CN109993360A - A kind of prediction technique and device of power data - Google Patents
A kind of prediction technique and device of power data Download PDFInfo
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Abstract
The invention discloses a kind of prediction technique of power data and devices, this method comprises: receiving the historical data of each load point in area information belonging to each block supply data and each region, determine the load index and parameter in different classes of region；A variety of prediction models are based on to each load point in each region and historical data establishes hybrid model for short-term load forecasting, each load point in each region is carried out according to weight by COMPREHENSIVE CALCULATING according to the load index in each region and parameter, obtains power data prediction model；Power data area information to be predicted is received, input power data prediction model predicts power data.
Description
Technical field
The disclosure belongs to the technical field of Power System Planning, is related to the prediction technique and device of a kind of power data.
Background technique
Only there is provided background technical informations relevant to the disclosure for the statement of this part, it is not necessary to so constitute first skill
Art.
Power data prediction is the basis of planning and design of power system, and with China's rapid economic development, electric system is supplied
Electric data prediction becomes to be more and more obvious to the importance of the development of entire society.The accuracy of power system power supply data prediction
Directly influence entire urban planning and construction.The prediction of existing power data be according to the relevant historical data of local power grid and
Local economic development situation, industrial structure, and the data such as Economic development direction carry out later.Currently, the method for prediction
Including year-on-year growth rate method, trade power consumption growth method, hydrometer method, district office electricity growth method and storage increment growth method etc..
But inventor has found in R&D process, the accuracy of Yao Tigao power data prediction, therefore, to assure that prediction system
System accumulation has enough, accurate history reference sample information, and utilizes newest power data information as far as possible, prediction knot
Restriction of the accuracy of fruit by available data.Since existing various power data prediction techniques are all there is uncertainty,
In order to ensure the accuracy of prediction result, in practical application, will according to different situations, different purpose, using different methods,
And need while using several different prediction techniques, to check prediction result mutually, so that long the time required to prediction, mistake
Journey is complicated.
Summary of the invention
For the deficiencies in the prior art, one or more other embodiments of the present disclosure provide a kind of power data
Prediction technique and device establish hybrid model for short-term load forecasting according to the historical data of different zones different load point, and according to region spy
Point matching hybrid model for short-term load forecasting weight carries out the prediction of specific block supply data, has specific aim, only needs to input in prediction
Design parameter avoids the check that result is carried out in prediction, effectively improves the accuracy and predetermined speed of power data prediction.
According to the one aspect of one or more other embodiments of the present disclosure, a kind of prediction technique of power data is provided.
A kind of prediction technique of power data, this method comprises:
The historical data of each load point in area information belonging to each block supply data and each region is received, is determined different
The load index and parameter of category regions；
A variety of prediction models are based on to each load point in each region and historical data establishes hybrid model for short-term load forecasting, according to
Each load point in each region is carried out COMPREHENSIVE CALCULATING according to weight by the load index and parameter in each region, obtains power data prediction mould
Type；
Power data area information to be predicted is received, input power data prediction model predicts power data.
Further, in the method, area information belonging to the power data includes planning data, power supply unit
Geographical location, attribute information；
The historical data of each load point includes history electricity and historical load data in each region.
Further, in the method, the specific steps packet of the load index in the different classes of region of the determination and parameter
It includes:
Power supply unit is established according to area information belonging to the power data and plans the matching relationship of data；
The area classification is determined according to the historical data of each load point in the matching relationship and each region；The region class
Four class regions are not divided into from high to low according to load according to load nature of electricity consumed and category of employment；
Load Characteristic Analysis is carried out to every class region, determines the load index and parameter in every class region, every class region
Load index and parameter include classification land used load density target, classification construction area load density target, demand factor, point
Industry distribution transforming utilization rate parameter, branch trade typical load curve.
Further, in the method, a variety of prediction models include individual event prediction model and multiple combinations prediction mould
Type.
Further, in the method, each load point to each region is based on a variety of prediction models and history
Data establish hybrid model for short-term load forecasting are as follows: predict a variety of individual event prediction models of each load point and multiple combinations in each region
Model verifies its historical data, selects the smallest prediction model of average absolute value error as the compound pre- of the load point
Survey model.
