CN113052385A - Method, device, equipment and storage medium for predicting power consumption in steel industry - Google Patents
Method, device, equipment and storage medium for predicting power consumption in steel industry Download PDFInfo
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Abstract
The invention is suitable for the technical field of electric power systems, and provides a method, a device, equipment and a storage medium for predicting power consumption in the steel industry, wherein the method for predicting the power consumption in the steel industry comprises the following steps: acquiring a production activity value of the target area in each unit time interval within a preset time interval in the steel industry; the production activity value of the steel industry is obtained based on a plurality of dimension activity values of a target area in each unit time period in a preset time period, and the plurality of dimension activity values at least comprise an iron ore production activity value, a crude steel production activity value, an automobile production activity value and a real estate new start construction area activity value; respectively establishing a prediction model of the production activity value of the steel industry and the power consumption of the steel industry of the target area in each unit time period within a preset time period under each preset regression model by utilizing a plurality of preset regression models; and predicting the power consumption of the steel industry in the target area according to the prediction model with the maximum goodness of fit. The method and the device can improve the prediction accuracy of the power consumption in the iron industry.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a method, a device, equipment and a storage medium for predicting power consumption in the steel industry.
Background
The prediction of the power consumption is an important link of the optimized scheduling of the power system, and the accurate and timely prediction of the power consumption not only can provide auxiliary decision support for the construction progress of a power supply and a power grid, but also has important significance in the aspects of preparing an economical and reasonable power allocation plan, reducing the operation cost of the power grid and guaranteeing the production and domestic power consumption. The steel industry is a high-energy-consumption industry, the power consumption of the steel industry occupies a large proportion in the power consumption of the whole society, and the fluctuation of the power consumption of the steel industry can generate strong influence on the power demand, so that the prediction of the power consumption of the steel industry is extremely important.
Therefore, a method capable of improving the power consumption prediction accuracy of the steel industry is needed.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a device and a storage medium for predicting power consumption in the iron and steel industry, so as to solve the problem in the prior art that the accuracy of predicting power consumption in the iron and steel industry is low.
The first aspect of the embodiment of the invention provides a method for predicting power consumption in the steel industry, which comprises the following steps:
acquiring a production activity value of the target area in each unit time interval within a preset time interval in the steel industry; the production activity value of the steel industry is obtained based on a plurality of dimension activity values of a target area in each unit time period in a preset time period, and the plurality of dimension activity values at least comprise an iron ore production activity value, a crude steel production activity value, an automobile production activity value and a real estate new start construction area activity value;
respectively establishing a prediction model of the production activity value of the steel industry and the power consumption of the steel industry of the target area in each unit time period within a preset time period under each preset regression model by utilizing a plurality of preset regression models;
and acquiring the goodness of fit of each prediction model, and predicting the power consumption of the steel industry in the target area according to the prediction model with the maximum goodness of fit.
Optionally, the obtaining of the steel industry production activity value of the target area in each unit time interval within the preset time interval includes:
acquiring a plurality of dimensionality vitality values of a target area in each unit time period within a preset time period;
and generating a production activity value of the steel industry according to the weight values of the plurality of dimension activity values.
Optionally, the obtaining a plurality of dimension vitality values of the target area in each unit time interval within a preset time interval includes:
respectively taking a plurality of dimensional yields of a target area in a preset time period and a target unit time period as reference yields, and setting a dimensional activity value of each reference yield as a unit activity value; the target unit time interval is any one unit time interval in a preset time interval, and the multiple dimensional yields at least comprise iron ore yield, crude steel yield, automobile yield and real estate new construction area;
acquiring the ratio of each dimension yield of each unit time interval in a preset time interval to the corresponding reference yield of a target area;
and determining the product of the ratio corresponding to each dimension yield of each unit time interval of the target area in the preset time interval and the unit vitality value as the dimension vitality value of the corresponding dimension yield of the corresponding unit time interval of the target area in the preset time interval.
