CN116523687A - Multi-factor electricity consumption growth driving force decomposition method, device and storage medium - Google Patents
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Abstract
Embodiments of the present specification provide a power consumption growth driving force decomposition method, apparatus, and storage medium considering multiple factors, the method including: determining a first electricity increment, a first driving force parameter and a second driving force parameter according to pre-collected historical electricity demand data, wherein the first driving force parameter is a parameter directly represented by electric quantity, and the second driving force parameter is a parameter directly represented by electric quantity and cannot be represented by electric quantity; obtaining a second electricity consumption increase according to the historical electricity demand data, the first electricity increase and the first driving force parameter; and determining the electric quantity corresponding to the second driving force parameter according to the second electricity consumption increment and the second driving force parameter. The technical scheme provided by the application is used for solving the problem that the electric power demand prediction method in the prior art is not suitable for diversified price mechanisms.
Description
Technical Field
The present document relates to the field of power engineering technologies, and in particular, to a method, an apparatus, and a storage medium for decomposing a power consumption growth driving force in consideration of multiple factors.
Background
The diversified price mechanism increases the difficulty of power demand prediction.
In the prior art, qualitative prediction is generally performed on the power demand according to a preset model.
However, the preset models are all classical models and are not suitable for diversified price mechanisms, and the parameters in the models are mostly theoretical values for the power demand, so that the accuracy of power demand prediction is reduced.
Disclosure of Invention
In view of the above analysis, the present application aims to propose a power consumption growth driving force decomposition method, a device and a storage medium which consider multiple factors, determine power consumption growth driving force through the decomposition method based on data of power demand, and quantitatively analyze contribution of the power consumption growth driving force to power demand increment so as to optimize a classical model, thereby improving accuracy of power demand prediction.
In a first aspect, one or more embodiments of the present specification provide a power growth driving force decomposition method considering multiple factors, including:
determining a first electricity increment, a first driving force parameter and a second driving force parameter according to pre-collected historical electricity demand data, wherein the first driving force parameter is a parameter directly represented by electric quantity, and the second driving force parameter is a parameter directly represented by electric quantity and cannot be represented by electric quantity;
obtaining a second electricity consumption increase according to the historical electricity demand data, the first electricity increase and the first driving force parameter;
and determining the electric quantity corresponding to the second driving force parameter according to the second electricity consumption increment and the second driving force parameter.
Further, the determining the first electricity growth amount, the first driving force parameter and the second driving force parameter according to the pre-collected historical electricity demand data comprises:
determining the first electrical growth and driving force parameters from the historical electrical demand data based on a text analysis and expert system;
determining a target parameter from the driving force parameters based on a correlation analysis;
and classifying the target parameters to obtain the first driving force parameters and the second driving force parameters.
Further, the obtaining a second electricity consumption increase amount according to the historical electricity demand data, the first electricity increase amount, and the first driving force parameter includes:
determining an electric quantity corresponding to the first driving force parameter according to the historical power demand data;
and deducting the electric quantity corresponding to the first driving force parameter from the first electricity increment to obtain a second electricity increment.
Further, the determining, according to the second electricity consumption increase amount and the second driving force parameter, an electric quantity corresponding to the second driving force parameter includes:
constructing a multiple regression equation according to the second electricity consumption increment and the second driving force parameter;
and determining the electric quantity corresponding to the second driving force parameter according to the multiple regression equation.
Further, the method further comprises:
determining an electric quantity corresponding to the first driving force parameter according to the historical power demand data;
and verifying the second electricity consumption increment according to the electric quantity corresponding to the first driving force parameter and the first electricity increment.