Further, in the method, described the step of obtaining power data prediction model are as follows:
The weight parameter of each region is determined according to the load index in every class region and parameter；
It is overlapped the hybrid model for short-term load forecasting of all load points in each region to obtain the hybrid model for short-term load forecasting in each region；
The hybrid model for short-term load forecasting in each region is overlapped COMPREHENSIVE CALCULATING according to the weight parameter of each region, is powered
Data prediction model.
Further, in the method, area information belonging to the power data is digitized, determine its with not
The load index in generic region and the corresponding relationship of parameter, it is described as the input data of power data prediction model
The output of power data prediction model is the power data of prediction.
According to the one aspect of one or more other embodiments of the present disclosure, a kind of computer readable storage medium is provided.
A kind of computer readable storage medium, wherein being stored with a plurality of instruction, described instruction is suitable for by terminal device
Reason device loads and executes a kind of prediction technique of power data.
According to the one aspect of one or more other embodiments of the present disclosure, a kind of terminal device is provided.
A kind of terminal device comprising processor and computer readable storage medium, processor is for realizing each instruction；Meter
Calculation machine readable storage medium storing program for executing is suitable for being loaded by processor and being executed a kind of power supply for storing a plurality of instruction, described instruction
The prediction technique of data.
According to the one aspect of one or more other embodiments of the present disclosure, a kind of prediction meanss of power data are provided.
A kind of prediction meanss of power data, the prediction technique based on a kind of power data, comprising:
Data access module is configured as receiving each load in area information belonging to each block supply data and each region
The historical data of point, determines the load index and parameter in different classes of region；
Model building module is configured as being based on a variety of prediction models and historical data to each load point in each region
Hybrid model for short-term load forecasting is established, each load point in each region is carried out according to weight by comprehensive meter according to the load index in each region and parameter
It calculates, obtains power data prediction model；
Data prediction module is configured as receiving power data area information to be predicted, inputs power data prediction model
Predict power data.
The disclosure the utility model has the advantages that
The prediction technique and device for a kind of power data that the disclosure provides, according to the history of different zones different load point
Data establish hybrid model for short-term load forecasting, and match hybrid model for short-term load forecasting weight according to regional characteristics and carry out specific block supply data
Prediction has specific aim, and input design parameter is only needed in prediction, avoids the check for carrying out result in prediction, effectively mentions
The accuracy and predetermined speed of high power data prediction.
Detailed description of the invention
The accompanying drawings constituting a part of this application is used to provide further understanding of the present application, and the application's shows
Meaning property embodiment and its explanation are not constituted an undue limitation on the present application for explaining the application.
Fig. 1 is the prediction technique flow chart according to a kind of power data of one or more embodiments.
Specific embodiment:
Below in conjunction with the attached drawing in one or more other embodiments of the present disclosure, to one or more other embodiments of the present disclosure
In technical solution be clearly and completely described, it is clear that described embodiments are only a part of the embodiments of the present invention,
Instead of all the embodiments.Based on one or more other embodiments of the present disclosure, those of ordinary skill in the art are not being made
Every other embodiment obtained, shall fall within the protection scope of the present invention under the premise of creative work.
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms that the present embodiment uses have and the application person of an ordinary skill in the technical field
Normally understood identical meanings.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
It should be noted that flowcharts and block diagrams in the drawings show according to various embodiments of the present disclosure method and
The architecture, function and operation in the cards of system.It should be noted that each box in flowchart or block diagram can represent
A part of one module, program segment or code, a part of the module, program segment or code may include one or more
A executable instruction for realizing the logic function of defined in each embodiment.It should also be noted that some alternately
Realization in, function marked in the box can also occur according to the sequence that is marked in attached drawing is different from.For example, two connect
The box even indicated can actually be basically executed in parallel or they can also be executed in a reverse order sometimes,
This depends on related function.It should also be noted that each box and flow chart in flowchart and or block diagram
And/or the combination of the box in block diagram, the dedicated hardware based system that functions or operations as defined in executing can be used are come
It realizes, or the combination of specialized hardware and computer instruction can be used to realize.
In the absence of conflict, the feature in the embodiment and embodiment in the disclosure can be combined with each other, and tie below
It closes attached drawing and embodiment is described further the disclosure.
