CN109242220A - Charging station transaction power predicating method, device, electronic equipment and storage medium - Google Patents
Charging station transaction power predicating method, device, electronic equipment and storage medium Download PDFInfo
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- CN109242220A CN109242220A CN201811365235.5A CN201811365235A CN109242220A CN 109242220 A CN109242220 A CN 109242220A CN 201811365235 A CN201811365235 A CN 201811365235A CN 109242220 A CN109242220 A CN 109242220A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
Subject description discloses a kind of charging station transaction power predicating method, device, electronic equipment and storage mediums, wherein charging station trades power predicating method including obtaining influence factor relevant to transaction electricity；Using the influence factor as input, charged station transaction electricity regressive prediction model processing, prediction charging station transaction electricity.The charging station transaction electricity regressive prediction model obtaining step includes: to pre-process to charging station transaction history data, obtains influence factor relevant to transaction electricity and transaction electricity historical data；Utilize the transaction electricity historical data, influence factor building charging station transaction electricity regressive prediction model relevant to transaction electricity.Compared with prior art, the technical program can effectively predict the short-term charge requirement of charging station, precision of prediction with higher, so as to for charging station itself construction plan and Economic Dispatch provide data support.
Description
Technical field
This specification is related to charging technique field, in particular to a kind of charging station transaction power predicating method, device, electronics
Equipment and storage medium.
Background technique
With universal and electric automobile charging station the Large scale construction of electric car, electric car charges in smart grid
Short-term charge requirement prediction stand by more and more focus of attention, is increasingly becoming the frontier of research.Charging station charge requirement
Addition can generate biggish impact to area power grid, it is great that research significance is carried out to it.Similar to photovoltaic and wind-powered electricity generation, charging station
The randomness of charge requirement is larger, compared to traditional network load, predicts that difficulty is larger.
Summary of the invention
To solve the deficiencies in the prior art, this case provides the charging station transaction power quantity predicting scheme of a set of high accuracy, from
And the construction of charging station itself and the rational management of power system resource is effectively instructed to utilize.
To achieve the above object, this specification embodiment provides a kind of charging station transaction power predicating method, comprising:
Obtain influence factor relevant to transaction electricity；
Using the influence factor as input, charged station transaction electricity regressive prediction model processing, prediction charging station friendship
Easy electricity.
Preferably, the charging station transaction electricity regressive prediction model obtaining step includes:
Charging station transaction history data is pre-processed, influence factor relevant to transaction electricity and transaction electricity are obtained
Historical data；
Utilize the transaction electricity historical data, influence factor building charging station transaction back electric quantity relevant to transaction electricity
Return prediction model.
Preferably, further includes:
Exploratory analysis is carried out to the influence factor, obtains the influence factor with correlation between transaction electricity.
Preferably, carrying out pretreated step to charging station transaction history data includes:
The charging station transaction history data is cleaned；
Influence relevant to transaction electricity is obtained by web crawlers technology on the charging station transaction history data after cleaning
Factor and transaction electricity historical data.
Preferably, include: to the step of influence factor progress exploratory analysis
The relevance values for obtaining influence factor between electricity of trading using Pearson correlation coefficient；
The variation tendency for obtaining the influence factor between electricity of trading by descriptive analysis.
Preferably, it is handed over using the transaction electricity historical data, influence factor building charging station relevant to transaction electricity
Easily the step of electricity regressive prediction model, includes:
It is constructed using the transaction electricity historical data, influence factor relevant to transaction electricity based on random forests algorithm
The relative Link Importance of first charging station transaction electricity regressive prediction model and the corresponding influence factor；
Extreme gradient boosting algorithm is based on using the transaction electricity historical data, influence factor relevant to transaction electricity
Construct the relative Link Importance of the second charging station transaction electricity regressive prediction model and the corresponding influence factor；
Based on test data, the transaction power quantity predicting value and second of the first charging station transaction electricity regressive prediction model is obtained
The transaction power quantity predicting value of charging station transaction electricity regressive prediction model；
Determine the first charging station transaction electricity regressive prediction model respectively according to the transaction power quantity predicting value of two kinds of models
The mean absolute percentage error of mean absolute percentage error and the second charging station transaction electricity regressive prediction model；
The mean absolute percentage error and the second charging station for comparing the first charging station transaction electricity regressive prediction model are handed over
The mean absolute percentage error of easy electricity regressive prediction model, select the second charging station trade electricity regressive prediction model as
Charging station transaction electricity regressive prediction model.
Preferably, it is handed over using the transaction electricity historical data, influence factor building charging station relevant to transaction electricity
The step of easy electricity regressive prediction model further include:
Utilize the relative Link Importance of the corresponding influence factor of second charging station transaction electricity regressive prediction model
Filter out the big influence factor of relative Link Importance；
Optimize the second charging station transaction electricity regressive prediction model using the big influence factor of the relative Link Importance filtered out.
Preferably, the cleaning includes that missing values look into benefit, outlier identification, data deduplication.
Preferably, the influence factor includes charging pile unit exception rate, day type, national legal festivals and holidays, season, day
Vaporous condition, the day highest temperature, day lowest temperature.
