CN115455367A - Interruptible load quantity calculation method, system, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a method, a system, equipment and a storage medium for calculating interruptible load capacity, wherein the method comprises the following steps: the method comprises the steps of determining a target type of a daily load curve of a target user based on a preset load classification model, obtaining an initial type parameter of the target user corresponding to the daily load curve of the target user based on a metering parameter in the daily load curve of the target user, determining a target type parameter group and a type parameter distribution probability value respectively corresponding to each type parameter in the target type parameter group from a target preset type parameter distribution probability table corresponding to the target type based on a user identification and the initial type parameter of the target user, and calculating the interruptible load of the target user according to the target type parameter group and the type parameter distribution probability value. The invention realizes the accurate calculation of the interruptible load quantity, thereby reducing the risk of mistaken removal of the load type with high requirement on the power supply reliability and improving the power supply reliability.
Description
Technical Field
The present invention relates to the field of interruptible load management technologies, and in particular, to a method, a system, a device, and a storage medium for calculating an interruptible load amount.
Background
The interruptible load refers to a load portion in which a power consumer can interrupt in a power peak period or an emergency situation. The power consumer and the power supplier realize the management of the interruptible load by signing an interruptible load contract. Existing interruptible load contracts are formulated based on the total amount of load that a user can interrupt.
However, with the development of economy, the types of loads in the power system are also gradually increasing. And the requirements of different load types on the power supply reliability also differ. If the load is removed according to the contract made by the total interruptible load, the risk of mistaken removal of the load type with high requirement on the power supply reliability is increased, and the power supply reliability is further reduced.
Disclosure of Invention
Embodiments of the present invention provide a method, a system, a device, and a storage medium for calculating an interruptible load amount, so as to achieve the purpose of reducing the risk of load type false removal with high requirement on power supply reliability, and improving power supply reliability. The specific technical scheme is as follows:
a method of calculating an interruptible load amount, the method comprising:
determining a target type of a daily load curve of a target user based on a preset load classification model;
obtaining an initial type parameter of the target user corresponding to the daily load curve of the target user based on the metering parameter in the daily load curve of the target user;
determining a target type parameter group and type parameter distribution probability values respectively corresponding to various types of parameters in the target type parameter group from a target preset type parameter distribution probability table corresponding to a target type based on the user identification of the target user and the initial type parameters;
and calculating the interruptible load quantity of the target user according to the target type parameter group and the distribution probability value of each type parameter.
Optionally, the calculating an initial type parameter of the target user corresponding to the daily load curve of the target user based on the measurement parameter in the daily load curve of the target user includes:
by the formula:
obtaining the daily load rate LR of the target user, wherein P is the total daily power consumption, and EL max Is the daily maximum load;
by the formula:
θ 0 ＝1-LR
obtaining the initial type parameter, wherein θ 0 Is the initial type parameter.
Optionally, the determining, based on the user identifier of the target user and the initial type parameter, a target type parameter group and a type parameter distribution probability value respectively corresponding to each type parameter in the target type parameter group from a target preset type parameter distribution probability table corresponding to the target type includes:
respectively calculating the parameter difference value of each reference type parameter and the initial type parameter in the target preset type parameter distribution probability table;
determining a reference type parameter corresponding to the minimum value in the parameter difference values as a target type parameter, and setting the type parameter distribution probability value corresponding to the target type parameter as a first numerical value;
determining a target adjacent type parameter of the target type parameter in the target preset type parameter distribution probability table, and setting the type parameter distribution probability value corresponding to the target adjacent type parameter as a second value, wherein the target adjacent type parameter is a reference type parameter of which the value is greater than that of the target type parameter and is adjacent to the target type parameter, and the sum of the first value and the second value is 1;
obtaining the target type parameter set comprising the target type parameter and the target neighbor type parameter.
