CN110264068A - The treating method and apparatus of electric power data - Google Patents
The treating method and apparatus of electric power data Download PDFInfo
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Abstract
This application discloses a kind for the treatment of method and apparatus of electric power data.This method comprises: responding judgement schematics by the magnification index of load distance, the load distance of various time points between the electric system actual load and power system objectives load of various time points and the weight coefficient building electricity needs of various time points；Parameter based on multiple history electricity needs response events constructs model, to update the magnification index of the load distance of various time points and the weight coefficient of various time points；The judgement schematics of the magnification index of load distance based on updated various time points, the weight coefficient at updated each time point and electricity needs response system evaluate target power demand response event.It by the application, solves and is evaluated in the related technology in order to which the stable operation of power grid needs to respond electricity needs, but every suit electricity needs response system is required to customization evaluation of programme, the not high problem of the pervasive degree of evaluation of programme.
Description
Technical field
This application involves electric power data process fields, in particular to a kind for the treatment of method and apparatus of electric power data.
Background technique
In order to ensure power grid operation, need to implement demand response work to electricity consumption situation, demand response mainly includes
Several links such as load prediction, response events initiation, index decomposition, instruction execution and response evaluation.Due to regional, scene
Difference, when demand response is embodied, there are respective differences, improve the research and development cost of related system, in evaluation link
It is particularly evident.That is, needing to design independent evaluation algorithms in the demand response system of every suit deployment.
Since demand response will affect the normal electricity consumption of user, this has manufactured certain barrier for the landing popularization of demand response
Hinder.As the last one link of demand response, response evaluation is closely bound up with the subsidy to user, directly affects the ginseng of user
With wish and specific participation method, a kind of being capable of adaptive different demands responding scene, user's acceptance height how is designed
Evaluation method, to push demand response landing and implementation be of great benefit to.
For in the related technology in order to the stable operation of power grid need to electricity needs respond evaluate, but every suit electricity
Power demand response system is required to customization evaluation of programme, and the not high problem of the pervasive degree of evaluation of programme not yet proposes effective at present
Solution.
Summary of the invention
The application provides a kind for the treatment of method and apparatus of electric power data, to solve in the related technology for the stabilization of power grid
Operation, which needs to respond electricity needs, to be evaluated, but every suit electricity needs response system is required to customization evaluation of programme, comments
The not high problem of the pervasive degree of valence scheme.
According to the one aspect of the application, a kind of processing method of electric power data is provided.This method comprises: by it is each when
Between load distance, the load distance of various time points between the electric system actual load put and power system objectives load
Magnification index and the weight coefficient of various time points building electricity needs respond judgement schematics；Based on multiple history electricity needs
The parameter of response events constructs model, to update the magnification index of the load distance of various time points and the power of various time points
Weight coefficient, wherein parameter includes at least the electric system actual load and history electricity needs of history electricity needs response events
The power system objectives load of response events；After the magnification index of load distance based on updated various time points, update
Each time point weight coefficient and electricity needs response system judgement schematics, to target power demand response event carry out
Evaluation.
Further, by the load distance between the electric system actual load of various time points and power system objectives load
The weight coefficient building electricity needs response evaluation of magnification index and various time points with a distance from the load of, various time points
Formula includes: the load distance constructed between i moment electric system actual load and power system objectives load
Wherein, piIndicate the electric system actual load at i moment,Indicate the power system objectives load at i moment；
According toThe magnification index of the load distance of various time points and the weight coefficient of various time points
Determine target load distance
Wherein, wiIndicate that the weight coefficient at i moment, m indicate that the magnification index of the load distance at i moment, n indicate measurement
The number at time point；
Based on target load distanceIt constructs electricity needs and responds judgement schematics s:
Further, the parameter based on multiple history electricity needs response events constructs model, to update various time points
The magnification index of load distance and the weight coefficient of various time points include: based on multiple history electricity needs response events
The neural network model of building, and history electricity needs is exported according to neural network model and responds evaluation of estimate；Calculate history electric power
The variance of demand response evaluation of estimate and desired electricity needs response evaluation of estimate；The load distance of various time points is updated based on variance
From magnification index；The weight coefficient of various time points is updated based on variance.
Further, the neural network model based on the building of multiple history electricity needs response events, and according to nerve net
Network model output history electricity needs responds evaluation of estimate and includes:
Input layer is constructed, in the electric system actual load p of input layer input i moment history electricity needs response eventsi
With power system objectives load
Hidden layer is constructed, calculates median h in hidden layeri:
Wherein, m indicates the magnification index of the load distance at i moment；
Output layer is constructed, history electricity needs is calculated in output layer and responds evaluation of estimate oreal:
Wherein, wiIndicate that the weight coefficient at i moment, n indicate the number at the time point measured；
Based on input layer, hidden layer and output layer building neural network model；Based on neural network model output history electricity
Power demand response evaluation of estimate oreal。
Further, the variance packet of history electricity needs response evaluation of estimate and desired electricity needs response evaluation of estimate is calculated
It includes:
Wherein, E indicates the variance of history electricity needs response evaluation of estimate and desired electricity needs response evaluation of estimate, otar
Indicate desired electricity needs response evaluation of estimate.