To achieve the above object, this specification embodiment provides a kind of charging station transaction capacity predicting apparatus, comprising:
Influence factor acquiring unit, for obtaining influence factor relevant to electricity of trading；
Predicting unit is used for using the influence factor as input, and charged station transaction electricity regressive prediction model is handled,
Predict charging station transaction electricity.
Preferably, further includes:
Pretreatment unit obtains shadow relevant to transaction electricity for pre-processing to charging station transaction history data
The factor of sound and transaction electricity historical data；
Prediction model acquiring unit, for using the transaction electricity historical data, to trade the relevant influence of electricity because
Element building charging station transaction electricity regressive prediction model.
Preferably, further includes:
Influence factor screening unit obtains between transaction electricity for carrying out exploratory analysis to the influence factor
Influence factor with correlation.
Preferably, the pretreatment unit includes:
Cleaning module, for being cleaned to the charging station transaction history data；
Crawler capturing module, for the charging station transaction history data after cleaning to be obtained and handed over by web crawlers technology
The easy relevant influence factor of electricity and transaction electricity historical data.
Preferably, the influence factor screening unit includes:
Quantitative analysis module is related between influence factor and electricity of trading for being obtained using Pearson correlation coefficient
Property value；
Qualitative analysis module, the variation for obtaining the influence factor between electricity of trading by descriptive analysis become
Gesture.
Preferably, the prediction model acquiring unit includes:
First model construction module, for using the transaction electricity historical data, to trade the relevant influence of electricity because
Element constructs the phase of the first charging station transaction electricity regressive prediction model and the corresponding influence factor based on random forests algorithm
To different degree；
Second model construction module, for using the transaction electricity historical data, to trade the relevant influence of electricity because
Element constructs the second charging station transaction electricity regressive prediction model and the corresponding influence factor based on extreme gradient boosting algorithm
Relative Link Importance；
Test module obtains the transaction electricity of the first charging station transaction electricity regressive prediction model for being based on test data
Measure the transaction power quantity predicting value of predicted value and the second charging station transaction electricity regressive prediction model；
Mean absolute percentage error obtains module, for determining the respectively according to the transaction power quantity predicting value of two kinds of models
The mean absolute percentage error and the second charging station transaction electricity regression forecasting of one charging station transaction electricity regressive prediction model
The mean absolute percentage error of model；
Model selection module, the average absolute percentage for comparing the first charging station transaction electricity regressive prediction model miss
The mean absolute percentage error of difference and the second charging station transaction electricity regressive prediction model selects the second charging station transaction electricity
Regressive prediction model is as charging station transaction electricity regressive prediction model.
Preferably, the prediction model acquiring unit further include:
Relative Link Importance obtains module, for utilizing the corresponding institute of second charging station transaction electricity regressive prediction model
The relative Link Importance for stating influence factor filters out the big influence factor of relative Link Importance；
Model optimization module is traded for optimizing the second charging station using the big influence factor of the relative Link Importance filtered out
Electricity regressive prediction model.
Preferably, the cleaning module looks into benefit, outlier identification, data deduplication to charging station transactions history using missing values
Data are cleaned.
Preferably, the influence factor that the influence factor acquiring unit obtains include charging pile unit exception rate, day type,
National legal festivals and holidays, season, weather conditions, the day highest temperature, day lowest temperature.
To achieve the above object, this specification embodiment provide a kind of electronic equipment, including memory, processor and
It is stored in the computer program that can be run on the memory and on the processor, the processor executes the computer
Charging station transaction power predicating method described above is realized when program.
To achieve the above object, this specification embodiment provides a kind of readable storage medium storing program for executing, is stored thereon with computer
Program, the computer program are performed the step of realizing charging station transaction power predicating method described above.
Therefore compared with prior art, the technical program can effectively to the short-term charge requirement of charging station into
Row prediction, precision of prediction with higher, so as to the construction plan and Economic Dispatch for charging station itself
Data are provided to support.
Detailed description of the invention
It, below will be to embodiment party in order to illustrate more clearly of this specification embodiment or technical solution in the prior art
Formula or attached drawing needed to be used in the description of the prior art are briefly described, it should be apparent that, the accompanying drawings in the following description is only
It is only some embodiments recorded in this specification, for those of ordinary skill in the art, is not paying creative labor
Under the premise of dynamic property, it is also possible to obtain other drawings based on these drawings.
Fig. 1 is a kind of charging station transaction power predicating method flow chart that this specification embodiment proposes；
Fig. 2 is the charging station Day Trading electric quantity change trend schematic diagram of this specification embodiment；
Fig. 3 is the extreme gradient boosted tree schematic diagram of charging station transaction power quantity predicting in this specification embodiment；
Fig. 4 is influence factor relative Link Importance sequence schematic diagram in this specification embodiment；
Fig. 5 is that the prediction model of the present embodiment obtains the comparison chart of predicted value and actual value；
Fig. 6 is a kind of charging station transaction capacity predicting apparatus functional block diagram that this specification embodiment proposes；
Fig. 7 is a kind of electronic equipment schematic diagram that this specification embodiment proposes.
Specific embodiment
Below in conjunction with attached drawing, the technical solution in the embodiment of the present disclosure is clearly and completely described with reference to attached
The non-limiting example embodiment for showing in figure and being described in detail in the following description, the example that the disclosure is more fully described below are implemented
Example and their various features and Advantageous details.It should be noted that feature shown in figure is not required to be drawn to scale.This
The open description that known materials, component and technology is omitted, to not make the example embodiment of the disclosure fuzzy.It is given
Example be only intended to be conducive to understand the implementation of disclosure example embodiment, and further enable those skilled in the art real
Apply example embodiment.Thus, these examples are understood not to the limitation to the range of embodiment of the disclosure.