In a second aspect, embodiments of the present application provide a power consumption growth driving force decomposition device considering multiple factors, including: the device comprises a parameter determining module, a data processing module and an electric quantity determining module;
the parameter determining module is used for determining a first electricity increment, a first driving force parameter and a second driving force parameter according to the pre-collected historical electricity demand data, wherein the first driving force parameter is a parameter directly represented by electric quantity, and the second driving force parameter is a parameter directly represented by electric quantity and cannot be represented by electric quantity;
the data processing module is used for obtaining a second electricity consumption increment according to the historical electricity demand data, the first electricity increment and the first driving force parameter;
the electric quantity determining module is used for determining the electric quantity corresponding to the second driving force parameter according to the second electricity consumption increment and the second driving force parameter.
Further, the parameter determination module is configured to determine the first electric growth amount and the driving force parameter from the historical electric power demand data based on a text analysis and an expert system; determining a target parameter from the driving force parameters based on a correlation analysis; and classifying the target parameters to obtain the first driving force parameters and the second driving force parameters.
Further, the data processing module is used for constructing a multiple regression equation according to the second electricity consumption increment and the second driving force parameter; and determining the electric quantity corresponding to the second driving force parameter according to the multiple regression equation.
Further, the electric quantity determining module is used for constructing a multiple regression equation according to the second electricity consumption increment and the second driving force parameter; and determining the electric quantity corresponding to the second driving force parameter according to the multiple regression equation.
In a third aspect, embodiments of the present application provide a storage medium, including:
for storing computer-executable instructions which, when executed, implement the method of any of the first aspects.
Compared with the prior art, the application can at least realize the following technical effects:
according to the method, parameters affecting electricity demand are divided into two types according to different characterization forms of the parameters, one is the parameter (first driving force parameter) directly represented by electricity, for example, the influence of temperature on electricity demand can be calculated based on the power company daily degree scheduling electricity generation amount. Another is that parameters (second driving force parameters) which cannot be represented by the electric quantity, such as the influence of the regional production total value on the electric quantity demand, cannot be expressed by using the specific electric quantity. And then subtracting the electric quantity corresponding to the first driving force parameter from the electricity consumption increment to obtain the electric quantity of the second driving force parameter so as to establish the relation between the second driving force parameter and the electric quantity. Therefore, an analyst can clearly know the contribution of the second driving force parameter to the electricity demand increment so as to carry out targeted modification on the electricity demand prediction model, and the accuracy of electricity demand prediction is improved.
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For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description that follow are only some of the embodiments described in the description, from which, for a person skilled in the art, other drawings can be obtained without inventive faculty.
FIG. 1 is a flow diagram of a method for factoring power growth driving force in consideration of multiple factors provided in one or more embodiments of the present disclosure.
Detailed Description
In order to enable a person skilled in the art to better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the present disclosure.
Many factors influence the electricity demand, such as temperature, economic development, electricity price, etc. Among these factors, the extent of influence of a part of the factors (first driving force parameters) on the electricity demand may be represented by electricity consumption, for example, the electricity consumption in summer may be calculated based on the power company's daily schedule power generation amount, and the larger the electricity consumption in summer, the larger the electricity consumption in summer is, which means the larger the electricity demand in summer. Thus, the extent to which temperature affects the electricity demand can be characterized by the amount of electricity. But another part of the factors (the second driving force parameter) cannot characterize the extent of the influence of electricity demand, for example, the influence of economic quantity on electricity demand.
Because the influence of the second driving force parameter on the electricity consumption requirement cannot be represented by the electricity consumption, when the detection personnel optimizes the electricity demand prediction model based on the second driving force parameter, the accuracy of the electricity demand prediction is reduced.
Aiming at the scene and the technical problems, the embodiment of the application provides a power consumption growth driving force decomposition method considering multiple factors, which comprises the following steps:
and step 1, determining a first electricity increment, a first driving force parameter and a second driving force parameter according to the pre-collected historical electricity demand data.
In this embodiment of the present application, the first driving force parameter is a parameter directly represented by electric quantity, and the second driving force parameter is a parameter directly represented by electric quantity, which cannot be represented by electric quantity. The first driving force parameter includes: power conservation, power substitution, digitizing infrastructure, and temperature. The historical power demand data includes: reports of electricity consumption of each industry in the past year, social responsibility reports of the past year electric company, large data centers and 5G base station electricity consumption data issued by past year authorities, daily schedule electricity generation quantity of the past year electric company, and historical year data of regional production total value, industrial increment value occupation ratio and town ratio.