Optionally, predicting the power consumption of the steel industry in the target area according to the prediction model with the maximum goodness of fit, including:
acquiring a production activity value of a target area in a unit time period to be predicted in the steel industry;
and inputting the steel industry production activity value of the unit time period to be predicted into the prediction model with the maximum goodness of fit to obtain the steel industry power consumption of the target area in the unit time period to be predicted.
The second aspect of the embodiment of the invention provides a device for predicting the power consumption in the steel industry, which comprises:
the first acquisition module is used for acquiring the production activity value of the steel industry of each unit time interval in a preset time interval of a target area; the production activity value of the steel industry is obtained based on a plurality of dimension activity values of a target area in each unit time period in a preset time period, and the plurality of dimension activity values at least comprise an iron ore production activity value, a crude steel production activity value, an automobile production activity value and a real estate new start construction area activity value;
the construction module is used for respectively establishing a prediction model of the steel industry production activity value under each preset regression model and the steel industry power consumption of the target area in each unit time period in the preset time period by utilizing a plurality of preset regression models;
the second acquisition module is used for acquiring the goodness of fit of each prediction model;
and the prediction module is used for predicting the power consumption of the steel industry in the target area according to the prediction model with the maximum goodness of fit.
Optionally, the first obtaining module is further configured to:
acquiring a plurality of dimensionality vitality values of a target area in each unit time period within a preset time period;
and generating a production activity value of the steel industry according to the weight values of the plurality of dimension activity values.
Optionally, the first obtaining module is further configured to:
respectively taking a plurality of dimensional yields of a target area in a preset time period and a target unit time period as reference yields, and setting a dimensional activity value of each reference yield as a unit activity value; the target unit time interval is any one unit time interval in a preset time interval, and the multiple dimensional yields at least comprise iron ore yield, crude steel yield, automobile yield and real estate new construction area;
acquiring the ratio of each dimension yield of each unit time interval in a preset time interval to the corresponding reference yield of a target area;
and determining the product of the ratio corresponding to each dimension yield of each unit time interval of the target area in the preset time interval and the unit vitality value as the dimension vitality value of the corresponding dimension yield of the corresponding unit time interval of the target area in the preset time interval.
Optionally, the prediction module is further configured to:
acquiring a production activity value of a target area in a unit time period to be predicted in the steel industry;
and inputting the steel industry production activity value of the unit time period to be predicted into the prediction model with the maximum goodness of fit to obtain the steel industry power consumption of the target area in the unit time period to be predicted.
A third aspect of embodiments of the present invention provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method according to the first aspect when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, performs the steps of the method according to the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the embodiment of the invention, the steel industry production activity value of the target area in each unit time period in the preset time period can be obtained through the multiple dimensionality activity values of the target area in each unit time period in the preset time period. And then, by utilizing various preset regression models, respectively establishing a prediction model of the steel industry production activity value under each preset regression model and the steel industry power consumption of the target area in each unit time period in the preset time period. And finally, predicting the power consumption of the steel industry in the target area by using the prediction model with the maximum goodness of fit. Therefore, causal connection between the production activity value of the steel industry and the power consumption of the steel industry can be quantized into a prediction model, and the power consumption of the steel industry in the region can be predicted more accurately by using the prediction model, so that the prediction accuracy of the power consumption of the steel industry is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flow chart illustrating steps of a method for predicting power consumption in the steel industry according to an embodiment of the present invention;
FIG. 2 is a graph showing the variation of the activity of steel production according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of a power consumption prediction apparatus for steel industry according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
The embodiment of the invention provides a method, a device, equipment and a storage medium for predicting power consumption in the steel industry. The method for predicting the power consumption of the steel industry provided by the embodiment of the invention is introduced below.
The invention is switched in from the aspect of the development requirement of the steel industry per se, considers the development conditions of upstream and downstream industries of the steel industry in a certain area, respectively selects a plurality of indexes which influence the development prospect flourishing degree of the steel industry, and constructs the production activity of the steel industry in the area. And then, establishing a prediction model according to the relation between the production activity of the steel industry in the area and the power consumption of the steel industry, wherein the prediction module can be used for predicting the future power consumption of the steel industry in the area, and provides a new idea for predicting the power consumption of the steel industry.
