by calculating the existing load density of the partition. The electric quantity prediction method and the traditional load density method rely on trend analysis of a large amount of historical load data, and for a newly developed park, the newly developed park lacks enough historical load data, and the load development has more uncertain factors and poorer historical regularity, so that the load prediction result of the existing load prediction method has larger deviation with the actual situation of the newly developed park on the load scale and the load geographic distribution, and the prediction accuracy of the electric load of the park is low.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for forecasting park electrical loads, which aim to solve the technical problem that the forecasting accuracy of the current park electrical loads is low.
In order to solve the above technical problem, in a first aspect, the present application provides a method for predicting a park electrical load, including:
the land use planning method comprises the steps of obtaining land use planning index data of a target park, and partitioning the target park according to the land use planning index data to obtain a plurality of plots, wherein the land use planning index data comprises land use properties, land area and building volume ratio;
calculating the land area, the building volume ratio and the preset unit area load density by using a multilayer partition space load prediction algorithm, and predicting a target load set of each partition;
generating the total saturated annual load of the target park according to the target load sets of the plurality of bays;
and generating the total saturated annual load of the target park according to the target load sets of the plurality of bays.
The method comprises the steps of obtaining land use planning index data of a target park, partitioning the target park according to the land use planning index data to obtain a plurality of sub-parks, and conducting coincidence prediction according to actual space planning of the park; the method is characterized by comprising the following steps of calculating the used area, the building volume ratio and the preset unit area load density by utilizing a multilayer partition space load prediction algorithm, predicting a target load set of each partition, predicting a load set conforming to geographical distribution, and solving the problem that the traditional load prediction result has larger deviation from the actual situation in geographical position distribution; generating the total saturated annual load of the target park according to the target load sets of the plurality of bays so as to ensure that the load scale is consistent with the actual situation; and finally, comprehensively analyzing the saturated annual load total amount and the development time sequence of the target park, and determining the middle annual load total amount of the target park to obtain the actual load result of the park at each development stage, so that the accuracy of the load preset result is improved.
Preferably, the predicting the target load set of each block by using a multi-layer partition space load prediction algorithm to calculate the land area, the building volume ratio and the preset load density per unit area comprises the following steps:
predicting the same type load set of the same type of load in a plurality of districts according to the land area, the building volume ratio, the unit area load density and the preset first load simultaneous rate by utilizing a multilayer partition space load prediction algorithm, wherein each district comprises multiple land properties, and each land property corresponds to one type of load;
and determining a target load set of each district according to the homogeneous load sets of the plurality of districts and a preset second load concurrence rate.
Preferably, the method for predicting the similar load sets of the same load type in a plurality of blocks by using a multi-layer partition space load prediction algorithm according to the land area, the building volume ratio, the unit area load density and a preset first load concurrence rate comprises the following steps:
an intra-district classification load prediction algorithm utilizing a multilayer partition space load prediction algorithm predicts the same type load sets of the same type of load in a plurality of districts according to the land area, the building volume ratio, the unit area load density and the first load simultaneous rate, and the intra-district classification load prediction algorithm has the following calculation formula:
Figure BDA0003561083230000031
wherein L is1ijFor homogeneous load sets of ith load type in jth zone, RijBuilding volume fraction, P, corresponding to ith load type in jth zoneijA unit area load density, K, corresponding to the ith load type in the jth slice1Is the first load coincidence rate, SqijThe area is the land area corresponding to the q-th land parcel belonging to the ith load type in the jth parcel, and l is the total land parcel belonging to the ith load type in the jth parcel.
Preferably, determining a target load set of each parcel according to the homogeneous load sets of a plurality of parcels and a preset second load concurrence rate comprises:
determining a target load set of each parcel according to the homogeneous load set and the second load concurrency rate of a plurality of parcels by utilizing an intra-district load combination algorithm of a multilayer parcel space load prediction algorithm, wherein the calculation formula of the intra-district load combination algorithm is as follows:
Figure BDA0003561083230000032
wherein L is2jTarget load set for jth partition, K2Is the second load coincidence rate, L1ijIs the homogeneous load set of the ith load type in the jth block, and m is the total number of the load types of the jth block.