Unless otherwise specifically defined, the technical term or scientific term that the disclosure uses should be disclosure fields
The ordinary meaning that the interior personage with general technical ability is understood." first ", " second " used in the disclosure and similar word
Language is not offered as any sequence, quantity or importance, and is used only to distinguish different component parts.In addition, in the disclosure
In each embodiment, same or similar reference label indicates same or similar component.
As shown in Figure 1, a kind of charging station transaction power predicating method flow chart proposed for this specification embodiment.Packet
It includes:
Step 101): influence factor relevant to transaction electricity is obtained.
Step 102): using the influence factor as input, charged station transaction electricity regressive prediction model processing, prediction
Charging station transaction electricity.
In the technical scheme, charging station transaction electricity regressive prediction model is obtained based on the training of charging station transaction history data
?.In the present embodiment, charging station transaction history data is cleaned.The method of cleaning includes but is not limited to that missing values are inserted
Benefit, outlier processing, data deduplication etc..Influence is obtained from cleaned transaction history data using web crawlers technology
The influence factors such as weather conditions, the temperature of charging station transaction electricity.
In the present embodiment, influence factor: charging pile unit exception rate, weekend on working day, national legal festivals and holidays, season
(spring, summer, autumn and winter), weather conditions, the day highest temperature, day lowest temperature etc..Wherein,
Unit exception rate: charging pile unit exception rate influences the use of charging pile, and then influences the size of transaction electricity.
Weekend on working day: working day is only more in demand of the period resident on and off duty to electric car, in other periods
Demand to electric car is less；More in the travel activity of weekend resident, the demand to electric car is relatively more.
The national legal festivals and holidays: the national legal festivals and holidays (New Year's Day, the Spring Festival, the Ching Ming Festival, International Labour Day, the Dragon Boat Festival, the Mid-autumn Festival
And National Day), the outdoor activity of resident increases, and the demand to electric car is relatively more, and charging times increase.
Day highest temperature, day lowest temperature: when outside air temperature is higher or lower, vehicle just will use air-conditioning, and energy consumption will increase
Greatly, charging times will be increase accordingly, and total charge power just will increase.In addition, temperature influences very the battery capacity of vehicle
Greatly, battery charge time at different temperatures also can difference.
Weather conditions: weather pattern mainly influences the traveling of electric car and the trip situation of resident.When rain and snow weather, occupy
The trip of the people is opposite to be reduced, and the usage quantity of electric car can also be reduced；The travel speed of electric car is slower, day charging times
It will accordingly reduce.
Season: charging station Day Trading electricity seasonal variation characteristics are obvious, and spring and summer transaction electricity is higher, autumn and winter
Electricity of trading is relatively low.
The influence factor got is encoded, coding rule are as follows:
Weekend on working day: working day -0, weekend -1；
The national legal festivals and holidays: the Dragon Boat Festival -1, the Mid-autumn Festival -2, National Day -3, New Year's Day -4, the Spring Festival -5, the Ching Ming Festival -6, labour
Section -7, non-country's legal festivals and holidays -0；
Season: spring 3,4, May -1, summer 6,7, August -2, autumn 9,10, November -3, winter 12,1,2 months -4；
Weather conditions: fine -1, cloudy, negative -2, rain, snow -3.
In the present embodiment, charging station transaction electricity regressive prediction model construction step includes:
It is constructed using the transaction electricity historical data, influence factor relevant to transaction electricity based on random forests algorithm
The relative Link Importance of first charging station transaction electricity regressive prediction model and the corresponding influence factor；
Extreme gradient boosting algorithm is based on using the transaction electricity historical data, influence factor relevant to transaction electricity
Construct the relative Link Importance of the second charging station transaction electricity regressive prediction model and the corresponding influence factor；
Based on test data, the transaction power quantity predicting value and second of the first charging station transaction electricity regressive prediction model is obtained
The transaction power quantity predicting value of charging station transaction electricity regressive prediction model；
Determine the first charging station transaction electricity regressive prediction model respectively according to the transaction power quantity predicting value of two kinds of models
The mean absolute percentage error of mean absolute percentage error and the second charging station transaction electricity regressive prediction model；
The mean absolute percentage error and the second charging station for comparing the first charging station transaction electricity regressive prediction model are handed over
The mean absolute percentage error of easy electricity regressive prediction model, select the second charging station trade electricity regressive prediction model as
Charging station transaction electricity regressive prediction model.
In order to advanced optimize charging station transaction electricity regressive prediction model, returned in above-mentioned acquisition charging station transaction electricity
On the basis of prediction model step, further includes:
Utilize the relative Link Importance of the corresponding influence factor of second charging station transaction electricity regressive prediction model
Filter out the big influence factor of relative Link Importance；
Optimize the second charging station transaction electricity regressive prediction model using the big influence factor of the relative Link Importance filtered out.