The main execution body of the method for predicting the power consumption in the steel industry may be a device for predicting the power consumption in the steel industry, and the device for predicting the power consumption in the steel industry may be a terminal device having a processor and a memory, such as a notebook computer, a server, a personal computer, or the like, and the embodiment of the present invention is not particularly limited.
As shown in fig. 1, the method for predicting the power consumption in the steel industry according to the embodiment of the present invention may include the following steps:
and S110, acquiring the production activity value of the steel industry of the target area in each unit time interval in a preset time interval.
The production activity value of the steel industry is obtained based on a plurality of dimensionality activity values of a target region in each unit time period in a preset time period, and the dimensionality activity values at least comprise an iron ore production activity value, a crude steel production activity value, an automobile production activity value and a real estate new construction area activity value.
Specifically, the increase and the change of the power consumption of the steel industry are greatly influenced by the national policy, and the execution of the national policy is bound to be reflected to the upstream and downstream industries of the steel industry, so that the power consumption of the steel industry is influenced, and the industry power is closely connected with the industry production condition. Thus, for the same area, the production activity of the steel industry which can reflect the flourishing condition of the steel market in the area can be synthesized from the production conditions of upstream and downstream industries of the steel industry, such as the yield of iron ore, the yield of crude steel, the yield of automobiles, the new construction area of real estate and the like.
In some embodiments, the target area may be an area including a steel enterprise, and the range may be a country, province, or city, such as provincial areas of Hebei province, Liaoning province, or city-level areas of Tangshan, Handan, and Anshan. The preset period may be a period of time with a long time span, such as three years up to the present, two years up to the present, etc., and the unit period may be a month, a half month, etc.
Optionally, the steel industry production activity value may be obtained according to the weight conditions corresponding to the activity degrees of different dimensions, and accordingly, the specific processing in step S110 may be as follows: acquiring a plurality of dimensionality vitality values of a target area in each unit time period within a preset time period; and generating a production activity value of the steel industry according to the weight values of the plurality of dimension activity values.
In some embodiments, a plurality of dimensional yields of the target region in a preset time period in a target unit time period may be respectively used as reference yields, and a dimensional vitality value of each reference yield may be set as a unit vitality value; the target unit time interval is any one unit time interval in the preset time interval, and the multiple dimensional yields at least comprise iron ore yield, crude steel yield, automobile yield and real estate new construction area. And then, acquiring the ratio of each dimension yield of each unit time interval in the preset time interval of the target area to the corresponding reference yield. Finally, the product of the ratio corresponding to each dimensional yield of each unit time interval of the target area in the preset time interval and the unit vitality value can be determined as the dimensional vitality value of the corresponding dimensional yield of the corresponding unit time interval of the target area in the preset time interval.
For example, the iron ore production rate of a region in 2018 month 1 can be set as a reference production rate, and correspondingly, the iron ore production activity value of the region in 2018 month 1 is set as a unit activity value, such as 1000 points. Thus, the iron ore production activity values for the remaining unit periods of the area can be calculated using the following formula:
wherein, TiFor the ith monthly iron ore production, TbThe iron ore production was 2018, month 1, i.e., the base production.
And S120, respectively establishing a prediction model of the steel industry production activity value under each preset regression model and the steel industry power consumption of the target area in each unit time period within the preset time period by using the multiple preset regression models.
The production activity value of the steel industry can reflect the production activity of the steel industry, and the higher the production activity is, the higher the steel yield is, and the larger the corresponding power consumption of the steel industry is. Therefore, a causal link exists between the steel industry production activity value and the steel industry power consumption, and the causal link can be quantized into a prediction model.