The parameter value of the electric energy saving is the electricity saving quantity generated by controlling the electricity consumption in order to reduce the cost in various industries. For example, in order to control the cost, the enterprise a uses m degrees of electricity less than the last year by controlling the electricity consumption in the current year, and m is the electricity-saving amount, that is, the corresponding parameter value of the electricity saving. The corresponding parameter value of the electric energy substitution is the electric energy substitution electric quantity generated by using the electric energy to substitute the energy source of the scattered burning coal and the fuel oil. The corresponding parameter value of the digital infrastructure is the electricity consumption generated when the digital infrastructure works. The parameter value corresponding to the temperature is the electricity consumption of the users in each season.
The second driving force parameter includes: economic total amount, industrial structure and town. The corresponding parameter value of the economic total amount is the regional production total value, the corresponding parameter value of the industrial structure is the industrial increment value ratio, and the corresponding parameter value of the town is the town ratio.
It should be noted that, the term "characterizing the electric quantity" means that the electric quantity is directly used as a parameter value of the first driving force parameter, and the larger the parameter value, the larger the influence of the first driving force parameter on the electric quantity demand is described. By "unable to characterize with the electrical quantity" it is meant that the electrical quantity cannot be directly used as a parameter value for the second driving parameter.
In an embodiment of the present application, a first electrical growth amount and a driving force parameter are determined from historical electrical demand data based on a text analysis and expert system. Based on the correlation analysis, a target parameter is determined from the driving force parameters. And classifying the target parameters to obtain a first driving force parameter and a second driving force parameter.
Specifically, relevant documents are studied by referring to the power consumption influence factors, and influence factors with higher word frequency are screened to serve as driving force parameters. And simultaneously, according to the advice given by the expert system, selecting corresponding factors as driving force parameters. And simultaneously, based on text analysis, acquiring a first electricity increment from the report of the corresponding department. Wherein the first electricity increment represents an increment of the total electricity demand of a certain area. And then, performing correlation analysis on each driving force parameter and the power consumption by adopting a correlation analysis method, and selecting the driving force parameter with the correlation exceeding a threshold value as a target parameter. And finally, classifying the target parameters to obtain a first driving force parameter and a second driving force parameter.
And step 2, obtaining a second electricity consumption increment according to the historical electricity demand data, the first electricity increment and the first driving force parameter.
In this embodiment of the present application, since the first driving force parameter may be represented by a power amount, the power amount corresponding to the first driving force parameter is determined according to the historical power demand data. And deducting the electric quantity corresponding to the first driving force parameter from the first electric quantity to obtain a second electric quantity. Wherein the second electricity consumption increase amount is a total electricity consumption increase amount of the second driving force parameter. By the mode, the first driving force parameter is represented by the initial utilization of electric quantity.
And step 3, determining the electric quantity corresponding to the second driving force parameter according to the second electricity consumption increment and the second driving force parameter.
In the embodiment of the application, a multiple regression equation is constructed according to the second electricity consumption increment and the second driving force parameter. And determining the electric quantity corresponding to the second driving force parameter according to a multiple regression equation.
Specifically, historical year data of regional production total values, industrial increment value duty ratios and town ratio are collected. And constructing a multiple regression equation based on historical year data of the regional production total value, the industrial increment value duty ratio and the town ratio. And (3) bringing the second electricity consumption increment, the regional production total value, the industrial increment value ratio and the urbanization rate into a multiple regression equation, and respectively calculating coefficients corresponding to the regional production total value, the industrial increment value ratio and the urbanization rate. And finally, multiplying the regional production total value, the industrial increment value duty ratio and the town ratio by corresponding coefficients respectively to obtain the electric quantity corresponding to the second driving force parameter.