Embodiment one
According to the one aspect of one or more other embodiments of the present disclosure, a kind of prediction technique of power data is provided.
As shown in Figure 1, a kind of prediction technique of power data, this method comprises:
According to the one aspect of one or more other embodiments of the present disclosure, a kind of prediction technique of power data is provided.
A kind of prediction technique of power data, this method comprises:
S101: the historical data of each load point in area information belonging to each block supply data and each region is received, really
The load index and parameter in fixed different classes of region；
S102: a variety of prediction models are based on to each load point in each region and historical data establishes compound prediction mould
Each load point in each region is carried out COMPREHENSIVE CALCULATING according to weight according to the load index in each region and parameter, obtains power supply number by type
It is predicted that model；
S103: receiving power data area information to be predicted, and input power data prediction model predicts power data.
In the step S101 of one or more other embodiments of the present disclosure, area information belonging to the power data includes
Planning data, the geographical location of power supply unit, attribute information；
The historical data of each load point includes history electricity and historical load data in each region.
In the step S101 of one or more other embodiments of the present disclosure, the load index in the different classes of region of determination
Specific steps with parameter include:
Power supply unit is established according to area information belonging to the power data and plans the matching relationship of data；
The area classification is determined according to the historical data of each load point in the matching relationship and each region；The region class
Four class regions are not divided into from high to low according to load according to load nature of electricity consumed and category of employment；
Load Characteristic Analysis is carried out to every class region, determines the load index and parameter in every class region, every class region
Load index and parameter include classification land used load density target, classification construction area load density target, demand factor, point
Industry distribution transforming utilization rate parameter, branch trade typical load curve.
In the step S102 of one or more other embodiments of the present disclosure, a variety of prediction models include individual event prediction mould
Type and multiple combinations prediction model.
In the step S102 of one or more other embodiments of the present disclosure, each load point to each region is based on
A variety of prediction models and historical data establish hybrid model for short-term load forecasting are as follows: predict a variety of individual events of each load point in each region
Model and multiple combinations prediction model verify its historical data, and the smallest prediction model of average absolute value error is selected to make
For the hybrid model for short-term load forecasting of the load point.
In the step S102 of one or more other embodiments of the present disclosure, described the step of obtaining power data prediction model
Are as follows:
The weight parameter of each region is determined according to the load index in every class region and parameter；
It is overlapped the hybrid model for short-term load forecasting of all load points in each region to obtain the hybrid model for short-term load forecasting in each region；
The hybrid model for short-term load forecasting in each region is overlapped COMPREHENSIVE CALCULATING according to the weight parameter of each region, is powered
Data prediction model.
In the step S102 of one or more other embodiments of the present disclosure, by area information belonging to the power data into
Digitized determines itself and the load index in different classes of region and the corresponding relationship of parameter, predicts as power data
The input data of model, the output of the power data prediction model are the power data of prediction.
In the step S103 of one or more other embodiments of the present disclosure, according to identical Information Number in step S102
Word method digitizes power data area information to be predicted.
Embodiment two
According to the one aspect of one or more other embodiments of the present disclosure, a kind of computer readable storage medium is provided.
A kind of computer readable storage medium, wherein being stored with a plurality of instruction, described instruction is suitable for by terminal device
Reason device loads and executes a kind of prediction technique of power data.
Embodiment three
According to the one aspect of one or more other embodiments of the present disclosure, a kind of terminal device is provided.
A kind of terminal device comprising processor and computer readable storage medium, processor is for realizing each instruction；Meter
Calculation machine readable storage medium storing program for executing is suitable for being loaded by processor and being executed a kind of power supply for storing a plurality of instruction, described instruction
The prediction technique of data.
These computer executable instructions execute the equipment according to each reality in the disclosure
Apply method or process described in example.