Further, the magnification index of load distance for updating various time points based on variance includes:
Variance is calculated about wiLocal derviation
Wherein,Indicate variance about orealLocal derviation,Indicate orealAbout wiLocal derviation；
Calculate the magnification index of i moment updated load distance
Wherein, ηwIndicate pace of learning.
Further, the weight coefficient for updating each time point based on variance includes:
Calculate local derviation of the variance about m
Wherein,Indicate variance about orealLocal derviation,Indicate orealLocal derviation about m；
Calculate i moment updated weight coefficient m+:
Wherein, ηwIndicate pace of learning.
Further, the magnification index of the load distance based on updated various time points, updated each time point
Weight coefficient and electricity needs response system judgement schematics, to target power demand response event carry out evaluation include:
In the case where update terminates, by the magnification index of the load distance of updated various time points, updated each time
The weight coefficient of point brings the judgement schematics of electricity needs response system into, obtains updated judgement schematics；Obtain each time
The electric system actual load and power system objectives load of point target electricity needs response events；By target power demand response
The electric system actual load and power system objectives load of event bring updated judgement schematics into, and calculating reaches target power
The evaluation of estimate of demand response event.
According to the another aspect of the application, a kind of processing unit of electric power data is provided.The device includes: the first building
Unit, between the electric system actual load and power system objectives load by various time points load distance, it is each
The magnification index of the load distance at time point and the weight coefficient building electricity needs of various time points respond judgement schematics；The
Two construction units construct model for the parameter based on multiple history electricity needs response events, to update various time points
The magnification index of load distance and the weight coefficient of various time points, wherein parameter is responded including at least history electricity needs
The electric system actual load of event and the power system objectives load of history electricity needs response events；Updating unit is used for
The magnification index of load distance based on updated various time points, the weight coefficient and electric power at updated each time point
The judgement schematics of demand response system evaluate target power demand response event.
To achieve the goals above, according to the another aspect of the application, a kind of storage medium is provided, storage medium includes
The program of storage, wherein program executes the processing method of any one of the above electric power data.
To achieve the goals above, according to the another aspect of the application, a kind of processor is provided, processor is for running
Program, wherein program executes the processing method of any one of the above electric power data when running.
By the application, using following steps: by the electric system actual load and power system objectives of various time points
The magnification index of load distance, the load distance of various time points between load and the weight coefficient building of various time points
Electricity needs responds judgement schematics；Parameter based on multiple history electricity needs response events constructs model, when updating each
Between the magnification index of load distance put and the weight coefficient of various time points, wherein parameter is needed including at least history electric power
Ask the electric system actual load of response events and the power system objectives load of history electricity needs response events；Based on update
The magnification index of the load distance of various time points afterwards, the weight coefficient at updated each time point and electricity needs response
The judgement schematics of system evaluate target power demand response event, solve the stabilization in the related technology for power grid
Operation, which needs to respond electricity needs, to be evaluated, but every suit electricity needs response system is required to customization evaluation of programme, comments
The not high problem of the pervasive degree of valence scheme.Pass through the magnification index of the load distance to various time points, the weight system at each time point
Number is updated, and substitutes into the judgement schematics of the electricity needs response system of creation, is evaluated demand response, and then reach
More set electricity needs response systems can be applicable in a set of evaluation of programme, the general moderately high effect of evaluation of programme.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present application, the schematic reality of the application
Example and its explanation are applied for explaining the application, is not constituted an undue limitation on the present application.In the accompanying drawings:
Fig. 1 is the flow chart according to the processing method of electric power data provided by the embodiments of the present application；
Fig. 2 is the neural network model according to the processing method of electric power data provided by the embodiments of the present application；
Fig. 3 is the simulation result diagram according to the processing method of electric power data provided by the embodiments of the present application；And
Fig. 4 is the schematic diagram according to the processing unit of electric power data provided by the embodiments of the present application.
Specific embodiment
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
In order to make those skilled in the art more fully understand application scheme, below in conjunction in the embodiment of the present application
Attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
The embodiment of the application a part, instead of all the embodiments.Based on the embodiment in the application, ordinary skill people
Member's every other embodiment obtained without making creative work, all should belong to the model of the application protection
It encloses.
It should be noted that the description and claims of this application and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to embodiments herein described herein.In addition, term " includes " and " tool
Have " and their any deformation, it is intended that cover it is non-exclusive include, for example, containing a series of steps or units
Process, method, system, product or equipment those of are not necessarily limited to be clearly listed step or unit, but may include without clear
Other step or units listing to Chu or intrinsic for these process, methods, product or equipment.