Preferably, the generating of the total saturated annual load of the target campus from the target load sets of the plurality of districts comprises:
according to a hierarchical partition merging algorithm of a multilayer partition space load prediction algorithm, generating a total saturated annual load of a target park according to a target load set of a plurality of bays and a preset third load concurrence rate, wherein the calculation formula of the hierarchical partition merging algorithm is as follows:
Figure BDA0003561083230000033
L3is the total saturated annual load of the target park, K3Is the third load coincidence rate, L2jIs the target load set of the jth parcel, and n is the total number of parcels of the target campus.
Preferably, before calculating the land area, the building volume fraction and the preset load density per unit area by using the multi-layer partition space load prediction algorithm and predicting the target load set of each partition, the method further comprises:
acquiring load characteristic prior data of each land property;
and determining the load density per unit area of each land use property according to the land use load characteristic prior data and the land use area.
Preferably, the comprehensive analysis is performed on the total saturated annual load and the development time sequence of the target park, and the determination of the total middle annual load of the target park comprises the following steps:
determining a project development progress and a project development trend in the target park according to the development time sequence of the target park;
and determining the middle annual load total of the target park according to the project development progress and the project development trend and by combining the saturated annual load total.
In a second aspect, the present application provides an apparatus for predicting a park electrical load, including:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring land use planning index data of a target park, and partitioning the target park according to the land use planning index data to obtain a plurality of plots, and the land use planning index data comprises land use properties, land area and building volume ratio;
the operation module is used for operating the land area, the building volume ratio and the preset unit area load density by utilizing a multilayer partition space load prediction algorithm and predicting a target load set of each partition;
the generating module is used for generating the total saturated annual load of the target park according to the target load sets of the plurality of bays;
and the determining module is used for comprehensively analyzing the saturated annual load total amount and the development time sequence of the target park and determining the middle annual load total amount of the target park.
In a third aspect, the present application further provides a computer device, comprising a processor and a memory, wherein the memory is used for storing a computer program, and the computer program is executed by the processor to implement the method for forecasting the park electrical load according to the first aspect.
In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, which when executed by a processor, implements the method for forecasting a park electrical load of the first aspect.
Please refer to the relevant description of the first aspect for the beneficial effects of the second to fourth aspects, which are not repeated herein.
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Fig. 1 is a schematic flowchart of a method for predicting a park electrical load according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a device for predicting a park electrical load according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
As described in the related art, the electric quantity prediction method and the traditional load density method rely on trend analysis of a large amount of historical load data, and for a newly developed park, the newly developed park lacks sufficient historical load data, and the load development has more uncertain factors and poorer historical regularity, so that the load prediction result of the existing load prediction method has larger deviation from the actual situation of the newly developed park on the load scale and the load geographic distribution, and the prediction accuracy of the electric load of the park is low.
Therefore, the embodiment of the application provides a forecasting method of the electric load of the park, which comprises the steps of obtaining land use planning index data of a target park, partitioning the target park according to the land use planning index data to obtain a plurality of plots, and conducting coincidence forecasting according to the actual space planning of the park; calculating the land area, the building volume ratio and the preset unit area load density by using a multilayer partition space load prediction algorithm, and predicting a target load set of each partition, so that a load set conforming to geographical distribution can be predicted, and the problem that a traditional load prediction result has larger deviation from the actual situation in geographical position distribution is solved; generating the total saturated annual load of the target park according to the target load sets of the plurality of bays so as to ensure that the load scale is consistent with the actual situation; and finally, comprehensively analyzing the saturated annual load total amount and the development time sequence of the target park, and determining the middle annual load total amount of the target park to obtain the actual load result of the park at each development stage, so that the accuracy of the load preset result is improved.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for predicting a park electrical load according to an embodiment of the present disclosure. The method of the embodiment of the application can be applied to computer equipment, including but not limited to smart phones, notebook computers, tablet computers, desktop computers, physical servers, cloud servers and other equipment. As shown in fig. 1, the method of the present embodiment includes steps S101 to S104, which are detailed as follows:
step S101, obtaining land use planning index data of a target park, and partitioning the target park according to the land use planning index data to obtain a plurality of plots, wherein the land use planning index data comprises land use properties, land area and building volume ratio;
in the step, according to the park controllability detailed planning file, the land property and the land area of each development unit in the park are determined, the partition space is established, and the building areas with different land properties are calculated according to the planning control volume ratio.