Optionally, the calculating an interruptible load amount of the target user according to the target type parameter group and each of the type parameter distribution probability values includes:
by the formula:
determining an interruptible load X for the target user i i (θ j ) Wherein, said P m Is a preset peak hour unit electricity price parameter, P 0 Is a preset selling unit electricity price parameter, the lambda is a preset power transmission unit cost parameter, and the P is i (θ j ) Is the jth of the target user iNumber theta j Corresponding type parameter distribution probability value, theta j+1 Is the target type parameter θ j Target neighbor type parameter of, said K 1 Is to preset a first outage cost coefficient, K 2 Is a preset second outage cost coefficient, J is the total number of reference type parameters in the target preset type parameter distribution probability table, P is i (θ n ) Is the nth reference type parameter theta of the target user i n And distributing probability values to the corresponding type parameters.
Optionally, the training process of the preset load classification model includes:
adding the acquired first number of historical daily load curves to a sample data set;
randomly selecting a target historical daily load curve from the sample data set to determine as a clustering center curve in a clustering center curve set;
respectively calculating Euclidean distances between each historical daily load curve in the sample data set except the clustering center curve and the clustering center curve in the clustering center curve set;
determining a historical daily load curve corresponding to the Euclidean distance with the minimum value in each Euclidean distance as a clustering center curve in the clustering center curve set, and returning to the step of respectively calculating the Euclidean distances between each historical daily load curve except the clustering center curve in the sample data set and the clustering center curve in the clustering center curve set;
and when the number of the clustering center curves in the clustering center curve set is equal to the preset classification number, stopping the calculation to obtain the preset load classification model.
A computing system of interruptible load amounts, the computing system comprising:
the curve classification module is used for determining a target type of a daily load curve of a target user based on a preset load classification model;
the parameter calculation module is used for solving initial type parameters of the target user corresponding to the daily load curve of the target user based on the metering parameters in the daily load curve of the target user;
a data determining module, configured to determine, based on the user identifier of the target user and the initial type parameter, a target type parameter group and type parameter distribution probability values respectively corresponding to various types of parameters in the target type parameter group from a target preset type parameter distribution probability table corresponding to the target type;
and the load quantity calculating module is used for calculating the interruptible load quantity of the target user according to the target type parameter group and the distribution probability value of each type parameter.
Optionally, the parameter calculation module is configured to:
by the formula:
obtaining the daily load rate LR of the target user, wherein P is the total daily power consumption in the metering parameters, and EL max Is the daily maximum load in the metering parameter;
by the formula:
θ 0 ＝1-LR
obtaining the initial type parameter, wherein θ 0 Is the initial type parameter.
Optionally, the data determination module is configured to:
respectively calculating the parameter difference between each reference type parameter and the initial type parameter in the target preset type parameter distribution probability table;
determining a reference type parameter corresponding to the minimum value in the parameter difference values as a target type parameter, and setting the type parameter distribution probability value corresponding to the target type parameter as a first numerical value;
determining a target adjacent type parameter of the target type parameter in the target preset type parameter distribution probability table, and setting the type parameter distribution probability value corresponding to the target adjacent type parameter as a second numerical value, wherein the target adjacent type parameter is a reference type parameter of which the numerical value of the reference type parameter is greater than that of the target type parameter and adjacent to the target type parameter, and the sum of the first numerical value and the second numerical value is 1;
obtaining the target type parameter group including the target type parameter and the target neighbor type parameter.
Optionally, the load metering module is configured to:
by the formula:
determining an interruptible load X of the target user i i (θ j ) Wherein, said P m Is a preset peak hour unit electricity price parameter, P 0 Is a preset selling unit electricity price parameter, the lambda is a preset power transmission unit cost parameter, and the P is i (θ j ) Is the jth said target type parameter theta of said target user i j Corresponding type parameter distribution probability value, theta j+1 Is the target type parameter θ j Target neighbor type parameter of (c), the K 1 Is to preset a first outage cost coefficient, K 2 Is a preset second outage cost coefficient, J is the total number of reference type parameters in the target preset type parameter distribution probability table, P is i (θ n ) Is the nth reference type parameter theta of the target user i n And distributing probability values to the corresponding type parameters.