Illustrate charging station transaction electricity regressive prediction model establishment process in order to apparent, the training sample of model is
20170518-20180523 districts and cities charging station Day Trading electricity and influence factor, test sample 20180524-20180530
Data.Charging station transaction electricity regressive prediction model is constructed respectively based on random forests algorithm and extreme gradient boosting algorithm.
(1) Random Forest model
The parameter setting of Random Forest model: defaulting number=500 of tree, variable number=2 divided every time, random forest
Type: return.
(2) extreme gradient promotes tree-model
Extreme gradient promotes the setting of tree-model major parameter: objective function objective=" reg:linear ", tree is most
Big depth max_depth=3 (being joined by CV tune, for preventing overfitting problem), learning rate eta=0.3 are (each by reducing
The weight of step enables to the model more robust established), the number of iterations nrounds=500, random seed seed=1234.
As shown in figure 3, for the extreme gradient boosted tree schematic diagram of charging station transaction power quantity predicting in the present embodiment.The knot of tree
Point is the characteristic attribute of input variable, and the value of each leaf node is with prediction score instead of the category of common decision tree
Label.
As shown in table 1, it sorts for relative Link Importance of each influence factor to transaction electricity, is given according to the data in table 1
Influence factor relative Link Importance sequence schematic diagram out, as shown in Figure 4.It is descending successively are as follows: constitutional law determines festivals or holidays > lowest temperature
> season > weekend on the working day > highest temperature > unit exception rate > weather conditions.In practice, relative Link Importance is higher, illustrate for
It is even more important to generate prediction charge capacity.
Table 1
4. model evaluation and optimization
Based on test data, the transaction power quantity predicting value and second of the first charging station transaction electricity regressive prediction model is obtained
The transaction power quantity predicting value of charging station transaction electricity regressive prediction model, as shown in Figure 5.Predicted value and actual value are carried out pair
Than measuring a model prediction using mean absolute percentage error MAPE (mean absolute percentage error)
As a result quality, as shown in table 2.Based on random forest charging station transaction electricity regressive prediction model average MAPE be
36.78%, the average MAPE of the charging station transaction electricity regressive prediction model based on extreme gradient boosted tree is 5.65%, is based on
The charging station transaction electricity regressive prediction model generalization ability of extreme gradient boosted tree is preferable, therefore final choice is based on extreme gradient
The charging station transaction electricity regressive prediction model of boosted tree is as charging station transaction power quantity predicting model.
Table 2
| Date | Actual value | RF | MAPE | XGBoost | MAPE |
| On May 24th, 2018 | 1560.55 | 1118.24 | 39.55% | 1535.59 | 1.63% |
| On May 25th, 2018 | 1851.49 | 983.28 | 88.30% | 1591.41 | 16.34% |
| On May 26th, 2018 | 2480.19 | 2254.01 | 10.03% | 2442.53 | 1.54% |
| On May 27th, 2018 | 2094.59 | 1736.03 | 20.65% | 2024.15 | 3.48% |
| On May 28th, 2018 | 1467.1 | 1054.73 | 39.10% | 1403.52 | 4.53% |
| On May 29th, 2018 | 1473.19 | 1117.25 | 31.86% | 1532.62 | 3.88% |
| On May 30th, 2018 | 1229.49 | 960.73 | 27.97% | 1338.53 | 8.15% |
In order to enable the charging station transaction electricity regressive prediction model of building is more accurate, from charging station transaction history data
The further exploratory analysis of the influence factor of middle acquisition makees further screening to influence factor based on the analysis results, so that ginseng
There is certain correlation really with transaction electricity with the influence factor of building charging station transaction electricity regressive prediction model.
Exploratory analysis includes two parts, and first part is to calculate influence factor and transaction using Pearson correlation coefficient
Correlation between electricity, this analysis are quantitative analysis.Such as the following table 3 it is found that unit exception rate, season and transaction electricity are in negative
Correlation；Remaining influence factor and transaction being positively correlated property of electricity, the correlation maximum of national legal festivals and holidays and transaction electricity,
There are certain positive correlations for weekend on working day, season, weather conditions, the highest temperature, lowest temperature and transaction electricity.
Second part in exploratory analysis is descriptive analysis, according to the corresponding pass between transaction electricity and influence factor
System, determines charging station Day Trading electric quantity change trend, as shown in Figure 2.As can be seen that the charging station Day Trading electricity seasonality becomes
It is obvious to change feature.Wherein, trading the spring and summer 3-8 month, electricity is relatively high, and month autumn and winter 9-2 is relatively low.Legal section is false
Day (May Day, 11, New Year's Day, Spring Festival etc.) transaction electricity increases suddenly.
Table 3
In addition, selecting influence factor according to the relative Link Importance of influence factor, filled to based on extreme gradient boosted tree
Power station transaction electricity regressive prediction model makees further optimization processing, so that precision of prediction is higher.
The technical program can effectively predict the short-term charge requirement of charging station, precision of prediction with higher,
So as to for charging station itself construction plan and Economic Dispatch provide data support.
As shown in fig. 6, a kind of charging station transaction capacity predicting apparatus functional block diagram proposed for this specification embodiment.Packet
It includes:
Influence factor acquiring unit 601, for obtaining influence factor relevant to electricity of trading；
Predicting unit 602, for using the influence factor as input, charged station to be traded at electricity regressive prediction model
Reason, prediction charging station transaction electricity.