In some embodiments, the predetermined regression model may be various, such as a linear model, an exponential model, a logarithmic model, a hyperbolic model, a power function model, and the like. The prediction models under different preset regression models can be respectively established by taking the production activity value of the steel industry as an independent variable and the power consumption of the steel industry as a dependent variable.
And step S130, acquiring the goodness of fit of each prediction model.
The goodness of fit of the prediction model can reflect the degree of fit between the predicted value and the observed value. The greater the goodness of fit, the higher the fitting degree is, the more the predicted value curve and the observed value curve are fitted, and the better the prediction effect is.
And S140, predicting the power consumption of the steel industry in the target area according to the prediction model with the maximum goodness of fit.
In some embodiments, after the goodness of fit for each prediction model is obtained, the prediction model with the greatest goodness of fit may be determined therefrom. Meanwhile, the steel industry production activity value of the target area in the unit time period to be predicted can be obtained, for example, the third month after the current time. And then, the steel industry production activity value of the unit time interval to be predicted can be input into the prediction model with the maximum goodness of fit, and then the prediction model can output corresponding steel industry power consumption, wherein the steel industry power consumption is the steel industry power consumption of the target area in the unit time interval to be predicted.
It should be noted that the steel industry production activity value of the unit time interval to be predicted can be obtained through information such as iron ore yield, crude steel yield, automobile yield, new real estate construction area and the like published or predicted in advance.
In order to better understand the method for predicting the power consumption of the steel industry provided by the embodiment of the invention, the area a is taken as an example, and the prediction process of the power consumption of the steel industry in the month next to the current date in the area a is introduced.
Firstly, obtaining the iron ore yield, the crude steel yield, the automobile yield and the real estate new start construction area of a region A in 2018, 1 month to 2020, 12 months, and calculating the steel industry production activity value of each month in 2018, 1 month to 2020, 12 months according to the iron and steel industry production activity value, wherein the measuring and calculating process comprises the following steps:
(1) establishing the value t of the activity of iron ore productioniThe yield of iron ore in 1 month in 2018 is set as a reference value, and the activity value is set as 1000 points. The monthly iron ore yield is taken as a target value, so that the annual and monthly activity value is calculated, and the specific formula is as follows:
wherein, TiFor the ith monthly iron ore production, TbThe reference value of the iron ore production in 2018 and 1 month.
(2) Establishing a value s of the production activity of the crude steeliThe yield of the rough steel in 2018 and 1 month is set as a reference value, and the activity value is set as 1000 points. And calculating the activity value of each year and month by taking the yield of the rough steel of each month as a target value, wherein the specific formula is as follows:
wherein S isiFor the i-th month yield of crude steel, SbThe yield of the crude steel is a reference value of the crude steel yield in 2018 and 1 month.
(3) Establishing a value of automotive production activity ciThe automobile yield in 2018 and 1 month is set as a reference value, and the vitality value is set as 1000 points. The monthly automobile yield is taken as a target value, so that the annual and monthly vitality value is calculated, and the specific formula is as follows:
wherein, CiFor the ith monthly car yield, CbThe standard value of the automobile output in 2018 and 1 month.
(4) Establishing a real estate new start construction area activity value eiSetting the new construction area of the real estate in 1 month in 2018 as a reference value, and setting the vitality value as 1000 points. The monthly new real estate construction area is taken as a target value, so that the activity value of each year and month is calculated, and the specific formula is as follows:
wherein E isiFor the i-th monthly property new construction area, EbThe standard value of the construction area for newly starting the real estate in 1 month in 2018.
(5) The production activity value of the synthetic steel industry has the following specific formula:
SPA＝α·ti+β·si+χ·ci+δ·ei
wherein, ti、si、ci、eiThe steel ore production activity value, the crude steel production activity value, the automobile production activity value and the real estate new construction area activity value in the ith month are respectively, and alpha, beta, chi and delta are respectively weighted values of the activity values.
As shown in table one below, specific data of the steel industry production viability values for region a between 2018 and 2020 and 12 months are shown.