And S102, calculating the land area, the building volume ratio and the preset unit area load density by using a multilayer partition space load prediction algorithm, and predicting a target load set of each block.
In the step, the multilayer partition space load prediction is to divide the land of each region constructed and divided by groups into a plurality of lands of different types according to the use property, map the lands of different types into the electric loads of the land of the type through the load density indexes, stack the electric loads of different types in the region, accumulate the regional level loads constructed and divided by different groups, and finally form the prediction process of the system load from bottom to top. For the hierarchical division of the load of the power distribution network in the park, the process of the load prediction of the planning is divided into an intra-district classification load calculation part and a hierarchical partition load combination part, wherein the intra-district classification load calculation part is used for calculating the first-layer load, and the hierarchical partition load combination part is used for calculating the second-layer load according to the partition level of the load.
Wherein the first layer load: loads of the same type in the parcel are classified into n regions according to the group building sequence, and if loads of m types are classified into a certain parcel according to land occupation properties, the first layer of loads are a set of single parcel loads with the same type of load characteristics.
Second layer load: the parcel classifies the load, the collection of m types of loads for a parcel.
Third layer load: and the total load of the planning area is the total set of the loads of the partition areas in the planning area.
And step S103, generating the total saturated annual load of the target park according to the target load sets of the plurality of bays.
In this step, the total saturated annual load is the third layer load. Optionally, according to a hierarchical partition merging algorithm of the multi-layer partition space load prediction algorithm, generating a total saturated annual load of the target park according to a target load set of the plurality of bays and a preset third load concurrence rate, where a calculation formula of the hierarchical partition merging algorithm is:
Figure BDA0003561083230000071
L3is the total saturated annual load of the target park, K3Is the third load coincidence rate, L2jThe target load set of the jth parcel, and n is the total number of parcels of the target campus.
And step S104, comprehensively analyzing the saturated annual load total amount and the development time sequence of the target park, and determining the middle annual load total amount of the target park.
In this step, optionally, determining a project development progress and a project development trend in the target park according to the development time sequence of the target park; and determining the middle annual load total of the target park according to the project development progress and the project development trend and by combining the saturated annual load total.
By combining the development time sequence of the park, according to the production progress and the development trend of large projects in the park and referring to the existing research results, the power demand of the park in the near term is predicted, and the load total of the planning park in the middle year is predicted. Optionally, the predicted result can be compared, analyzed and checked by a transverse comparison method through analogy with similar regions.
In an embodiment, the step S102 includes:
predicting a homogeneous load set of the same load type in a plurality of districts according to the land area, the building volume ratio, the unit area load density and a preset first load simultaneous rate by utilizing the multi-layer subarea space load prediction algorithm, wherein each district comprises a plurality of land properties, and each land property corresponds to one load type;
and determining a target load set of each district according to the homogeneous load sets of the districts and a preset second load concurrence rate.
Optionally, an intra-zone classification load prediction algorithm of the multi-layer zone space load prediction algorithm is used to predict a plurality of similar load sets of the same load type in the zones according to the land area, the building volume ratio, the unit area load density and the first load concurrence rate, and a calculation formula of the intra-zone classification load prediction algorithm is as follows:
Figure BDA0003561083230000081
wherein L is1ijFor the homogeneous load set of the ith load type in the jth tile, RijThe building volume fraction, P, corresponding to the ith load type in the jth zoneijThe unit area load density, K, corresponding to the ith load type in the jth slice1Is the first load coincidence rate, SqijThe land area corresponding to the q plot belonging to the ith load type in the jth plot, and l is the total number of plots belonging to the ith load type in the jth plot.
Optionally, an intra-area load merging algorithm of the multi-layer partition space load prediction algorithm is used to determine a target load set of each of the partitions according to the homogeneous load sets and the second load concurrence rate of the plurality of partitions, and a calculation formula of the intra-area load merging algorithm is:
Figure BDA0003561083230000082
wherein L is2jThe target load set for the jth parcel, K2Is the second load coincidence rate, L1ijAnd m is the total number of the load types of the jth block.
