CN113837597B - Analysis method, device, equipment and storage medium for electricity utilization opposite-standard mining - Google Patents
Analysis method, device, equipment and storage medium for electricity utilization opposite-standard mining Download PDFInfo
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Abstract
The embodiment of the invention provides an analysis method, a device, equipment and a storage medium for electric power consumption opposite-standard mining, and the analysis method for electric power consumption opposite-standard mining comprises the following steps: acquiring electricity consumption data of a target user; acquiring electricity consumption data of each unit in the industry of the target user, and calculating to obtain an average value of the electricity consumption data according to the electricity consumption data of each unit in the industry of the target user; and comparing and analyzing the electricity consumption data of the target user with the average value of the electricity consumption data. The embodiment of the invention provides an analysis method, an analysis device, analysis equipment and a storage medium for electricity utilization benchmarking, which are used for improving the electricity utilization operation quality and management level of a target user and enhancing the competitiveness of equipment maintenance service of the target user.
Description
Technical Field
The present invention relates to the power grid technology, and in particular, to a method, an apparatus, a device, and a storage medium for analyzing a power consumption target.
Background
The bid-centering submerged work is an effective management measure for actively coping with market, changing pressure into power, leading two eyes to be inward, deeply digging potential and enhancing the survival development capability and efficiency and profitability of the enterprise under the situation that the market competition of the customer equipment is very intense. To develop the target dig and dive, the work has good effect, the target must be found, the plan is made, the implementation scheme with strong operability is formulated, the process control is tracked and guided, the examination and the examination are timely carried out, the comprehensive participation is realized, the regular communication learning is carried out, the continuous summary is improved, and the long-lasting development is further carried out.
Under the services of customer equipment, such as maintenance, a power supply company or an electric power operation and maintenance company faces the problem of how to improve the power utilization operation quality of the customer equipment for a target customer.
Disclosure of Invention
The embodiment of the invention provides an analysis method, an analysis device, analysis equipment and a storage medium for electricity utilization benchmarking, which are used for improving the electricity utilization operation quality and management level of a target user and enhancing the competitiveness of equipment maintenance service of the target user.
In a first aspect, an embodiment of the present invention provides a method for analyzing a power consumption target dig, including:
acquiring electricity consumption data of a target user;
acquiring electricity consumption data of each unit in the industry of the target user, and calculating to obtain an average value of the electricity consumption data according to the electricity consumption data of each unit in the industry of the target user;
and comparing and analyzing the electricity consumption data of the target user with the average value of the electricity consumption data.
Optionally, the electricity usage data includes electricity usage for a plurality of months.
Optionally, the method further comprises: the ranking of the unit GDP annual energy consumption of the target user in the industry where the target user is located is obtained.
Optionally, the electricity usage data includes a comprehensive unit price for a plurality of months;
wherein the comprehensive unit price is the ratio of the total electricity charge to the total electricity consumption.
Optionally, the electricity consumption data includes peak electricity proportions of a plurality of months;
the peak electricity proportion is the ratio of the peak period electricity consumption to the total electricity consumption.
Optionally, the electricity consumption data includes a load rate of a plurality of months;
wherein the load factor is the ratio of the average load to the rated capacity.
Optionally, the electricity consumption data includes a force adjustment coefficient; wherein, the power adjustment coefficient is M1, the power adjustment electric charge is M2, the basic electric charge is M3, the electricity consumption electric charge is M4, satisfy: m1=m2/(m3+m4).
In a second aspect, an embodiment of the present invention provides an analysis apparatus for electric power consumption dig, including:
the power consumption data acquisition module is used for acquiring power consumption data of a target user;
the power consumption average value acquisition module is used for acquiring power consumption data of each unit in the industry of the target user, and calculating to obtain a power consumption data average value according to the power consumption data of each unit in the industry of the target user;
and the comparison and analysis module is used for comparing and analyzing the electricity consumption data of the target user with the average value of the electricity consumption data.
In a third aspect, an embodiment of the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements a method as described in the first aspect.
The embodiment of the invention provides an analysis method for electricity consumption standard mining, which is used for acquiring electricity consumption data of a target user, acquiring electricity consumption data of each unit in the industry of the target user, calculating to obtain an average value of the electricity consumption data according to the electricity consumption data of each unit in the industry of the target user, and comparing and analyzing the electricity consumption data of the target user with the average value of the electricity consumption data. The embodiment of the invention improves the electricity utilization operation quality and management level of the target user and enhances the competitiveness of the equipment maintenance service of the target user.