Watch 1
As shown in fig. 2, the change curve of the production activity of the steel industry with time corresponding to the data in the table one is shown. From fig. 2, it can be found that the production activity of the steel industry in three years generally shows a trend of fluctuation and rise, the production peak season of the industry per year is concentrated in 6 months, the steel industry has obvious holiday characteristics, and 1 month and 2 months per year are production low valleys of the industry.
And then, monthly data of the power consumption of the steel industry in 2018-2020 in the area A can be obtained, and 11 preset regression models are adopted to respectively establish corresponding prediction models of the production activity x of the steel industry and the power consumption y of the steel industry.
The 11 preset regression models are as follows:
(1) linear model: y is a + b · x;
(2) index model 1: y is a.ebx；
(3) Index model 2: y is a.eb/x；
(4) Logarithmic model: y ═ a + b · ln (x);
(5) hyperbolic model 1: y is a + b/x;
(6) a hyperbolic model 2:1/Y ═ a + b/X;
(7) power function model: y is a.xb；
(8) An S-shaped curve model:
(9) gompertz curve: lnY ═ a + b · e-x;
(10) a parabolic model: y ═ a + bx + cx2；
(11) Curve model for n times: y ═ a0+ a1X + a2X2+ … + anXn;
and a and b in each model are model parameters to be determined by the regression model.
As shown in table two below, the goodness of fit R2 of the prediction model under each preset regression model is shown.
Watch two
| Model (model) | Goodness of fit R2 |
| Linear model | 0.4540 |
| Exponential model 1 | 0.4434 |
| Exponential model 2 | 0.4213 |
| Logarithmic model | 0.4809 |
| Hyperbolic model 1 | 0.3776 |
| Hyperbolic model 2 | 0.3883 |
| Power function model | 0.4723 |
| S-shaped curve model | 0.3551 |
| Gompertz curve | 0.4448 |
| Parabola model | 0.4556 |
| 2-degree curve model | 0.8922 |
From the second table, it can be found that the goodness of fit of the 2-time curve model is highest, so that the steel industry production activity value of the region a in the prediction month can be input into the prediction model under the 2-time curve model, and the steel industry power consumption of the region a in the prediction month can be predicted.
In the embodiment of the invention, the steel industry production activity value of the target area in each unit time period in the preset time period can be obtained through the multiple dimensionality activity values of the target area in each unit time period in the preset time period. And then, by utilizing various preset regression models, respectively establishing a prediction model of the steel industry production activity value under each preset regression model and the steel industry power consumption of the target area in each unit time period in the preset time period. And finally, predicting the power consumption of the steel industry in the target area by using the prediction model with the maximum goodness of fit. Therefore, causal connection between the production activity value of the steel industry and the power consumption of the steel industry can be quantized into a prediction model, and the power consumption of the steel industry in the region can be predicted more accurately by using the prediction model, so that the prediction accuracy of the power consumption of the steel industry is improved.
Based on the method for predicting the power consumption of the steel industry provided by the embodiment, correspondingly, the invention also provides a concrete implementation mode of the device for predicting the power consumption of the steel industry, which is applied to the method for predicting the power consumption of the steel industry. Please see the examples below.
As shown in fig. 3, there is provided an apparatus 300 for predicting a power consumption in a steel industry, the apparatus including:
the first obtaining module 310 is configured to obtain a steel industry production activity value of each unit time interval of a target area within a preset time interval; the production activity value of the steel industry is obtained based on a plurality of dimension activity values of a target area in each unit time period in a preset time period, and the plurality of dimension activity values at least comprise an iron ore production activity value, a crude steel production activity value, an automobile production activity value and a real estate new start construction area activity value;
the building module 320 is used for respectively building a prediction model of the steel industry production activity value under each preset regression model and the steel industry power consumption of the target area in each unit time period within the preset time period by using a plurality of preset regression models;
a second obtaining module 330, configured to obtain goodness-of-fit of each prediction model;
and the prediction module 340 is used for predicting the power consumption of the steel industry in the target area according to the prediction model with the maximum goodness of fit.