In the present embodiment, computer program product may include computer readable storage medium, containing for holding
The computer-readable program instructions of row various aspects of the disclosure.Computer readable storage medium, which can be, can keep and store
By the tangible device for the instruction that instruction execution equipment uses.Computer readable storage medium for example can be-- but it is unlimited
In-- storage device electric, magnetic storage apparatus, light storage device, electric magnetic storage apparatus, semiconductor memory apparatus or above-mentioned
Any appropriate combination.The more specific example (non exhaustive list) of computer readable storage medium includes: portable computing
Machine disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM or
Flash memory), static random access memory (SRAM), Portable compressed disk read-only memory (CD-ROM), digital versatile disc
(DVD), memory stick, floppy disk, mechanical coding equipment, the punch card for being for example stored thereon with instruction or groove internal projection structure, with
And above-mentioned any appropriate combination.Computer readable storage medium used herein above is not interpreted instantaneous signal itself,
The electromagnetic wave of such as radio wave or other Free propagations, the electromagnetic wave (example propagated by waveguide or other transmission mediums
Such as, pass through the light pulse of fiber optic cables) or pass through electric wire transmit electric signal.
Computer-readable program instructions described herein can be downloaded to from computer readable storage medium it is each calculate/
Processing equipment, or outer computer or outer is downloaded to by network, such as internet, local area network, wide area network and/or wireless network
Portion stores equipment.Network may include copper transmission cable, optical fiber transmission, wireless transmission, router, firewall, interchanger, gateway
Computer and/or Edge Server.Adapter or network interface in each calculating/processing equipment are received from network to be counted
Calculation machine readable program instructions, and the computer-readable program instructions are forwarded, for the meter being stored in each calculating/processing equipment
In calculation machine readable storage medium storing program for executing.
Computer program instructions for executing present disclosure operation can be assembly instruction, instruction set architecture (I SA)
Instruction, machine instruction, machine-dependent instructions, microcode, firmware instructions, condition setup data or with one or more programmings
The source code or object code that any combination of language is write, the programming language include the programming language-of object-oriented such as
C++ etc., and conventional procedural programming languages-such as " C " language or similar programming language.Computer-readable program refers to
Order can be executed fully on the user computer, partly be executed on the user computer, as an independent software package
Execute, part on the user computer part on the remote computer execute or completely on a remote computer or server
It executes.In situations involving remote computers, remote computer can include local area network by the network-of any kind
(LAN) or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as utilize internet
Service provider is connected by internet).In some embodiments, by being believed using the state of computer-readable program instructions
Breath comes personalized customization electronic circuit, such as programmable logic circuit, field programmable gate array (FPGA) or programmable logic
Array (PLA), the electronic circuit can execute computer-readable program instructions, to realize the various aspects of present disclosure.
Example IV
According to the one aspect of one or more other embodiments of the present disclosure, a kind of prediction meanss of power data are provided.
A kind of prediction meanss of power data, the prediction technique based on a kind of power data, comprising:
Data access module is configured as receiving each load in area information belonging to each block supply data and each region
The historical data of point, determines the load index and parameter in different classes of region；
Model building module is configured as being based on a variety of prediction models and historical data to each load point in each region
Hybrid model for short-term load forecasting is established, each load point in each region is carried out according to weight by comprehensive meter according to the load index in each region and parameter
It calculates, obtains power data prediction model；
Data prediction module is configured as receiving power data area information to be predicted, inputs power data prediction model
Predict power data.
It should be noted that although being referred to several modules or submodule of equipment in the detailed description above, it is this
Division is only exemplary rather than enforceable.In fact, in accordance with an embodiment of the present disclosure, two or more above-described moulds
The feature and function of block can embody in a module.Conversely, the feature and function of an above-described module can be with
Further division is to be embodied by multiple modules.
The disclosure the utility model has the advantages that
The prediction technique and device for a kind of power data that the disclosure provides, according to the history of different zones different load point
Data establish hybrid model for short-term load forecasting, and match hybrid model for short-term load forecasting weight according to regional characteristics and carry out specific block supply data
Prediction has specific aim, and input design parameter is only needed in prediction, avoids the check for carrying out result in prediction, effectively mentions
The accuracy and predetermined speed of high power data prediction.