Drawings
FIG. 1 is a flow chart of an analysis method for electric power utilization opposite mining according to a first embodiment of the present invention;
FIG. 2 is a graph of trend analysis of target user versus power consumption;
FIG. 3 is a graph of power consumption trend versus standard analysis;
FIG. 4 is a graph of a target user's comparative integrated monovalent trend analysis;
FIG. 5 is a graph of comprehensive unit price versus standard analysis;
FIG. 6 is a graph of trend analysis of target user peaks Gu Chazhi;
FIG. 7 is a graph of peak-to-peak ratio versus standard analysis;
FIG. 8 is a graph of annual load rate versus standard analysis;
FIG. 9 is a graph of weekend load rate versus label analysis;
FIG. 10 is a graph of work day load rate versus standard analysis;
FIG. 11 is a graph of spring festival duty versus standard analysis;
FIG. 12 is a graph of load factor versus standard analysis;
FIG. 13 is a graph of a trend analysis of target user power factor;
FIG. 14 is a graph of force modulation factor versus standard analysis;
FIG. 15 is a schematic diagram of an analysis device for electric pair logging in accordance with a second embodiment of the present invention;
fig. 16 is a schematic structural diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of an analysis method for electric power consumption opposite-mining according to a first embodiment of the present invention, and referring to fig. 1, the method includes:
s110, acquiring power consumption data of a target user.
The target user may be one target user or a plurality of target users.
S120, acquiring electricity consumption data of each unit in the industry of the target user, and calculating to obtain an average value of the electricity consumption data according to the electricity consumption data of each unit in the industry of the target user.
The power consumption data of each unit in the industry of the target user comprises the power consumption data of the target user and the power consumption data of other units except the target user. For example, the target user is an enterprise a, and the industry in which the enterprise a is located is the steel manufacturing industry. In this step, the electricity consumption data of each iron and steel manufacturing enterprise in the iron and steel manufacturing industry is acquired.
The average value of the power consumption data reflects the overall power consumption condition of the industry where the target enterprise is located.
S130, comparing and analyzing the electricity consumption data of the target user with the average value of the electricity consumption data.
The electricity consumption data may include one or more parameters, for example, the electricity consumption data includes an a parameter, and in this step, the a parameter of the target user is compared with an average value of a parameters of industries where the target user is located, so as to facilitate knowing electricity consumption conditions of the target enterprise therefrom, and further propose an improved electricity consumption scheme based on the target user.
Illustratively, the industry in which the target users are located is the steel production industry. In addition to the target use, the whole steel production industry includes a number of other steel production enterprises. The electricity usage data for the target user at 2019, month 01, may include a number of parameters, such as the electricity usage by the target user at 2019, month 01, for example. The electricity consumption of the target user in 2019 and 01 is 1216.3MW & h, and the average value of the electricity consumption of the steel production industry in 2019 and 01 is 279.3MW & h. The average value of the electricity consumption of the target user in 2019 and 01 is higher than that of the electricity consumption of the target user in the steel production industry, namely the electricity consumption of the target user in the steel production industry is higher, and the electricity consumption is a weakness of the target user in 2019 and 01. It will be understood that the electricity consumption of the target user for one month is taken as an example, but the electricity consumption is not limited to this, and the electricity consumption of the target user for a plurality of months may be analyzed. When the electricity consumption of the target user in a plurality of months exceeds the average value of the electricity consumption of the steel production industry of the target user, the electricity consumption is higher, and the electricity consumption is a weakness of the target user in a plurality of months. As can be seen from benchmarking analysis, the target user may have problems with power usage exceeding industry averages, based on which the target user may reduce his power usage, particularly depending on the target user's needs.
The embodiment of the invention provides an analysis method for electricity consumption standard mining, which is used for acquiring electricity consumption data of a target user, acquiring electricity consumption data of each unit in the industry of the target user, calculating to obtain an average value of the electricity consumption data according to the electricity consumption data of each unit in the industry of the target user, and comparing and analyzing the electricity consumption data of the target user with the average value of the electricity consumption data. The embodiment of the invention improves the electricity utilization operation quality and management level of the target user and enhances the competitiveness of the equipment maintenance service of the target user.