Optionally, the first obtaining module is further configured to:
acquiring a plurality of dimensionality vitality values of a target area in each unit time period within a preset time period;
and generating a production activity value of the steel industry according to the weight values of the plurality of dimension activity values.
Optionally, the first obtaining module is further configured to:
respectively taking a plurality of dimensional yields of a target area in a preset time period and a target unit time period as reference yields, and setting a dimensional activity value of each reference yield as a unit activity value; the target unit time interval is any one unit time interval in a preset time interval, and the multiple dimensional yields at least comprise iron ore yield, crude steel yield, automobile yield and real estate new construction area;
acquiring the ratio of each dimension yield of each unit time interval in a preset time interval to the corresponding reference yield of a target area;
and determining the product of the ratio corresponding to each dimension yield of each unit time interval of the target area in the preset time interval and the unit vitality value as the dimension vitality value of the corresponding dimension yield of the corresponding unit time interval of the target area in the preset time interval.
Optionally, the prediction module is further configured to:
acquiring a production activity value of a target area in a unit time period to be predicted in the steel industry;
and inputting the steel industry production activity value of the unit time period to be predicted into the prediction model with the maximum goodness of fit to obtain the steel industry power consumption of the target area in the unit time period to be predicted.
In the embodiment of the invention, the steel industry production activity value of the target area in each unit time period in the preset time period can be obtained through the multiple dimensionality activity values of the target area in each unit time period in the preset time period. And then, by utilizing various preset regression models, respectively establishing a prediction model of the steel industry production activity value under each preset regression model and the steel industry power consumption of the target area in each unit time period in the preset time period. And finally, predicting the power consumption of the steel industry in the target area by using the prediction model with the maximum goodness of fit. Therefore, causal connection between the production activity value of the steel industry and the power consumption of the steel industry can be quantized into a prediction model, and the power consumption of the steel industry in the region can be predicted more accurately by using the prediction model, so that the prediction accuracy of the power consumption of the steel industry is improved.
Fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 4, the terminal device 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40 implements the steps of the above-described embodiments of the method for predicting power usage in the steel industry when executing the computer program 42. Alternatively, the processor 40 implements the functions of the modules/units in the above-described device embodiments when executing the computer program 42.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 42 in the terminal device 4. For example, the computer program 42 may be divided into a first obtaining module, a constructing module, a second obtaining module, and a predicting module, and the specific functions of each module are as follows:
the first acquisition module is used for acquiring the production activity value of the steel industry of each unit time interval in a preset time interval of a target area; the production activity value of the steel industry is obtained based on a plurality of dimension activity values of a target area in each unit time period in a preset time period, and the plurality of dimension activity values at least comprise an iron ore production activity value, a crude steel production activity value, an automobile production activity value and a real estate new start construction area activity value;
the construction module is used for respectively establishing a prediction model of the steel industry production activity value under each preset regression model and the steel industry power consumption of the target area in each unit time period in the preset time period by utilizing a plurality of preset regression models;
the second acquisition module is used for acquiring the goodness of fit of each prediction model;
and the prediction module is used for predicting the power consumption of the steel industry in the target area according to the prediction model with the maximum goodness of fit.
The terminal device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device 4 and does not constitute a limitation of terminal device 4 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal device. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A prediction method for power consumption in the steel industry is characterized by comprising the following steps:
acquiring a production activity value of the target area in each unit time interval within a preset time interval in the steel industry; the production activity value of the steel industry is obtained based on a plurality of dimension activity values of the target region in each unit time period in a preset time period, and the plurality of dimension activity values at least comprise an iron ore production activity value, a crude steel production activity value, an automobile production activity value and a real estate new construction area activity value;
respectively establishing a prediction model of the production activity value of the steel industry and the power consumption of the steel industry of the target area in each unit time interval in the preset time interval under each preset regression model by utilizing a plurality of preset regression models;
and acquiring the goodness of fit of each prediction model, and predicting the power consumption of the steel industry in the target area according to the prediction model with the maximum goodness of fit.