In the present embodiment, further includes:
Pretreatment unit obtains shadow relevant to transaction electricity for pre-processing to charging station transaction history data
The factor of sound and transaction electricity historical data；
Prediction model acquiring unit, for using the transaction electricity historical data, to trade the relevant influence of electricity because
Element building charging station transaction electricity regressive prediction model.
In the present embodiment, further includes:
Influence factor screening unit obtains between transaction electricity for carrying out exploratory analysis to the influence factor
Influence factor with correlation.
In the present embodiment, the pretreatment unit includes:
Cleaning module, for being cleaned to the charging station transaction history data；
Crawler capturing module, for the charging station transaction history data after cleaning to be obtained and handed over by web crawlers technology
The easy relevant influence factor of electricity and transaction electricity historical data.
In the present embodiment, the influence factor screening unit includes:
Quantitative analysis module is related between influence factor and electricity of trading for being obtained using Pearson correlation coefficient
Property value；
Qualitative analysis module, the variation for obtaining the influence factor between electricity of trading by descriptive analysis become
Gesture.
In the present embodiment, the prediction model acquiring unit includes:
First model construction module, for using the transaction electricity historical data, to trade the relevant influence of electricity because
Element constructs the phase of the first charging station transaction electricity regressive prediction model and the corresponding influence factor based on random forests algorithm
To different degree；
Second model construction module, for using the transaction electricity historical data, to trade the relevant influence of electricity because
Element constructs the second charging station transaction electricity regressive prediction model and the corresponding influence factor based on extreme gradient boosting algorithm
Relative Link Importance；
Test module obtains the transaction electricity of the first charging station transaction electricity regressive prediction model for being based on test data
Measure the transaction power quantity predicting value of predicted value and the second charging station transaction electricity regressive prediction model；
Mean absolute percentage error obtains module, for determining the respectively according to the transaction power quantity predicting value of two kinds of models
The mean absolute percentage error and the second charging station transaction electricity regression forecasting of one charging station transaction electricity regressive prediction model
The mean absolute percentage error of model；
Model selection module, the average absolute percentage for comparing the first charging station transaction electricity regressive prediction model miss
The mean absolute percentage error of difference and the second charging station transaction electricity regressive prediction model selects the second charging station transaction electricity
Regressive prediction model is as charging station transaction electricity regressive prediction model.
In the present embodiment, the prediction model acquiring unit further include:
Relative Link Importance obtains module, for utilizing the corresponding institute of second charging station transaction electricity regressive prediction model
The relative Link Importance for stating influence factor filters out the big influence factor of relative Link Importance；
Model optimization module is traded for optimizing the second charging station using the big influence factor of the relative Link Importance filtered out
Electricity regressive prediction model.
In the present embodiment, the cleaning module is looked into benefit, outlier identification, data deduplication using missing values and is handed over charging station
Easy historical data is cleaned.
In the present embodiment, the influence factor that the influence factor acquiring unit obtains include charging pile unit exception rate,
Day type, national legal festivals and holidays, season, weather conditions, the day highest temperature, day lowest temperature.
As shown in fig. 7, a kind of electronic equipment schematic diagram proposed for this specification embodiment.Including memory, processor
And it is stored in the computer program that can be run on the memory and on the processor, the processor executes the meter
The transaction power predicating method of charging station described in above-mentioned Fig. 1 is realized when calculation machine program.
The charging station transaction power predicating method that this specification embodiment provides, the tool that memory and processor are realized
Body function can contrast explanation with the aforementioned embodiments in this specification, and can reach the technology of aforementioned embodiments
Effect just repeats no more here.
In the present embodiment, the memory may include the physical unit for storing information, usually by information
It is stored again with the media using the methods of electricity, magnetic or optics after digitlization.Memory described in present embodiment again may be used
To include: to store the device of information, such as RAM, ROM in the way of electric energy；The device of information is stored in the way of magnetic energy, it is such as hard
Disk, floppy disk, tape, core memory, magnetic bubble memory, USB flash disk；Using the device of optical mode storage information, such as CD or DVD.
Certainly, there are also memories of other modes, such as quantum memory, graphene memory etc..
In the present embodiment, the processor can be implemented in any suitable manner.For example, the processor can be with
Take such as microprocessor or processor and storage can by (micro-) processor execute computer readable program code (such as
Software or firmware) computer-readable medium, logic gate, switch, specific integrated circuit (Application Specific
Integrated Circuit, ASIC), programmable logic controller (PLC) and the form etc. for being embedded in microcontroller.
In the present embodiment, this specification embodiment also provides a kind of readable storage medium storing program for executing, is stored thereon with computer journey
Sequence, the computer program are performed the step of realizing charging station transaction power predicating method described above.
Compared with prior art, the technical program can effectively predict the short-term charge requirement of charging station, tool
Have higher precision of prediction, so as to for charging station itself construction plan and Economic Dispatch data branch is provided
It holds.
It is also known in the art that in addition to realizing client and server in a manner of pure computer readable program code
In addition, completely can by by method and step carry out programming in logic come so that client and server with logic gate, switch, dedicated
The form of integrated circuit, programmable logic controller (PLC) and insertion microcontroller etc. realizes identical function.Therefore this client
It is considered a kind of hardware component with server, and the device for realizing various functions for including in it can also be regarded
For the structure in hardware component.Or even, can will be considered as realizing the device of various functions either implementation method
Software module can be the structure in hardware component again.