In the embodiment of the application, in order to further improve the prediction accuracy of the power consumption requirement, the electric quantity corresponding to the first driving force parameter is determined according to the historical power requirement data. And verifying the second electricity consumption increment according to the electric quantity corresponding to the first driving force parameter and the first electricity increment.
Specifically, the electric quantity corresponding to the first driving force parameter is utilized to calculate the electric quantity increase rate corresponding to the first driving force parameter. And calculating the electric quantity increase rate corresponding to the second driving force parameter by using the second electric quantity increase amount. The total power increase rate is calculated using the first power increase amount. Finally, it is verified whether the electric quantity increase rate corresponding to the first driving force parameter and the electric quantity increase rate corresponding to the second driving force parameter match the total electric quantity increase rate. If so, the electric quantity corresponding to the target parameter and the second driving force parameter is correct, and the electric quantity can be used for optimizing the prediction model. Otherwise, it is indicated that there is a problem with the electric quantity corresponding to the target parameter or the second driving force parameter, and further improvement is required, such as replacement of the target parameter or modification of the multiple regression equation.
In order to illustrate the feasibility of the technical scheme, the application provides the following examples:
s1: and the method of correlation analysis, text analysis and the like is adopted, and expert experience judgment is combined to screen out factors with larger influence on electricity utilization growth, including economic total amount, industrial structure, town, electricity saving, electric energy substitution, digitalization, climate and air temperature and the like.
S2: and removing the historical electricity-saving quantity, the electricity-replacing quantity, the digital infrastructure quantity and the cooling and heating quantity based on the historical annual electricity consumption, so as to obtain the economic and social influence quantity.
Specifically, the historical electricity-saving quantity, the electric energy replacing quantity, the digital infrastructure quantity and the cooling and heating quantity respectively correspond to electricity saving, electric energy replacing, digitalization and climate and temperature.
The method for calculating the historical electricity-saving quantity comprises the following steps: according to the bottom-up analysis architecture, starting from important industry products such as black, colored, chemical, building materials, electric power, transportation, construction, agriculture and the like, the electricity-saving quantity of each industry is calculated based on the change condition of unit consumption of the products and the change condition of the product scale in the last year, and then the total electricity-saving quantity is obtained. The history is accumulated to the total power saving amount in the current year as the power saving amount in the current year.
The method for calculating the electric energy substitution electric quantity comprises the following steps: and obtaining annual electric energy replacing electric quantity data according to annual social responsibility reports issued by domestic electric power companies. The history is accumulated to the total electric energy replacing electric quantity in the current year as the electric energy replacing electric quantity in the current year.
The method for calculating the electric quantity of the digital infrastructure comprises the following steps: and adding the large data center issued by authorities such as the industrial information department and the 5G base station electricity consumption data to obtain the digital infrastructure electricity of the current year.
The method for calculating the cooling and heating power consumption comprises the following steps: the power generation quantity is scheduled based on the power company daily degree, the typical day in spring and autumn is set to be free of cooling and heating power consumption, and the basic power consumption is linearly increased along with time; the actual electricity consumption in summer and winter is subtracted from the basic electricity consumption, and the cooling and heating electricity consumption in the current year is obtained.
S3: and decomposing the economic and social influence electric quantity into economic total influence electric quantity, industrial structure adjustment influence electric quantity and town influence electric quantity by adopting a multiple regression analysis method.
Specifically, the method is used for respectively standardizing the economic and social influence electric quantity, the regional production total value, the industrial increment value ratio and the town ratio. Then, a multiple regression equation is constructed by using the normalized data, as shown in Table 1:
TABLE 1 multiple regression equation
And finally, multiplying the total regional production value, the industrial increment value ratio and the urbanization rate by corresponding coefficients to obtain the economic total quantity influence electric quantity, the industrial structure adjustment influence electric quantity and the urbanization influence electric quantity.
S4: and respectively determining the electricity utilization increase rate corresponding to the economic total amount, the industrial structure, the town, the electricity saving, the electric energy substitution, the digitization, the cooling and heating.