2. The method for predicting power consumption of the steel industry according to claim 1, wherein the step of obtaining the value of the production activity of the steel industry in each unit time interval of the target area in the preset time interval comprises the following steps:
acquiring a plurality of dimensionality vitality values of the target area in each unit time period within a preset time period;
and generating the production activity value of the steel industry according to the weight values of the dimension activity values.
3. The method for predicting power consumption of the steel industry as claimed in claim 2, wherein the step of obtaining the plurality of dimensional vitality values of the target area in each unit time interval within the preset time interval comprises the following steps:
respectively taking a plurality of dimensional yields of the target region in a preset time period and a target unit time period as reference yields, and setting a dimensional activity value of each reference yield as a unit activity value; the target unit time interval is any one unit time interval in the preset time interval, and the multiple dimensional yields at least comprise iron ore yields, crude steel yields, automobile yields and real estate new construction areas;
acquiring the ratio of each dimension yield of each unit time interval of the target area in a preset time interval to the corresponding reference yield;
and determining the product of the ratio corresponding to each dimension yield of each unit time interval of the target area in a preset time interval and the unit vitality value as the dimension vitality value of the corresponding dimension yield of the corresponding unit time interval of the target area in the preset time interval.
4. The method of predicting power usage by an iron and steel industry of claim 1, wherein predicting power usage by the iron and steel industry for the target area based on the prediction model having the greatest goodness-of-fit comprises:
acquiring a production activity value of the target area in the unit time period to be predicted in the steel industry;
and inputting the steel industry production activity value of the unit time interval to be predicted into the prediction model with the maximum goodness of fit to obtain the steel industry power consumption of the target area in the unit time interval to be predicted.
5. A prediction device of power consumption of steel industry, characterized by that, includes:
the first acquisition module is used for acquiring the production activity value of the steel industry of each unit time interval in a preset time interval of a target area; the production activity value of the steel industry is obtained based on a plurality of dimension activity values of the target region in each unit time period in a preset time period, and the plurality of dimension activity values at least comprise an iron ore production activity value, a crude steel production activity value, an automobile production activity value and a real estate new construction area activity value;
the construction module is used for respectively establishing a prediction model of the steel industry production activity value and the steel industry power consumption of the target area in each unit time interval in the preset time interval under each preset regression model by utilizing a plurality of preset regression models;
the second obtaining module is used for obtaining the goodness of fit of each prediction model;
and the prediction module is used for predicting the power consumption of the steel industry in the target area according to the prediction model with the maximum goodness of fit.
6. The steel industry power usage prediction apparatus of claim 5, wherein the first obtaining module is further configured to:
acquiring a plurality of dimensionality vitality values of the target area in each unit time period within a preset time period;
and generating the production activity value of the steel industry according to the weight values of the dimension activity values.
7. The forecasting apparatus of power usage in the steel industry of claim 6, wherein the first obtaining module is further configured to:
respectively taking a plurality of dimensional yields of the target region in a preset time period and a target unit time period as reference yields, and setting a dimensional activity value of each reference yield as a unit activity value; the target unit time interval is any one unit time interval in the preset time interval, and the multiple dimensional yields at least comprise iron ore yields, crude steel yields, automobile yields and real estate new construction areas;
acquiring the ratio of each dimension yield of each unit time interval of the target area in a preset time interval to the corresponding reference yield;
and determining the product of the ratio corresponding to each dimension yield of each unit time interval of the target area in a preset time interval and the unit vitality value as the dimension vitality value of the corresponding dimension yield of the corresponding unit time interval of the target area in the preset time interval.
8. The steel industry power usage prediction apparatus of claim 5, wherein the prediction module is further configured to:
acquiring a production activity value of the target area in the unit time period to be predicted in the steel industry;
and inputting the steel industry production activity value of the unit time interval to be predicted into the prediction model with the maximum goodness of fit to obtain the steel industry power consumption of the target area in the unit time interval to be predicted.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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