As seen through the above description of the embodiments, those skilled in the art can be understood that this specification
It can realize by means of software and necessary general hardware platform.Based on this understanding, the technical solution of this specification
Substantially the part that contributes to existing technology can be embodied in the form of software products in other words, the computer software
Product can store in storage medium, such as ROM/RAM, magnetic disk, CD, including some instructions are used so that a computer
Equipment (can be personal computer, server or the network equipment etc.) executes each embodiment of this specification or implementation
Method described in certain parts of mode.
Each embodiment in this specification is described in a progressive manner, same and similar between each embodiment
Part may refer to each other, what each embodiment stressed is the difference with other embodiments.In particular, needle
For the embodiment of client and server, the introduction control for being referred to the embodiment of preceding method is explained.
This specification can describe in the general context of computer-executable instructions executed by a computer, such as journey
Sequence module.Generally, program module include routines performing specific tasks or implementing specific abstract data types, programs, objects,
Component, data structure etc..This specification can also be practiced in a distributed computing environment, in these distributed computing environment
In, by executing task by the connected remote processing devices of communication network.In a distributed computing environment, program module
It can be located in the local and remote computer storage media including storage equipment.
In the 1990s, the improvement of a technology can be distinguished clearly be on hardware improvement (for example,
Improvement to circuit structures such as diode, transistor, switches) or software on improvement (improvement for method flow).So
And with the development of technology, the improvement of current many method flows can be considered as directly improving for hardware circuit.
Designer nearly all obtains corresponding hardware circuit by the way that improved method flow to be programmed into hardware circuit.Cause
This, it cannot be said that the improvement of a method flow cannot be realized with hardware entities module.For example, programmable logic device
(Programmable Logic Device, PLD) (such as field programmable gate array (Field Programmable Gate
Array, FPGA)) it is exactly such a integrated circuit, logic function determines device programming by user.By designer
Voluntarily programming comes a digital display circuit " integrated " on a piece of PLD, designs and makes without asking chip maker
Dedicated IC chip.Moreover, nowadays, substitution manually makes IC chip, this programming is also used instead mostly " is patrolled
Volume compiler (logic compiler) " software realizes that software compiler used is similar when it writes with program development,
And the source code before compiling also write by handy specific programming language, this is referred to as hardware description language
(Hardware Description Language, HDL), and HDL is also not only a kind of, but there are many kind, 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 is most generally used at present
Integrated Circuit Hardware Description Language) and Verilog.Those skilled in the art also answer
This understands, it is only necessary to method flow slightly programming in logic and is programmed into integrated circuit with above-mentioned several hardware description languages,
The hardware circuit for realizing the logical method process can be readily available.
Controller can be implemented in any suitable manner, for example, controller can take such as microprocessor or processing
The computer for the computer readable program code (such as software or firmware) that device and storage can be executed by (micro-) processor can
Read medium, logic gate, switch, specific integrated circuit (Application Specific Integrated Circuit,
ASIC), the form of programmable logic controller (PLC) and insertion microcontroller, the example of controller includes but is not limited to following microcontroller
Device: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320 are deposited
Memory controller is also implemented as a part of the control logic of memory.It is also known in the art that in addition to
Pure computer readable program code mode is realized other than controller, can be made completely by the way that method and step is carried out programming in logic
Controller is obtained to come in fact in the form of logic gate, switch, specific integrated circuit, programmable logic controller (PLC) and insertion microcontroller etc.
Existing identical function.Therefore this controller is considered a kind of hardware component, and to including for realizing various in it
The device of function can also be considered as the structure in hardware component.Or even, it can will be regarded for realizing the device of various functions
For either the software module of implementation method can be the structure in hardware component again.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity,
Or it is realized by the product with certain function.It is a kind of typically to realize that equipment is computer.Specifically, computer for example may be used
Think personal computer, laptop computer, vehicle-mounted human-computer interaction device, cellular phone, camera phone, smart phone, individual
Digital assistants, media player, navigation equipment, electronic mail equipment, game console, tablet computer, wearable device or
The combination of any equipment in these equipment of person.
Although this specification embodiment provides the method operating procedure as described in embodiment or flow chart, based on conventional
It may include either more or less operating procedure without creative means.The step of being enumerated in embodiment sequence be only
One of numerous step execution sequence mode does not represent and unique executes sequence.Device or end product in practice is held
When row, can be executed according to embodiment or method shown in the drawings sequence or it is parallel execute (such as parallel processor or
The environment of multiple threads, even distributed data processing environment).The terms "include", "comprise" or its any other change
Body is intended to non-exclusive inclusion, so that process, method, product or equipment including a series of elements are not only wrapped
Those elements are included, but also including other elements that are not explicitly listed, or further includes for this process, method, product
Or the element that equipment is intrinsic.In the absence of more restrictions, being not precluded is including process, the side of the element
There is also other identical or equivalent elements in method, product or equipment.