The foregoing is merely preferred embodiment of the present application, are not intended to limit this application, for the skill of this field
For art personnel, various changes and changes are possible in this application.Within the spirit and principles of this application, made any to repair
Change, equivalent replacement, improvement etc., should be included within the scope of protection of this application.Therefore, the present invention is not intended to be limited to this
These embodiments shown in text, and it is to fit to the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. a kind of prediction technique of power data, which is characterized in that this method comprises:
The historical data for receiving each load point in area information belonging to each block supply data and each region, determines different classes of
The load index and parameter in region；
A variety of prediction models are based on to each load point in each region and historical data establishes hybrid model for short-term load forecasting, according to each area
Each load point in each region is carried out COMPREHENSIVE CALCULATING according to weight by the load index and parameter in domain, obtains power data prediction model；
Power data area information to be predicted is received, input power data prediction model predicts power data.
2. a kind of prediction technique of power data as described in claim 1, which is characterized in that area belonging to the power data
Domain information includes planning data, the geographical location of power supply unit, attribute information；
The historical data of each load point includes history electricity and historical load data in each region.
3. a kind of prediction technique of power data as described in claim 1, which is characterized in that the different classes of region of determination
Load index and the specific steps of parameter include:
Power supply unit is established according to area information belonging to the power data and plans the matching relationship of data；
The area classification is determined according to the historical data of each load point in the matching relationship and each region；The area classification is pressed
Four class regions are divided into from high to low according to load according to load nature of electricity consumed and category of employment；
Load Characteristic Analysis is carried out to every class region, determines the load index and parameter in every class region, every class region is born
Lotus index and parameter include classification land used load density target, classification construction area load density target, demand factor, branch trade
Distribution transforming utilization rate parameter, branch trade typical load curve.
4. a kind of prediction technique of power data as described in claim 1, which is characterized in that a variety of prediction models include
Individual event prediction model and multiple combinations prediction model.
5. a kind of prediction technique of power data as described in claim 1, which is characterized in that described to each of each region
Load point is based on a variety of prediction models and historical data establishes hybrid model for short-term load forecasting are as follows: uses each load point in each region more
Kind individual event prediction model and multiple combinations prediction model verify its historical data, select average absolute value error the smallest
Hybrid model for short-term load forecasting of the prediction model as the load point.
6. a kind of prediction technique of power data as described in claim 1, which is characterized in that described to obtain power data prediction
The step of model are as follows:
The weight parameter of each region is determined according to the load index in every class region and parameter；
It is overlapped the hybrid model for short-term load forecasting of all load points in each region to obtain the hybrid model for short-term load forecasting in each region；
The hybrid model for short-term load forecasting in each region is overlapped COMPREHENSIVE CALCULATING according to the weight parameter of each region, obtains power data
Prediction model.
7. a kind of prediction technique of power data as described in claim 1, which is characterized in that will be belonging to the power data
Area information is digitized, itself and the load index in different classes of region and the corresponding relationship of parameter is determined, as confession
The input data of electric data prediction model, the output of the power data prediction model are the power data of prediction.
8. a kind of computer readable storage medium, wherein being stored with a plurality of instruction, which is characterized in that described instruction is suitable for by terminal
The processor of equipment is loaded and is executed such as a kind of described in any item prediction techniques of power data of claim 1-7.
9. a kind of terminal device comprising processor and computer readable storage medium, processor is for realizing each instruction；It calculates
Machine readable storage medium storing program for executing is for storing a plurality of instruction, which is characterized in that described instruction is suitable for being loaded by processor and being executed such as power
Benefit requires a kind of described in any item prediction techniques of power data of 1-7.
10. a kind of prediction meanss of power data, which is characterized in that based on such as a kind of described in any item confessions of claim 1-7
The prediction technique of electric data, comprising:
Data access module is configured as receiving each load point in area information belonging to each block supply data and each region
Historical data determines the load index and parameter in different classes of region；
Model building module is configured as being based on a variety of prediction models to each load point in each region and historical data is established
Each load point in each region is carried out COMPREHENSIVE CALCULATING according to weight according to the load index in each region and parameter by hybrid model for short-term load forecasting,
Obtain power data prediction model；
Data prediction module is configured as receiving power data area information to be predicted, input power data prediction model prediction
Power data.
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| CN110889544A (en) * | 2019-11-20 | 2020-03-17 | 贵州电网有限责任公司电力科学研究院 | Method and device for predicting operation indexes of power distribution network |
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