Specifically, the difference between the current-year economic total quantity influence electric quantity and the last-year economic total quantity influence electric quantity is divided by the last-year electric quantity to obtain the electric utilization increase rate corresponding to the economic total quantity influence electric quantity.
Dividing the difference between the current year of the electric quantity influenced by the industrial structure and the last year of the electric quantity influenced by the industrial structure by the last year of the electric quantity, and obtaining the electricity utilization increase rate corresponding to the electric quantity influenced by the industrial structure.
Dividing the difference between the current-year urban electricity quantity and the last-year urban electricity quantity by the last-year electricity quantity to obtain the electricity utilization increase rate corresponding to the urban electricity quantity.
Dividing the difference between the current-year electricity-saving quantity and the last-year electricity-saving quantity by the electricity consumption of the last year to obtain the electricity utilization increase rate corresponding to the electricity-saving quantity.
Dividing the difference between the current-year electric energy replacement electric quantity and the last-year electric energy replacement electric quantity by the last-year electric quantity to obtain the electric energy growth rate corresponding to the electric energy replacement electric quantity.
Dividing the difference between the digitally affected electric quantity in the current year and the digitally affected electric quantity in the last year by the electric quantity used in the last year to obtain the electric utilization increase rate corresponding to the digitally affected electric quantity.
Dividing the difference between the current year cooling and heating electric quantity and the last year cooling and heating electric quantity by the last year electric quantity to obtain the corresponding electricity utilization increase rate of the cooling and heating electric quantity.
Through calculation, the electricity utilization increase rates of economic growth, structure adjustment, town, energy saving and electricity saving, electrification, digitalization and climate and temperature are respectively 7.2-0.7, 0.6-3.7, 2.1, 0.3 and 0.5 percent.
S5: and verifying whether the obtained target parameters and regression equations are correct or not by using the electricity utilization growth rate of economic growth, structure adjustment, town, energy saving and electricity saving, electrification, digitalization and climate and air temperature.
Specifically, the average electricity quantity increase rate of the reference year is set to be 5.7%, the sum of electricity consumption increase rates of economic increase, structure adjustment, town, energy and electricity saving, electrification, digitalization and climate and temperature is set to be 6.1%, and the error is set to be 0.4% within a preset range. Thus, the target parameters and regression equations obtained above are correct. The inspector can optimize the electricity consumption requirement model according to the target parameters and the regression equation.
The embodiment of the application also provides a power consumption growth driving force decomposition device considering multiple factors, which comprises: the device comprises a parameter determining module, a data processing module and an electric quantity determining module;
the parameter determining module is used for determining a first electricity increment, a first driving force parameter and a second driving force parameter according to the pre-collected historical electricity demand data, wherein the first driving force parameter is a parameter directly represented by electric quantity, and the second driving force parameter is a parameter directly represented by electric quantity and cannot be represented by electric quantity;
the data processing module is used for obtaining a second electricity consumption increment according to the historical electricity demand data, the first electricity increment and the first driving force parameter;
the electric quantity determining module is used for determining the electric quantity corresponding to the second driving force parameter according to the second electricity consumption increment and the second driving force parameter.
In an embodiment of the present application, the parameter determination module is configured to determine the first electricity growth amount and the driving force parameter from the historical electricity demand data based on a text analysis and an expert system; determining a target parameter from the driving force parameters based on a correlation analysis; and classifying the target parameters to obtain the first driving force parameters and the second driving force parameters.
In this embodiment of the present application, the data processing module is configured to construct a multiple regression equation according to the second electricity consumption increase amount and the second driving force parameter; and determining the electric quantity corresponding to the second driving force parameter according to the multiple regression equation.
In this embodiment of the present application, the electric quantity determining module is configured to construct a multiple regression equation according to the second electricity consumption increase amount and the second driving force parameter; and determining the electric quantity corresponding to the second driving force parameter according to the multiple regression equation.