Optionally, the computing system of interruptible load amount further comprises:
the model training module is used for adding the acquired first number of historical daily load curves to the sample data set;
randomly selecting a target historical daily load curve from the sample data set to determine as a clustering center curve in a clustering center curve set;
respectively calculating Euclidean distances between each historical daily load curve in the sample data set except the clustering center curve and the clustering center curve in the clustering center curve set;
determining a historical daily load curve corresponding to the Euclidean distance with the minimum value in each Euclidean distance as a clustering center curve in the clustering center curve set, and returning to execute the step of respectively calculating the Euclidean distances between each historical daily load curve except the clustering center curve in the sample data set and the clustering center curve in the clustering center curve set;
and when the number of the clustering center curves in the clustering center curve set is equal to the preset classification number, stopping the calculation to obtain the preset load classification model.
A computing device of an interruptible load amount, the computing device comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of calculating an interruptible load amount as any of the above.
A computer readable storage medium having instructions which, when executed by a processor of a computing device of an interruptible load amount, enable the computing device to perform a method of computing an interruptible load amount as any one of the above.
The method, the system, the equipment and the storage medium for calculating the interruptible load capacity provided by the embodiment of the invention can be used for classifying the daily load curve by utilizing the preset load classification model, so that the determination precision of the type parameters can be improved, and the precision of the subsequent generation of the interruptible load capacity can be improved. Meanwhile, based on the metering parameters of the daily load curve, initial type parameters capable of representing the type of the target user are determined. The problems of reduced calculation efficiency caused by huge calculation data quantity and reduced calculation precision caused by interference among data are solved. Finally, the invention can realize the accurate calculation of the interruptible load of each target user belonging to the same target type according to each target type parameter and the type parameter distribution probability value in a target preset type parameter distribution probability table. Therefore, the method and the device realize accurate calculation of the interruptible load quantity, thereby reducing the risk of mistaken removal of the load type with high requirement on power supply reliability and improving the power supply reliability.
Of course, it is not necessary for any product or method to achieve all of the above-described advantages at the same time for practicing the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for calculating an interruptible load amount according to an embodiment of the present invention;
FIG. 2 is a block diagram of a computing system that may interrupt a load amount in accordance with an alternate embodiment of the present invention;
FIG. 3 is a block diagram of a computing device capable of interrupting a load according to another alternative embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the present invention provides a method for calculating an interruptible load amount, as shown in fig. 1, where the method includes:
s101, determining a target type of a daily load curve of a target user based on a preset load classification model.
The daily Load curve (Load curve) is a curve for representing the change of each type of power Load in the power system with time.
Optionally, in an optional embodiment of the present invention, in an actual application scenario, the target types may be multiple. For example: if the daily load curve shows the trend that the daily load is low and the night load is reduced from high, the target type corresponding to the daily load curve is indicated as the residential user. And if the daily load curve shows that the load is high all day, indicating that the target type corresponding to the daily load curve is an industrial user. If the daily load curve shows that the daily load is high and the night load is high and low, the target type corresponding to the daily load curve is a commercial user.
Optionally, in another optional embodiment of the present invention, the preset load classification model may be a model constructed based on a K-means clustering algorithm (K-means). The daily load curve represents the use condition of the user load in different periods. And different types of user loads correspond to different types of parameters. Therefore, the method and the device can improve the determination precision of the type parameters by classifying the daily load curve by using the preset load classification model. Thereby improving the accuracy of the subsequent generation of interruptible load quantities.
And S102, obtaining initial type parameters of the target user corresponding to the daily load curve of the target user based on the metering parameters in the daily load curve of the target user.
Optionally, in an alternative embodiment of the present invention, the daily load curve contains a large amount of load data of the target user in a natural day. If the subsequent calculation of the interruptible load amount is performed based on a large amount of load data, the calculation efficiency and the calculation accuracy may be reduced. Therefore, the method and the device determine initial type parameters capable of representing the type of the target user based on the metering parameters of the daily load curve. The problems that the calculation efficiency is reduced due to the large number of operation data, and the calculation accuracy is reduced due to the interference among the data are solved.