The embodiment of the invention continuously updates an informationized calibration analysis framework and quantitatively analyzes specific indexes by comparing and evaluating the capability of the whole and comprehensive calibration enterprises with the capability of the same industry at regular intervals, strengthens the selection of calibration objects, provides qualitative and quantitative calibration methods, develops key index qualitative analysis and key index quantitative analysis, mainly completes calibration analysis of electricity consumption trend, comprehensive electricity price, peak electricity proportion, load characteristic and electric energy quality, finds out enterprise weaknesses, improves operation quality and management level, and enhances the competitiveness of the maintenance service of enterprise client equipment. It can be understood that the electricity consumption trend, the comprehensive electricity price, the peak electricity proportion, the load characteristic and the electric energy quality are all reference indexes, namely parameters, in the analysis process of the target mining. For example, the electricity consumption trend of the steel production industry where the target user and the target user are located can be compared and analyzed, and the comprehensive electricity price of the steel production industry where the target user and the target user are located can be compared and analyzed.
Fig. 2 is a graph for analyzing the trend of the power consumption of the target users in a same ratio, and referring to fig. 2, the trend of the power consumption of the target users in 2019 and 2020 is drawn, so that the situation of the same ratio increase of the power consumption of the target users is analyzed.
Optionally, the electricity usage data includes electricity usage for a plurality of months. Fig. 3 is a graph of electricity usage trend versus standard analysis, referring to fig. 3, wherein "industry" refers to the industry in which the target users are located. And comparing and analyzing the average value of the electricity consumption of the target user and the industry electricity consumption of the target user by drawing the average value of the electricity consumption of the target user and the industry electricity consumption of the target user.
Optionally, the analysis method of the electric pair mark mining further comprises the following steps: the ranking of the unit GDP annual energy consumption of the target user in the industry where the target user is located is obtained. The unit GDP annual energy consumption y of the target user satisfies the following conditions:where n=12, x is the unit GDP month energy consumption of the target user.
Fig. 4 is a graph of trend analysis of target users on the same-ratio comprehensive unit price, wherein the comprehensive unit price is calculated by the ratio of total electricity charge to total electricity consumption, and the economic efficiency of enterprise electricity consumption is comprehensively reflected. That is, the smaller the value of the integrated unit price=the total electricity charge/the total electricity consumption, the more economical the electricity consumption, and the larger the value of the integrated unit price, the more expensive the electricity consumption. Referring to fig. 4, the same-rate increase condition of the target user's comprehensive unit price is analyzed by plotting comprehensive unit price trend charts of the target users in 2018, 2019 and 2020.
Optionally, the electricity consumption data includes a comprehensive unit price of a plurality of months, wherein the comprehensive unit price is a ratio of a total electricity fee to a total electricity consumption. Fig. 5 is a graph of comprehensive unit price versus standard analysis, and referring to fig. 5, the comprehensive unit price of the target user is compared with the average value of the comprehensive unit price of the industry where the target user is located by plotting the average value of the comprehensive unit price of the target user and the comprehensive unit price of the industry where the target user is located.
Fig. 6 is a graph showing a trend analysis of a peak Gu Chazhi of a target user, and a peak Gu Chazhi is a difference between a peak electricity consumption amount and a valley electricity consumption amount, i.e., the peak electricity consumption amount is subtracted from the valley electricity consumption amount. In other embodiments, the peak period power consumption minus the valley period power consumption may also be used as the peak-to-valley difference value. The electricity consumption in the valley period refers to the electricity consumption in the night, and the electricity consumption in the peak period refers to the electricity consumption in the daytime. Referring to fig. 6, the peak Gu Chazhi of the target user is analyzed for the homonymous growth by plotting the peak-to-valley difference trend for the target user in 2019 and 2020.
Optionally, the electricity consumption data includes a peak electricity ratio of a plurality of months, wherein the peak electricity ratio is a ratio of a peak period electricity consumption to a total electricity consumption. Fig. 7 is a peak electricity ratio comparison analysis chart, and referring to fig. 7, the peak electricity ratio of the target user is compared with the average value of the peak electricity ratio of the industry where the target user is located by drawing the average value of the peak electricity ratio of the target user and the peak electricity ratio of the industry where the target user is located. Further, fig. 7 also illustrates a "peak power" of the target user, that is, peak power consumption of the target user in a plurality of months.