An embodiment of the present application provides a storage medium, including:
for storing computer executable instructions that when executed implement the method of any of the preceding embodiments.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In the 30 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each unit may be implemented in the same piece or pieces of software and/or hardware when implementing the embodiments of the present specification.
One skilled in the relevant art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
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 storage media for a computer 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, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
One or more embodiments of the present specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is by way of example only and is not intended to limit the present disclosure. Various modifications and changes may occur to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. that fall within the spirit and principles of the present document are intended to be included within the scope of the claims of the present document.
Claims (10)
1. A power consumption growth driving force decomposition method considering multiple factors, characterized by comprising:
determining a first electricity increment, a first driving force parameter and a second driving force parameter according to pre-collected historical electricity demand data, wherein the first driving force parameter is a parameter directly represented by electric quantity, and the second driving force parameter is a parameter directly represented by electric quantity and cannot be represented by electric quantity;
obtaining a second electricity consumption increase according to the historical electricity demand data, the first electricity increase and the first driving force parameter;
and determining the electric quantity corresponding to the second driving force parameter according to the second electricity consumption increment and the second driving force parameter.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the method for determining the first electricity increment, the first driving force parameter and the second driving force parameter according to the pre-collected historical electricity demand data comprises the following steps:
determining the first electrical growth and driving force parameters from the historical electrical demand data based on a text analysis and expert system;
determining a target parameter from the driving force parameters based on a correlation analysis;
and classifying the target parameters to obtain the first driving force parameters and the second driving force parameters.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the obtaining a second electricity consumption increase according to the historical electricity demand data, the first electricity increase and the first driving force parameter includes:
determining an electric quantity corresponding to the first driving force parameter according to the historical power demand data;
and deducting the electric quantity corresponding to the first driving force parameter from the first electricity increment to obtain a second electricity increment.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the determining, according to the second electricity consumption increment and the second driving force parameter, an electric quantity corresponding to the second driving force parameter includes:
constructing a multiple regression equation according to the second electricity consumption increment and the second driving force parameter;
and determining the electric quantity corresponding to the second driving force parameter according to the multiple regression equation.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the method further comprises the steps of:
determining an electric quantity corresponding to the first driving force parameter according to the historical power demand data;
and verifying the second electricity consumption increment according to the electric quantity corresponding to the first driving force parameter and the first electricity increment.
6. A power consumption growth driving force decomposition device considering multiple factors, characterized by comprising: the device comprises a parameter determining module, a data processing module and an electric quantity determining module;
the parameter determining module is used for determining a first electricity increment, a first driving force parameter and a second driving force parameter according to the pre-collected historical electricity demand data, wherein the first driving force parameter is a parameter directly represented by electric quantity, and the second driving force parameter is a parameter directly represented by electric quantity and cannot be represented by electric quantity;
the data processing module is used for obtaining a second electricity consumption increment according to the historical electricity demand data, the first electricity increment and the first driving force parameter;
the electric quantity determining module is used for determining the electric quantity corresponding to the second driving force parameter according to the second electricity consumption increment and the second driving force parameter.
7. The apparatus of claim 6, wherein the device comprises a plurality of sensors,
the parameter determination module is used for determining the first electricity increment and driving force parameters from the historical electricity demand data based on text analysis and an expert system; determining a target parameter from the driving force parameters based on a correlation analysis; and classifying the target parameters to obtain the first driving force parameters and the second driving force parameters.
8. The apparatus of claim 6, wherein the device comprises a plurality of sensors,
the data processing module is used for constructing a multiple regression equation according to the second electricity consumption increment and the second driving force parameter; and determining the electric quantity corresponding to the second driving force parameter according to the multiple regression equation.
9. The apparatus of claim 6, wherein the device comprises a plurality of sensors,
the electric quantity determining module is used for constructing a multiple regression equation according to the second electricity consumption increment and the second driving force parameter; and determining the electric quantity corresponding to the second driving force parameter according to the multiple regression equation.
10. A storage medium, comprising:
for storing computer-executable instructions which, when executed, implement the method of any of claims 1-5.
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