S103, based on the user identification and the initial type parameters of the target user, determining a target type parameter group and type parameter distribution probability values respectively corresponding to various types of parameters in the target type parameter group from a target preset type parameter distribution probability table corresponding to the target type.
Optionally, in an optional embodiment of the present invention, since the daily load curves of different users cannot be completely fitted, the initial type parameters of different target users having the same target type daily load curve may be different. Meanwhile, if the user temporarily increases the load or extends the load power consumption time, the daily load curve cannot reflect the real power consumption situation, and the initial type parameters are also changed. Therefore, in order to accurately calculate the interruptible load quantities of a large number of users with the same target type daily load curve, it is necessary to uniformly divide the initial type parameters of each target user and determine the occurrence probability of multiple type parameters that may occur to the same user.
Optionally, in another optional embodiment of the present invention, the type parameter distribution probability value may be a matching probability between an initial type parameter determined based on a daily load curve of the target user and a type parameter in a corresponding target preset type parameter distribution probability table. The distribution probability value of the type parameters can be set according to the historical daily load curve and the corresponding type parameters after probability statistics.
Alternatively, in another alternative embodiment of the present invention, a specific form of the target preset-type parameter distribution probability table may be as shown in table 1 below.
TABLE 1
The type parameter distribution probability values of preset types of six users belonging to the same target type are recorded in table 1.
And S104, calculating the interruptible load quantity of the target user according to the target type parameter group and the distribution probability value of each type of parameter.
Optionally, in an optional embodiment of the present invention, the target preset type parameter distribution probability table records all target type parameters that may appear in each target user belonging to the same target type and a type parameter distribution probability corresponding to each target type parameter. Therefore, according to the target type parameters and the type parameter distribution probability values in a target preset type parameter distribution probability table, the interruptible load quantity of each target user belonging to the same target type can be accurately calculated.
According to the invention, the daily load curve is classified by using the preset load classification model, so that the determination precision of the type parameter can be improved, and the precision of the subsequent generation of the interruptible load quantity is improved. Meanwhile, based on the metering parameters of the daily load curve, initial type parameters capable of representing the type of the target user are determined. The problems of reduced calculation efficiency caused by huge calculation data quantity and reduced calculation precision caused by interference among data are solved. Finally, the invention can realize accurate calculation of the interruptible load quantity of each target user belonging to the same target type according to each target type parameter and the type parameter distribution probability value in a target preset type parameter distribution probability table. Therefore, the method and the device realize accurate calculation of the interruptible load quantity, thereby reducing the risk of mistaken removal of the load type with high requirement on power supply reliability and improving the power supply reliability.
Optionally, the measuring parameters include total daily power consumption and maximum daily load, and the initial type parameters of the target user corresponding to the daily load curve of the target user are obtained based on the measuring parameters in the daily load curve of the target user, including:
by the formula:
obtaining the daily load rate LR of the target user, wherein P is the total daily power consumption, EL max Is the daily maximum load;
by the formula:
θ 0 ＝1-LR
determining an initial type parameter, wherein θ 0 Is an initial type parameter.
Wherein the daily maximum load amount EL is max Is in kilowatt-hours and the total daily electricity usage is in kilowatts. To facilitate uniform computation, it is therefore necessary to multiply by 24 hours in uniform units.
Optionally, determining, based on the user identifier of the target user and the initial type parameter, a target type parameter set and type parameter distribution probability values respectively corresponding to each type parameter in the target type parameter set from a target preset type parameter distribution probability table corresponding to the target type, where the determining includes:
respectively calculating the parameter difference value of each reference type parameter and the initial type parameter in the target preset type parameter distribution probability table;
determining a reference type parameter corresponding to the minimum value in the parameter difference values as a target type parameter, and setting a type parameter distribution probability value corresponding to the target type parameter as a first numerical value;
determining a target adjacent type parameter of the target type parameter in the target preset type parameter distribution probability table, and setting a type parameter distribution probability value corresponding to the target adjacent type parameter as a second numerical value, wherein the target adjacent type parameter is the reference type parameter of which the numerical value is greater than that of the target type parameter and is adjacent to the target type parameter, and the sum of the first numerical value and the second numerical value is 1;
a set of target type parameters is obtained comprising a target type parameter and a target neighbor type parameter.