According to an embodiment of the present application, a kind of processing method of electric power data is provided.
Fig. 1 is the flow chart according to the processing method of the electric power data of the embodiment of the present application.As shown in Figure 1, this method packet
Include following steps:
Step S101, by the load distance between the electric system actual load and power system objectives load of various time points
The weight coefficient building electricity needs response evaluation of magnification index and various time points with a distance from the load of, various time points
Formula.
Optionally, in the processing method of electric power data provided by the embodiments of the present application, by the power train of various time points
Unite load distance between actual load and power system objectives load, the load distance of various time points magnification index and
The weight coefficient building electricity needs response judgement schematics of various time points include: electric system actual negative described in the building i moment
Load distance between lotus and the power system objectives load
Wherein, piIndicate the electric system actual load at i moment,Indicate the power system objectives load at i moment；
According toThe magnification index of the load distance of various time points and various time points it is described
Weight coefficient determines target load distance
Wherein, wiIndicate that the weight coefficient at i moment, m indicate that the magnification index of the load distance at i moment, n indicate measurement
The number at time point；It should be noted that initial stage wiIt is set as 1, m and is set as 2.
Based on the target load distanceIt constructs electricity needs and responds judgement schematics s:
Step S102, the parameter based on multiple history electricity needs response events constructs model, to update various time points
The load distance magnification index and various time points the weight coefficient, wherein the parameter include at least institute
State the electric system of the electric system actual load and the history electricity needs response events of history electricity needs response events
Target load.
Specifically, by system manager, in conjunction with demand, the selection demand response thing that (generally ten times) have occurred several times
Part, the parameter based on these demand response events are modeled.
Optionally, in the processing method of electric power data provided by the embodiments of the present application, multiple history electricity needs are based on
The parameter of response events constructs model, to update the magnification index and various time points of the load distance of various time points
The weight coefficient include: based on the multiple history electricity needs response events building neural network model, and according to
The neural network model output history electricity needs responds evaluation of estimate；Calculate the history electricity needs response evaluation of estimate and phase
The variance of the electricity needs response evaluation of estimate of prestige；The amplification for updating the load distance of various time points based on the variance refers to
Number；The weight coefficient of various time points is updated based on the variance.
Optionally, in the processing method of electric power data provided by the embodiments of the present application, it is based on the multiple history electric power
The neural network model of demand response event building, and the response evaluation of history electricity needs is exported according to the neural network model
Value includes:
As shown in Fig. 2, building input layer, the history electricity needs response events described in the input layer input i moment
Electric system actual load piWith power system objectives load
Hidden layer is constructed, calculates median h in the hidden layeri:
Wherein, m indicates the magnification index of the load distance at i moment；
Output layer is constructed, the history electricity needs is calculated in the output layer and responds evaluation of estimate oreal:
Wherein, wiIndicate that the weight coefficient at i moment, n indicate the number at the time point measured；
Based on neural network model described in the input layer, the hidden layer and the output layer building；Based on the mind
The history electricity needs response evaluation of estimate o is exported through network modelreal。
Optionally, in the processing method of electric power data provided by the embodiments of the present application, the history electricity needs is calculated
Response evaluation of estimate and the variance of desired electricity needs response evaluation of estimate include:
Wherein, E indicates the variance of the history electricity needs response evaluation of estimate and desired electricity needs response evaluation of estimate,
otarIndicate the desired electricity needs response evaluation of estimate.
Optionally, it in the processing method of electric power data provided by the embodiments of the present application, is updated based on the variance each
The magnification index of the load distance at time point includes:
The variance is calculated about wiLocal derviation
Wherein,Indicate the variance about orealLocal derviation,Indicate orealAbout wiLocal derviation；
Calculate the magnification index of the i moment updated load distance
Wherein, ηwIndicate pace of learning.
Optionally, in the processing method of electric power data provided by the embodiments of the present application, when updating each based on the variance
Between the weight coefficient put include:
Calculate local derviation of the variance about m
Wherein,Indicate the variance about orealLocal derviation,Indicate orealLocal derviation about m；
Calculate the i moment updated weight coefficient m+:
Wherein, ηwIndicate pace of learning.
It should be noted that parameter update terminates when variance yields specifies numerical value less than administrator；When variance yields is greater than management
When the specified numerical value of member, above-mentioned renewal process is repeated.
Step S103, the magnification index of the load distance based on updated various time points, it is updated each when
Between the weight coefficient put and the judgement schematics of the electricity needs response system, to target power demand response event into
Row evaluation.