In an embodiment, before the step S102, the method further includes:
acquiring load characteristic prior data of each land property;
determining said load density per unit area for each said property of right terrain based on said right terrain load feature prior data and said right terrain area.
In this alternative embodiment, the load characteristic prior data is the load characteristic of the distribution network of the campus where other distribution networks have developed maturity.
By way of example and not limitation, a load prediction scenario is provided below.
1. According to the park controllability detailed planning file, the land property and the land area of each development unit in the park are determined, the partition space is established, and the building areas with different land properties are calculated according to the planning control volume rate.
Figure BDA0003561083230000083
Figure BDA0003561083230000091
2. The load density index of each property of land in each district of the park in unit building area is determined by researching and referring to load density index values of developed and mature parks of the same type in China, fully considering park electricity utilization characteristics according to urban electricity planning regulations (GB/50293-1999) and park electricity utilization characteristics, referring to existing research results and electricity utilization levels of construction land for current projects at home and abroad and combining with urban land property load density index investigation results.
Figure BDA0003561083230000092
Figure BDA0003561083230000101
3. And comprehensively analyzing according to the determined load density indexes of the building area of each site property unit of each district of the park by combining with the actual condition of the park, and measuring and calculating the electric load of the distant view of the park by adopting a space load density method.
Figure BDA0003561083230000102
Figure BDA0003561083230000111
The load prediction result is as follows:
Figure BDA0003561083230000112
4. and (3) forecasting the recent power demand of the park and forecasting the load total of the planning park in the middle year by combining the development time sequence of the park and referring to the existing research results according to the production progress and the development trend of large projects in the park.
5. And comparing, analyzing and checking the prediction result by using a transverse comparison method through the similar analogy area.
In order to execute the forecasting method of the electric load of the park corresponding to the embodiment of the method, corresponding functions and technical effects are realized. Referring to fig. 2, fig. 2 is a block diagram illustrating a configuration of a device for predicting electrical loads of a campus according to an embodiment of the present application. For convenience of explanation, only the part related to the present embodiment is shown, and the device for predicting a campus electrical load according to the present embodiment includes:
an obtaining module 201, configured to obtain land use planning index data of a target park, and partition the target park according to the land use planning index data to obtain a plurality of districts, where the land use planning index data includes land use properties, land use area, and building volume ratio;
the operation module 202 is configured to calculate the land area, the building volume fraction and a preset load density per unit area by using a multi-layer partition space load prediction algorithm, and predict a target load set of each block;
a generating module 203, configured to generate a total saturated annual load of the target campus according to the target load sets of the multiple districts;
a determining module 204, configured to perform comprehensive analysis on the total saturated annual load amount and the development time sequence of the target campus, and determine the total middle annual load amount of the target campus.
In one embodiment, the operation module 202 includes:
the prediction unit is used for predicting the same-class load set of the same load types in a plurality of districts according to the land area, the building volume ratio, the unit area load density and a preset first load simultaneous rate by utilizing the multi-layer partition space load prediction algorithm, wherein each district comprises a plurality of land properties, and each land property corresponds to one load type;
a first determining unit, configured to determine a target load set of each of the segments according to the homogeneous load sets of the segments and a preset second load concurrence rate.
In an embodiment, the prediction unit is specifically configured to:
predicting a similar load set of the same load type in a plurality of the districts according to the land area, the building volume ratio, the unit area load density and the first load simultaneous rate by utilizing an intra-district classification load prediction algorithm of the multilayer partition space load prediction algorithm, wherein a calculation formula of the intra-district classification load prediction algorithm is as follows:
Figure BDA0003561083230000121
wherein L is1ijFor the homogeneous load set of the ith load type in the jth tile, RijThe building volume fraction, P, corresponding to the ith load type in the jth parcelijThe unit area load density, K, corresponding to the ith load type in the jth slice1Is the first load coincidence rate, SqijThe land area corresponding to the q plot belonging to the ith load type in the jth plot, and l is the land area belonging to the jth plotTotal number of plots for i load types.