Optionally, calculating an interruptible load amount of the target user according to the target type parameter group and the distribution probability value of each type of parameter, including:
by the formula:
determining an interruptible load X for a target user i i (θ j ) Wherein P is m Is a preset peak hour unit electricity price parameter, P 0 Is a preset selling unit electricity price parameter, lambda is a preset power transmission unit cost parameter, P i (θ j ) Is the jth target type parameter theta of the target user i j Corresponding type parameter distribution probability value theta j+1 Is the target type parameter theta j Target neighbor type parameter, K 1 Is to preset a first power-off cost coefficient, K 2 Is a preset second outage cost coefficient, J is the total number of reference type parameters in the target preset type parameter distribution probability table, P i (θ n ) Is the nth reference type parameter theta of the target user i n And distributing probability values according to the type parameters.
Optionally, the training process of the preset load classification model includes:
adding the acquired first number of historical daily load curves to a sample data set;
randomly selecting a target historical daily load curve from the sample data set to determine the target historical daily load curve as a clustering center curve in the clustering center curve set;
respectively calculating Euclidean distances between each historical daily load curve in the sample data set except the clustering center curve and the clustering center curve in the clustering center curve set;
determining the historical daily load curve corresponding to the Euclidean distance with the minimum value in the Euclidean distances as a clustering center curve in a clustering center curve set, and returning to execute the step of respectively calculating the Euclidean distances between the historical daily load curves except the clustering center curve in the sample data set and the clustering center curve in the clustering center curve set;
and when the number of the clustering center curves in the clustering center curve set is equal to the preset classification number, stopping the calculation to obtain a preset load classification model.
Corresponding to the above method embodiment, the present invention further provides a computing system capable of interrupting the load amount, as shown in fig. 2, the computing system includes:
a curve classification module 201, configured to determine a target type of a daily load curve of a target user based on a preset load classification model;
the parameter calculation module 202 is configured to obtain an initial type parameter of the target user, which corresponds to the daily load curve of the target user, based on the metering parameter in the daily load curve of the target user;
a data determining module 203, configured to determine, based on a user identifier of a target user and an initial type parameter, a target type parameter group and a type parameter distribution probability value respectively corresponding to each type parameter in the target type parameter group from a target preset type parameter distribution probability table corresponding to the target type;
and the load quantity calculating module 204 is configured to calculate an interruptible load quantity of the target user according to the target type parameter group and the distribution probability values of the types of parameters.
Optionally, the parameter calculating module 202 is configured to:
by the formula:
obtaining the daily load rate LR of the target user, wherein P is the total daily power consumption in the metering parameters, EL max Is the daily maximum load in the metering parameters;
by the formula:
θ 0 ＝1-LR
obtaining an initial type parameter, wherein 0 Is an initial type parameter.
Optionally, the data determining module 203 is configured to:
respectively calculating the parameter difference value of each reference type parameter and the initial type parameter in the target preset type parameter distribution probability table;
determining a reference type parameter corresponding to the minimum value in the parameter difference values as a target type parameter, and setting a type parameter distribution probability value corresponding to the target type parameter as a first numerical value;
determining a target adjacent type parameter of the target type parameter in the target preset type parameter distribution probability table, and setting a type parameter distribution probability value corresponding to the target adjacent type parameter as a second numerical value, wherein the target adjacent type parameter is the reference type parameter of which the numerical value is greater than that of the target type parameter and is adjacent to the target type parameter, and the sum of the first numerical value and the second numerical value is 1;
a target type parameter set comprising a target type parameter and a target neighbor type parameter is obtained.