For convenience of description, it is divided into various modules when description apparatus above with function to describe respectively.Certainly, implementing this
The function of each module can be realized in the same or multiple software and or hardware when specification embodiment, it can also be by reality
Show the module of same function by the combination realization etc. of multiple submodule or subelement.Installation practice described above is only
Schematically, for example, the division of the unit, only a kind of logical function partition, can there is other draw in actual implementation
The mode of dividing, such as multiple units or components can be combined or can be integrated into another system, or some features can be ignored,
Or it does not execute.Another point, shown or discussed mutual coupling, direct-coupling or communication connection can be by one
The indirect coupling or communication connection of a little interfaces, device or unit can be electrical property, mechanical or other forms.
It is also known in the art that other than realizing controller in a manner of pure computer readable program code, it is complete
Entirely can by by method and step carry out programming in logic come so that controller with logic gate, switch, specific integrated circuit, programmable
Logic controller realizes identical function with the form for being embedded in microcontroller etc..Therefore this controller is considered one kind
Hardware component, and the structure that the device for realizing various functions that its inside includes can also be considered as in hardware component.Or
Person even, can will be considered as realizing the device of various functions either the software module of implementation method can be hardware again
Structure in component.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
In a typical configuration, calculating equipment includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology come realize information store.Information can be computer readable instructions, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage or other magnetic storage devices
Or any other non-transmission medium, can be used for storage can be accessed by a computing device information.As defined in this article, it calculates
Machine readable medium does not include temporary computer readable media (transitory media), such as the data-signal and carrier wave of modulation.
It will be understood by those skilled in the art that the embodiment of this specification can provide as the production of method, system or computer program
Product.Therefore, in terms of this specification embodiment can be used complete hardware embodiment, complete software embodiment or combine software and hardware
Embodiment form.Moreover, it wherein includes computer available programs that this specification embodiment, which can be used in one or more,
Implement in the computer-usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) of code
The form of computer program product.
This specification embodiment can describe in the general context of computer-executable instructions executed by a computer,
Such as program module.Generally, program module includes routines performing specific tasks or implementing specific abstract data types, journey
Sequence, object, component, data structure etc..This specification embodiment can also be practiced in a distributed computing environment, in these points
Cloth calculates in environment, by executing task by the connected remote processing devices of communication network.In distributed computing ring
In border, program module can be located in the local and remote computer storage media including storage equipment.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality
For applying example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring to embodiment of the method
Part explanation.In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ",
The description of " specific example " or " some examples " etc. means specific features described in conjunction with this embodiment or example, structure, material
Or feature is contained at least one embodiment or example of this specification embodiment.In the present specification, to above-mentioned term
Schematic representation be necessarily directed to identical embodiment or example.Moreover, description specific features, structure, material or
Person's feature may be combined in any suitable manner in any one or more of the embodiments or examples.In addition, in not conflicting feelings
Under condition, those skilled in the art by different embodiments or examples described in this specification and different embodiment or can show
The feature of example is combined.
The foregoing is merely the embodiments of this specification embodiment, are not limited to this specification embodiment.It is right
For those skilled in the art, this specification embodiment can have various modifications and variations.It is all in this specification embodiment
Any modification, equivalent replacement, improvement and so within spirit and principle, the right that should be included in this specification embodiment are wanted
Within the scope of asking.
Although depicting this specification by embodiment, it will be appreciated by the skilled addressee that there are many this specification
Deformation and change without departing from this specification spirit, it is desirable to the attached claims include these deformation and change without departing from
The spirit of this specification.
Claims (20)
- The power predicating method 1. a kind of charging station is traded characterized by comprisingObtain influence factor relevant to transaction electricity；Using the influence factor as input, charged station transaction electricity regressive prediction model processing, prediction charging station transaction electricity Amount.
- 2. the method as described in claim 1, which is characterized in that the charging station transaction electricity regressive prediction model obtaining step Include:Charging station transaction history data is pre-processed, influence factor relevant to transaction electricity and transaction electricity history are obtained Data；It is returned using the transaction electricity historical data, influence factor building charging station transaction electricity relevant to transaction electricity pre- Survey model.
- 3. method according to claim 1 or 2, which is characterized in that further include:Exploratory analysis is carried out to the influence factor, obtains the influence factor with correlation between transaction electricity.
- 4. method according to claim 2, which is characterized in that carry out pretreated step packet to charging station transaction history data It includes:The charging station transaction history data is cleaned；Influence factor relevant to transaction electricity is obtained by web crawlers technology to the charging station transaction history data after cleaning And transaction electricity historical data.
- 5. method as claimed in claim 3, which is characterized in that the step of carrying out exploratory analysis to influence factor packet It includes:The relevance values for obtaining influence factor between electricity of trading using Pearson correlation coefficient；The variation tendency for obtaining the influence factor between electricity of trading by descriptive analysis.
- 6. method according to claim 2, which is characterized in that using the transaction electricity historical data, with trade electricity phase Pass influence factor building charging station transaction electricity regressive prediction model the step of include:Random forests algorithm building first is based on using the transaction electricity historical data, influence factor relevant to transaction electricity The relative Link Importance of charging station transaction electricity regressive prediction model and the corresponding influence factor；It is constructed using the transaction electricity historical data, influence factor relevant to transaction electricity based on extreme gradient boosting algorithm The relative Link Importance of second charging station transaction electricity regressive prediction model and the corresponding influence factor；Based on test data, the transaction power quantity predicting value and the second charging of the first charging station transaction electricity regressive prediction model are obtained The transaction power quantity predicting value for transaction electricity regressive prediction model of standing；Being averaged for the first charging station transaction electricity regressive prediction model is determined respectively according to the transaction power quantity predicting value of two kinds of models The mean absolute percentage error of absolute percent error and the second charging station transaction electricity regressive prediction model；Compare the mean absolute percentage error and the second charging station transaction electricity of the first charging station transaction electricity regressive prediction model The mean absolute percentage error for measuring regressive prediction model selects the second charging station transaction electricity regressive prediction model as charging Stand transaction electricity regressive prediction model.