In an embodiment, the first determining unit is specifically configured to:
determining a target load set of each parcel by using an intra-district load merging algorithm of the multi-layer partitioned space load prediction algorithm according to the homogeneous load sets and the second load concurrence rate of a plurality of parcels, wherein the intra-district load merging algorithm has a calculation formula as follows:
Figure BDA0003561083230000131
wherein L is2jThe target load set for jth parcel, K2Is the second load coincidence rate, L1ijThe homogeneous load set of the ith load type in the jth block is obtained, and m is the total number of the load types of the jth block.
In an embodiment, the generating module 203 is specifically configured to:
according to a hierarchical partition merging algorithm of the multilayer partition space load prediction algorithm, generating a total saturated annual load of the target park according to a target load set of the plurality of bays and a preset third load concurrence rate, wherein a calculation formula of the hierarchical partition merging algorithm is as follows:
Figure BDA0003561083230000132
L3is the total saturated annual load of the target park, K3Is the third load coincidence rate, L2jThe target load set of the jth parcel, and n is the total number of parcels of the target campus.
In one embodiment, the prediction apparatus further includes:
a second acquisition module, configured to acquire load feature prior data of each of the land characteristics;
and the second determination module is used for determining the load density per unit area of each land property according to the land load feature prior data and the land area.
In an embodiment, the determining module 204 includes:
the second determining unit is used for determining the project development progress and the project development trend in the target park according to the development time sequence of the target park;
and the third determining unit is used for determining the middle-year total load of the target park according to the project development progress and the project development trend and by combining the saturated year total load.
The above-described device for predicting a campus electrical load may implement the method for predicting a campus electrical load according to the above-described method embodiment. The alternatives in the above-described method embodiments are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the contents of the above method embodiments, and in this embodiment, details are not described again.
Fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application. As shown in fig. 3, the computer device 3 of this embodiment includes: at least one processor 30 (only one shown in fig. 3), a memory 31, and a computer program 32 stored in the memory 31 and executable on the at least one processor 30, the processor 30 implementing the steps of any of the above-described method embodiments when executing the computer program 32.
The computer device 3 may be a computing device such as a smart phone, a tablet computer, a desktop computer, and a cloud server. The computer device may include, but is not limited to, a processor 30, a memory 31. Those skilled in the art will appreciate that fig. 3 is merely an example of the computer device 3, and does not constitute a limitation of the computer device 3, and may include more or less components than those shown, or combine some of the components, or different components, such as input output devices, network access devices, etc.
The processor 30 may be a Central Processing Unit (CPU), and the processor 30 may be other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may in some embodiments be an internal storage unit of the computer device 3, such as a hard disk or a memory of the computer device 3. The memory 31 may also be an external storage device of the computer device 3 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the computer device 3. The memory 31 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 31 may also be used to temporarily store data that has been output or is to be output.
In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in any of the method embodiments described above.
The embodiments of the present application provide a computer program product, which when executed on a computer device, enables the computer device to implement the steps in the above method embodiments.
In several embodiments provided herein, it will be understood that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present application in detail, and it should be understood that the above-mentioned embodiments are only examples of the present application and are not intended to limit the scope of the present application. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the present application, may occur to those skilled in the art and are intended to be included within the scope of the present application.

Claims (10)

1. A method for forecasting park electrical loads, comprising:
the method comprises the steps of obtaining land use planning index data of a target park, and partitioning the target park according to the land use planning index data to obtain a plurality of districts, wherein the land use planning index data comprises land use properties, land area and building volume ratio;
calculating the land area, the building volume ratio and a preset unit area load density by utilizing a multilayer partition space load prediction algorithm, and predicting a target load set of each partition;
generating the total saturated annual load of the target park according to the target load sets of the plurality of bays;
and comprehensively analyzing the saturated annual load total amount and the development time sequence of the target park, and determining the middle annual load total amount of the target park.
2. The campus electrical load forecasting method according to claim 1, wherein the predicting the target load set of each of the districts by operating the land area, the building volume fraction and the preset load density per unit area using a multi-level zoning space load forecasting algorithm comprises:
predicting a homogeneous load set of the same load type in a plurality of districts according to the land area, the building volume ratio, the unit area load density and a preset first load simultaneous rate by utilizing the multi-layer subarea space load prediction algorithm, wherein each district comprises a plurality of land properties, and each land property corresponds to one load type;
and determining a target load set of each district according to the homogeneous load sets of the districts and a preset second load concurrence rate.