Optionally, the load amount calculating module 204 is configured to:
by the formula:
determining an interruptible load X for a target user i i (θ j ) Wherein P is m Is a preset peak hour unit electricity price parameter, P 0 Is a preset selling unit electricity price parameter, lambda is a preset power transmission unit cost parameter, P i (θ j ) Is the jth target type parameter theta of the target user i j Corresponding type parameter distribution probability value theta j+1 Is the target type parameter theta j Target neighbor type parameter, K 1 Is to preset a first power-off cost coefficient, K 2 Is a preset second outage cost coefficient, J is the total number of reference type parameters in the target preset type parameter distribution probability table, P i (θ n ) Is the nth reference type parameter theta of the target user i n The corresponding type parameter distribution probability value.
Optionally, the computing system for interruptible load amount as shown in fig. 2 further comprises:
the model training module is used for adding the acquired first number of historical daily load curves to the sample data set;
randomly selecting a target historical daily load curve from the sample data set to determine the target historical daily load curve as a clustering center curve in the clustering center curve set;
respectively calculating Euclidean distances between each historical daily load curve in the sample data set except the clustering center curve and the clustering center curve in the clustering center curve set;
determining the historical daily load curve corresponding to the Euclidean distance with the minimum value in each Euclidean distance as a clustering center curve in a clustering center curve set, and returning to the step of respectively calculating the Euclidean distances between each historical daily load curve except the clustering center curve in the sample data set and the clustering center curve in the clustering center curve set;
and when the number of the clustering center curves in the clustering center curve set is equal to the preset classification number, stopping the calculation to obtain a preset load classification model.
An embodiment of the present invention further provides a computing device capable of interrupting a load amount, as shown in fig. 3, where the computing device includes:
a processor 301;
a memory 302 for storing instructions executable by the processor 301;
wherein the processor 301 is configured to execute instructions to implement the method of calculating an interruptible load amount as any of the above.
Embodiments of the present invention also provide a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of a computing device capable of interrupting a load amount, the computing device is enabled to execute any one of the methods for calculating an interruptible load amount as described above.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), including at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the system embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.
Claims (10)
1. A method of calculating an interruptible load amount, the method comprising:
determining a target type of a daily load curve of a target user based on a preset load classification model;
obtaining an initial type parameter of the target user corresponding to the daily load curve of the target user based on the metering parameter in the daily load curve of the target user;
determining a target type parameter group and type parameter distribution probability values respectively corresponding to various types of parameters in the target type parameter group from a target preset type parameter distribution probability table corresponding to a target type based on the user identification of the target user and the initial type parameters;
and calculating the interruptible load quantity of the target user according to the target type parameter group and the distribution probability value of each type parameter.
2. The method according to claim 1, wherein the metering parameters comprise total daily power consumption and maximum daily load, and the obtaining initial type parameters of the target user corresponding to the daily load curve of the target user based on the metering parameters in the daily load curve of the target user comprises:
by the formula:
obtaining the daily load rate LR of the target user, wherein P is the total daily power consumption, and EL is max Is the daily maximum load;
by the formula:
θ 0 ＝1-LR
obtaining the initial type parameter, wherein θ 0 Is the initial type parameter.
3. The method of claim 2, wherein determining a target type parameter set and type parameter distribution probability values corresponding to respective types of parameters in the target type parameter set from a target preset type parameter distribution probability table corresponding to the target type based on the subscriber identity of the target subscriber and the initial type parameter comprises:
respectively calculating the parameter difference value of each reference type parameter and the initial type parameter in the target preset type parameter distribution probability table;
determining a reference type parameter corresponding to the minimum value in the parameter difference values as a target type parameter, and setting the type parameter distribution probability value corresponding to the target type parameter as a first numerical value;
determining a target adjacent type parameter of the target type parameter in the target preset type parameter distribution probability table, and setting the type parameter distribution probability value corresponding to the target adjacent type parameter as a second value, wherein the target adjacent type parameter is a reference type parameter of which the value is greater than that of the target type parameter and is adjacent to the target type parameter, and the sum of the first value and the second value is 1;
obtaining the target type parameter group including the target type parameter and the target neighbor type parameter.