- 7. method as claimed in claim 6, which is characterized in that using the transaction electricity historical data, with trade electricity phase The step of influence factor building charging station transaction electricity regressive prediction model of pass further include:It is screened using the relative Link Importance of the corresponding influence factor of second charging station transaction electricity regressive prediction model The big influence factor of relative Link Importance out；Optimize the second charging station transaction electricity regressive prediction model using the big influence factor of the relative Link Importance filtered out.
- 8. method as claimed in claim 4, which is characterized in that the cleaning includes that missing values look into benefit, outlier identification, data Duplicate removal.
- 9. method according to claim 1 or 2, which is characterized in that the influence factor includes charging pile unit exception rate, day Type, national legal festivals and holidays, season, weather conditions, the day highest temperature, day lowest temperature.
- The capacity predicting apparatus 10. a kind of charging station is traded characterized by comprisingInfluence factor acquiring unit, for obtaining influence factor relevant to electricity of trading；Predicting unit, for using the influence factor as input, charged station transaction electricity regressive prediction model processing, prediction Charging station transaction electricity.
- 11. device as claimed in claim 10, which is characterized in that further include:Pretreatment unit, for being pre-processed to charging station transaction history data, obtain influence relevant to transaction electricity because Element and transaction electricity historical data；Prediction model acquiring unit, for utilizing the transaction electricity historical data, influence factor structure relevant to transaction electricity Build charging station transaction electricity regressive prediction model.
- 12. device as described in claim 10 or 11, which is characterized in that further include:Influence factor screening unit, for carrying out exploratory analysis to the influence factor, obtaining has between transaction electricity The influence factor of correlation.
- 13. device as claimed in claim 11, which is characterized in that the pretreatment unit includes:Cleaning module, for being cleaned to the charging station transaction history data；Crawler capturing module, for being obtained and electricity of trading to the charging station transaction history data after cleaning by web crawlers technology Measure relevant influence factor and transaction electricity historical data.
- 14. device as claimed in claim 12, which is characterized in that the influence factor screening unit includes:Quantitative analysis module, the relevance values for obtaining influence factor between electricity of trading using Pearson correlation coefficient；Qualitative analysis module, the variation tendency for obtaining the influence factor between electricity of trading by descriptive analysis.
- 15. device as claimed in claim 11, which is characterized in that the prediction model acquiring unit includes:First model construction module, for utilizing the transaction electricity historical data, influence factor base relevant to transaction electricity In random forests algorithm construct the first charging station transaction electricity regressive prediction model and the corresponding influence factor it is relatively heavy It spends；Second model construction module, for utilizing the transaction electricity historical data, influence factor base relevant to transaction electricity The phase of the second charging station transaction electricity regressive prediction model and the corresponding influence factor is constructed in extreme gradient boosting algorithm To different degree；Test module, for being based on test data, the transaction electricity for obtaining the first charging station transaction electricity regressive prediction model is pre- The transaction power quantity predicting value of measured value and the second charging station transaction electricity regressive prediction model；Mean absolute percentage error obtains module, for determining that first fills respectively according to the transaction power quantity predicting value of two kinds of models The mean absolute percentage error and the second charging station transaction electricity regressive prediction model of power station transaction electricity regressive prediction model Mean absolute percentage error；Model selection module, for compare the first charging station transaction electricity regressive prediction model mean absolute percentage error and The mean absolute percentage error of second charging station transaction electricity regressive prediction model selects the second charging station transaction electricity to return Prediction model is as charging station transaction electricity regressive prediction model.
- 16. device as claimed in claim 15, which is characterized in that the prediction model acquiring unit further include:Relative Link Importance obtains module, for utilizing the corresponding shadow of second charging station transaction electricity regressive prediction model The relative Link Importance of the factor of sound filters out the big influence factor of relative Link Importance；Model optimization module, for optimizing the second charging station transaction electricity using the big influence factor of the relative Link Importance filtered out Regressive prediction model.
- 17. device as claimed in claim 13, which is characterized in that the cleaning module looks into benefit using missing values, exceptional value is known Not, data deduplication cleans charging station transaction history data.
- 18. device as described in claim 10 or 11, which is characterized in that the influence that the influence factor acquiring unit obtains because Element includes charging pile unit exception rate, day type, national legal festivals and holidays, season, weather conditions, the day highest temperature, day lowest temperature.
- 19. a kind of electronic equipment, including memory, processor and it is stored on the memory and can be on the processor The computer program of operation, which is characterized in that the processor realizes claim 1~9 times when executing the computer program Charging station described in a claim of anticipating transaction power predicating method.
- 20. a kind of readable storage medium storing program for executing, is stored thereon with computer program, which is characterized in that the computer program is performed The step of transaction power predicating method of charging station described in Shi Shixian claim 1~9 any one claim.
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