Optionally, the electricity usage data includes a load rate for a plurality of months. Wherein, the load factor is the ratio of the average load to the highest load. Load factor = average load/highest load, the larger the data reflects the better load balancing, the more smooth the load variation. Fig. 8 is an annual load rate versus target analysis chart, fig. 9 is a weekend load rate versus target analysis chart, fig. 10 is a working day load rate versus target analysis chart, fig. 11 is a spring festival load rate versus target analysis chart, and reference is made to fig. 9-11, wherein the "maximum load" is the highest load. In fig. 9, "2019.11 wednesday" refers to one wednesday of 11 months of 2019, and so on, "2019.11 sunday" refers to one sunday of 11 months of 2019, and will not be described in detail herein. The load factor condition of the whole target user is analyzed by drawing a target user annual load factor curve and a typical daily load factor curve (workday, weekend, spring festival holiday and the like).
Optionally, the electricity consumption data includes a load rate of a plurality of months, the load rate being a ratio of an average load to a rated capacity. The larger the value of the load factor reflects the more fully utilized the device. The load rate can effectively reflect the utilization level of the equipment, the heavy overload or light load condition is identified through the change analysis of the load rate level per month, and the load distribution rule is further analyzed to provide a reference for pertinently improving the load level. Fig. 12 is a graph of load factor vs. standard analysis, referring to fig. 12, in which "maximum load factor" is the load factor of the target user. The "average load factor" is the average of the load factors at which the target user is located. And comparing and analyzing the average value of the load rate of the target user and the industry load rate of the target user by drawing the average value of the load rate of the target user and the industry load rate of the target user.
Fig. 13 is a trend analysis chart of the power factor of the target user, and referring to fig. 13, the larger the data of the power factor is, the better the reactive power control is reflected, and the higher the electricity quality is. Through power factor analysis, a target user can conveniently grasp the power factor condition in time, and when the power factor is lower than an assessment standard, the production mode can be changed in time or reactive compensation equipment of an enterprise can be checked and analyzed, so that the maximum power factor is improved, fine adjustment is avoided, and the electricity consumption of the target user is reduced. As shown in fig. 13, the evaluation criterion is exemplified by 0.9, but not limited thereto, and in other embodiments, the evaluation criterion may have other values, for example, 0.95.
Illustratively, the power factor penalty specification may be: every lower than standard 0.01, from 0.5% of the electric charge sum fine, increase progressively, lower than 0.7 each grade raise to 1%, lower than 0.65 each grade raise to 2%; every time the total prize is higher than the standard 0.01, the total prize of the electric charge is 0.15%, and the total prize is pushed by the total prize, and the total prize is capped by 0.75%. Taking an examination standard of 0.9 as an example, when the power factor reaches more than 0.95, the rewarding proportion reaches 0.75% capping. And comparing the power factor of each month with the assessment standard, so that the power consumption of an enterprise (namely, a target user) is further optimized.
Optionally, the power usage data includes a force modulation factor. Wherein, the power adjustment coefficient is M1, the power adjustment electric charge is M2, the basic electric charge is M3, the electricity consumption electric charge is M4, satisfy: m1=m2/(m3+m4). That is, the smaller the force adjustment coefficient=force adjustment electricity rate/(basic electricity rate+electricity rate), the better the reactive control, the higher the electricity quality, the negative number indicates that the reactive control is good, the electricity rate rewards, the positive number indicates that the reactive control does not reach the standard, and the electricity rate penalties. Fig. 14 is a graph of force adjustment coefficient versus standard analysis, and referring to fig. 14, the force adjustment coefficient of the target user is compared with the average value of the industry force adjustment coefficient of the target user by plotting the average value of the force adjustment coefficient of the target user and the industry force adjustment coefficient of the target user.
Example two
Fig. 15 is a schematic diagram of an analysis device for electric power consumption versus standard mining according to a second embodiment of the present invention, and referring to fig. 15, the analysis device for electric power consumption versus standard mining includes an electric power consumption data obtaining module 10, an electric power consumption average value obtaining module 20, and a comparison analysis module 30. The electricity consumption data acquisition module 10 is configured to acquire electricity consumption data of a target user. The electricity consumption average value obtaining module 20 is configured to obtain electricity consumption data of each unit in the industry where the target user is located, and calculate an average value of the electricity consumption data according to the electricity consumption data of each unit in the industry where the target user is located. The comparison and analysis module 30 is configured to compare and analyze the power consumption data of the target user with the average value of the power consumption data.
The embodiment of the invention provides an analysis device for electricity consumption opposite-sign mining, wherein an electricity consumption data acquisition module 10 acquires electricity consumption data of a target user, an electricity consumption average value acquisition module 20 acquires electricity consumption data of each unit in the industry of the target user, an electricity consumption data average value is calculated according to the electricity consumption data of each unit in the industry of the target user, and a comparison analysis module 30 compares and analyzes the electricity consumption data of the target user with the electricity consumption data average value. The embodiment of the invention improves the electricity utilization operation quality and management level of the target user and enhances the competitiveness of the equipment maintenance service of the target user.