3. The campus electrical load forecasting method as claimed in claim 2, wherein the forecasting of the homogeneous load sets of the same load types in a plurality of the districts according to the land area, the building volume fraction, the unit area load density and a preset first load simultaneous rate by using the multi-level partitioned space load forecasting algorithm comprises:
predicting a similar load set of the same load type in a plurality of the districts according to the land area, the building volume ratio, the unit area load density and the first load simultaneous rate by utilizing an intra-district classification load prediction algorithm of the multilayer partition space load prediction algorithm, wherein a calculation formula of the intra-district classification load prediction algorithm is as follows:
Figure FDA0003561083220000021
wherein L is1ijFor the homogeneous load set of the ith load type in the jth zone, RijThe building volume fraction, P, corresponding to the ith load type in the jth zoneijThe unit area load density, K, corresponding to the ith load type in the jth slice1Is the first load coincidence rate, SqijAnd l is the total number of the plots belonging to the ith load type in the jth plot.
4. The method for forecasting loads on a campus of claim 2 wherein said determining a target load set for each said sector based on said homogeneous load sets and a predetermined second load concurrency rate for a plurality of said sectors comprises:
determining a target load set of each parcel by using an intra-district load merging algorithm of the multi-layer partitioned space load prediction algorithm according to the homogeneous load sets and the second load concurrence rate of a plurality of parcels, wherein the intra-district load merging algorithm has a calculation formula as follows:
Figure FDA0003561083220000022
wherein L is2jThe target load set for jth parcel, K2Is the second load coincidence rate, L1ijThe homogeneous load set of the ith load type in the jth block is obtained, and m is the total number of the load types of the jth block.
5. The method for predicting a power load on a campus of any one of claims 1 to 4, wherein the generating a total annual load saturation amount of the target campus based on a target load set of the plurality of the districts includes:
according to a hierarchical partition merging algorithm of the multilayer partition space load prediction algorithm, generating a total saturated annual load of the target park according to a target load set of the plurality of bays and a preset third load concurrence rate, wherein a calculation formula of the hierarchical partition merging algorithm is as follows:
Figure FDA0003561083220000023
L3is the total saturated annual load of the target park, K3Is the third load coincidence rate, L2jThe target load set of the jth district, and n is the total number of districts of the target park.
6. The campus electrical load forecasting method as claimed in any one of claims 1 to 4, wherein the method further comprises, before predicting the target load set of each of the blocks by using the multi-level zoning space load forecasting algorithm to calculate the land area, the building volume ratio and the preset load density per unit area:
acquiring load characteristic prior data of each land property;
determining said load density per unit area for each said property of right terrain based on said right terrain load feature prior data and said right terrain area.
7. The method of forecasting loads on a campus of claim 1, wherein said comprehensively analyzing the total saturated annual load and the development time sequence of said target campus to determine the total mid-annual load of said target campus comprises:
determining a project development progress and a project development trend in the target park according to the development time sequence of the target park;
and determining the middle annual load sum of the target park according to the project development progress and the project development trend and by combining the saturated annual load sum.
8. A prediction apparatus for a park electrical load, comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring land use planning index data of a target park, and partitioning the target park according to the land use planning index data to obtain a plurality of plots, and the land use planning index data comprises land use properties, land area and building volume ratio;
the operation module is used for operating the land area, the building volume ratio and the preset unit area load density by utilizing a multilayer partition space load prediction algorithm and predicting a target load set of each block;
the generating module is used for generating the total saturated annual load of the target park according to the target load sets of the plurality of bays;
and the determining module is used for comprehensively analyzing the saturated annual load total amount and the development time sequence of the target park and determining the middle annual load total amount of the target park.
9. A computer device comprising a processor and a memory for storing a computer program which, when executed by the processor, implements a method of forecasting a park electrical load as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out a method for forecasting a park electrical load according to any one of claims 1 to 7.
CN202210292246.5A 2022-03-23 2022-03-23 Method, device, equipment and storage medium for forecasting park electrical load Pending CN114638423A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116029480A (en) * 2023-03-28 2023-04-28 广东电网有限责任公司 Proxy purchase electricity measuring method and system thereof

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