4. The method of claim 3, wherein said calculating an interruptible load amount for the target user based on the target set of type parameters and each of the type parameter distribution probability values comprises:
by the formula:
determining an interruptible load X for the target user i i (θ j ) Wherein, said P m Is a preset peak hour unit electricity price parameter, P 0 Is a preset selling unit electricity price parameter, the lambda is a preset power transmission unit cost parameter, and the P is i (θ j ) Is the jth said target type parameter theta of said target user i j Corresponding type parameter distribution probability value, theta j+1 Is the target type parameter θ j Target neighbor type parameter of (c), the K 1 Is to preset a first outage cost coefficient, K 2 Is a preset second outage cost coefficient, J is a total number of reference type parameters in the target preset type parameter distribution probability table, P is i (θ n ) Is the nth reference type parameter theta of the target user i n And distributing probability values of the corresponding type parameters.
5. The method of claim 1, wherein the training process of the preset load classification model comprises:
adding the acquired first number of historical daily load curves to a sample data set;
randomly selecting a target historical daily load curve from the sample data set to determine as a clustering center curve in a clustering center curve set;
respectively calculating Euclidean distances between each historical daily load curve in the sample data set except the clustering center curve and the clustering center curve in the clustering center curve set;
determining a historical daily load curve corresponding to the Euclidean distance with the minimum value in each Euclidean distance as a clustering center curve in the clustering center curve set, and returning to execute the step of respectively calculating the Euclidean distances between each historical daily load curve except the clustering center curve in the sample data set and the clustering center curve in the clustering center curve set;
and when the number of the clustering center curves in the clustering center curve set is equal to the preset classification number, stopping the calculation to obtain the preset load classification model.
6. A computing system that can interrupt a load amount, the computing system comprising:
the curve classification module is used for determining a target type of a daily load curve of a target user based on a preset load classification model;
the parameter calculation module is used for solving initial type parameters of the target user corresponding to the daily load curve of the target user based on the metering parameters in the daily load curve of the target user;
a data determining module, configured to determine, based on the user identifier of the target user and the initial type parameter, a target type parameter group and type parameter distribution probability values respectively corresponding to various types of parameters in the target type parameter group from a target preset type parameter distribution probability table corresponding to the target type;
and the load quantity calculation module is used for calculating the interruptible load quantity of the target user according to the target type parameter group and the distribution probability value of each type parameter.
7. The computing system of claim 6, wherein the parameter calculation module is configured to:
by the formula:
obtaining the daily load rate LR of the target user, wherein P is the total daily power consumption in the metering parameters, and EL max Is the daily maximum load in the metering parameter;
by the formula:
θ 0 ＝1-LR
obtaining the initial type parameter, wherein the theta 0 Is the initial type parameter.
8. The computing system of claim 7, wherein the data determination module is configured to:
respectively calculating the parameter difference between each reference type parameter and the initial type parameter in the target preset type parameter distribution probability table;
determining a reference type parameter corresponding to the minimum value in the parameter difference values as a target type parameter, and setting the type parameter distribution probability value corresponding to the target type parameter as a first numerical value;
determining a target adjacent type parameter of the target type parameter in the target preset type parameter distribution probability table, and setting the type parameter distribution probability value corresponding to the target adjacent type parameter as a second numerical value, wherein the target adjacent type parameter is a reference type parameter of which the numerical value of the reference type parameter is greater than that of the target type parameter and adjacent to the target type parameter, and the sum of the first numerical value and the second numerical value is 1;
obtaining the target type parameter set comprising the target type parameter and the target neighbor type parameter.
9. A computing device of interruptible load amounts, the computing device comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of calculating an interruptible load amount according to any one of claims 1-5.
10. A computer-readable storage medium, wherein instructions in the computer-readable storage medium, when executed by a processor of a computing device of an interruptible load amount, enable the computing device to perform the method of computing an interruptible load amount according to any one of claims 1-5.
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