Example III
Fig. 16 is a schematic structural diagram of a computer device according to a third embodiment of the present invention, and referring to fig. 16, a computer device 60 includes a memory 602, a processor 601, and a computer program stored in the memory 602 and capable of running on the processor, where the processor 601 executes the program to implement the method in the above embodiment. FIG. 16 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present invention. The computer device 60 shown in fig. 16 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention. As shown in fig. 16, the computer device 60 is in the form of a general purpose computing device. The components of the computer device 60 may include, but are not limited to: one or more processors 601, a system memory 602, and a bus 603 that connects the different system components (including the system memory 602 and the processor 601).
Bus 603 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 60 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 60 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 602 may include computer system readable media in the form of volatile memory such as Random Access Memory (RAM) 604 and/or cache memory 605. The computer device 60 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 606 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 16, commonly referred to as a "hard disk drive"). Although not shown in fig. 16, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 603 through one or more data medium interfaces. The system memory 602 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the invention.
A program/utility 608 having a set (at least one) of program modules 607 may be stored in, for example, system memory 602, such program modules 607 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 607 generally perform the functions and/or methods of the described embodiments of the invention.
The computer device 60 may also communicate with one or more external devices 609 (e.g., keyboard, pointing device, display 610, etc.), one or more devices that enable a user to interact with the device, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 60 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 611. Moreover, the computer device 60 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 612. As shown in fig. 16, the network adapter 612 communicates with other modules of the computer device 60 over the bus 603. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with computer device 60, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processor 601 executes various functional applications and data processing by running programs stored in the system memory 602.
Example IV
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, can implement the method described in the above embodiment.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, and that various obvious changes, rearrangements, combinations, and substitutions can be made by those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (6)
1. An analysis method of electric pair mark mining, which is characterized by comprising the following steps:
acquiring electricity consumption data of a target user;
acquiring electricity consumption data of each unit in the industry of the target user, and calculating to obtain an average value of the electricity consumption data according to the electricity consumption data of each unit in the industry of the target user;
comparing and analyzing the electricity consumption data of the target user with the average value of the electricity consumption data;
the electricity consumption data comprises comprehensive unit prices of a plurality of months; wherein the comprehensive unit price is the ratio of the total electricity charge to the total electricity consumption; the electricity consumption data comprise peak electricity proportions of a plurality of months; wherein the peak electricity proportion is the ratio of the peak period electricity consumption to the total electricity consumption; the electricity consumption data comprises a plurality of month load rates; wherein the load factor is the ratio of the average load to the rated capacity; the electricity consumption data comprise force adjustment coefficients; wherein, the power adjustment coefficient is M1, the power adjustment electric charge is M2, the basic electric charge is M3, the electricity consumption electric charge is M4, satisfy: m1=m2/(m3+m4).
2. The method of claim 1, wherein the power usage data comprises a plurality of months of power usage.
3. The method as recited in claim 2, further comprising: the ranking of the unit GDP annual energy consumption of the target user in the industry where the target user is located is obtained.
4. An apparatus for analyzing the electrical signature, comprising:
the power consumption data acquisition module is used for acquiring power consumption data of a target user;
the power consumption average value acquisition module is used for acquiring power consumption data of each unit in the industry of the target user, and calculating to obtain a power consumption data average value according to the power consumption data of each unit in the industry of the target user;
the comparison analysis module is used for comparing and analyzing the electricity consumption data of the target user with the average value of the electricity consumption data;
the electricity consumption data comprises comprehensive unit prices of a plurality of months; wherein the comprehensive unit price is the ratio of the total electricity charge to the total electricity consumption; the electricity consumption data comprise peak electricity proportions of a plurality of months; wherein the peak electricity proportion is the ratio of the peak period electricity consumption to the total electricity consumption; the electricity consumption data comprises a plurality of month load rates; wherein the load factor is the ratio of the average load to the rated capacity; the electricity consumption data comprise force adjustment coefficients; wherein, the power adjustment coefficient is M1, the power adjustment electric charge is M2, the basic electric charge is M3, the electricity consumption electric charge is M4, satisfy: m1=m2/(m3+m4).
5. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-3 when the program is executed by the processor.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-3.
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