Optionally, in the processing method of electric power data provided by the embodiments of the present application, it is based on updated each time
The evaluation of the magnification index of load distance of point, the weight coefficient at updated each time point and electricity needs response system is public
Formula, carrying out evaluation to target power demand response event includes: in the case where update terminates, by updated various time points
The magnification index of load distance, the weight coefficient of updated various time points bring into electricity needs response system evaluation it is public
Formula obtains updated judgement schematics；Obtain the electric system actual load of various time points target power demand response event
With power system objectives load；By the electric system actual load of target power demand response event and power system objectives load
It brings updated judgement schematics into, calculates the evaluation of estimate for reaching target power demand response event.
For example, Fig. 3 is the simulation result diagram of the processing method of electric power data provided by the embodiments of the present application.
This emulation takes the electric power data of Daxing District, the data of acquisition in every five minutes, due in neural network model
In, an event is usually no more than 4 hours, therefore the value of n is 48.Select 129 users as analysis object, these users
In, it experienced within 2017 3 efficiencies and promote event, in every event, all each user for participating in being promoted individually is evaluated,
3 event aggregate users are evaluated 267 times.In these evaluations, event is promoted as training sample using 9 times initial efficiencies and is obtained
Neural network model is computed, and obtains the ascending weights vector of (Fig. 3 only marks at 24 points) at one 48 points, according to weight vectors by m
5.7 are adjusted to, the Generalization Capability of the weight after optimizing using this method, BP neural network is higher, and the prediction error of test set is more
It is low, evaluation result it is preparatory very high.This method can be generalized to the other large-scale demands with energy mechanism in smart grid garden and ring
In should implementing, is conducive to the landing application for pushing demand response, realizes the stability of power grid.
The processing method of electric power data provided by the embodiments of the present application passes through the electric system actual negative by various time points
The magnification index of load distance, the load distance of various time points between lotus and power system objectives load and each time
The weight coefficient building electricity needs of point responds judgement schematics；Parameter based on multiple history electricity needs response events constructs mould
Type, to update the magnification index of the load distance of various time points and the weight coefficient of various time points, wherein parameter is at least
The electric system mesh of electric system actual load and history electricity needs response events including history electricity needs response events
Mark load；The magnification index of load distance based on updated various time points, the weight coefficient at updated each time point
And the judgement schematics of electricity needs response system, target power demand response event is evaluated, solves the relevant technologies
In evaluated in order to which the stable operation of power grid needs to respond electricity needs, but every suit electricity needs response system is required to
Customize evaluation of programme, the not high problem of the pervasive degree of evaluation of programme.By the magnification index of the load distance to various time points, respectively
The weight coefficient at time point is updated, and substitutes into the judgement schematics of the electricity needs response system of creation, to demand response into
It goes and evaluates, and then reached more set electricity needs response systems to be applicable in a set of evaluation of programme, evaluation of programme is general moderately high
Effect.
It should be noted that step shown in the flowchart of the accompanying drawings can be in such as a group of computer-executable instructions
It is executed in computer system, although also, logical order is shown in flow charts, and it in some cases, can be with not
The sequence being same as herein executes shown or described step.
The embodiment of the present application also provides a kind of processing units of electric power data, it should be noted that the embodiment of the present application
The processing unit of electric power data can be used for executing the processing method that electric power data is used for provided by the embodiment of the present application.With
Under the processing unit of electric power data provided by the embodiments of the present application is introduced.
Fig. 4 is the schematic diagram according to the processing unit of the electric power data of the embodiment of the present application.As shown in figure 4, the device packet
It includes: the first construction unit 10, the second construction unit 20 and updating unit 30.
Specifically, the first construction unit 10, for the electric system actual load and electric system mesh by various time points
Mark load distance, the magnification index of the load distance of various time points and the weight coefficient structure of various time points between load
Build electricity needs response judgement schematics.
Second construction unit 20 constructs model for the parameter based on multiple history electricity needs response events, to update
The magnification index of the load distance of various time points and the weight coefficient of various time points, wherein parameter includes at least history
The electric system actual load of electricity needs response events and the power system objectives load of history electricity needs response events.
Updating unit 30, the magnification index, updated each for the load distance based on updated various time points
The weight coefficient at time point and the judgement schematics of electricity needs response system, comment target power demand response event
Valence.
Optionally, in the processing unit of electric power data provided by the embodiments of the present application, the first construction unit includes: first
Module is constructed, for constructing the load distance between i moment electric system actual load and power system objectives load
Wherein, piIndicate the electric system actual load at i moment,Indicate the power system objectives load at i moment；
First determining module is used for basisThe magnification index of the load distance of various time points and each
The weight coefficient at time point determines target load distance
Wherein, wiIndicate that the weight coefficient at i moment, m indicate that the magnification index of the load distance at i moment, n indicate measurement
The number at time point；
Second building module, for being based on target load distanceIt constructs electricity needs and responds judgement schematics s:
Optionally, in the processing unit of electric power data provided by the embodiments of the present application, the second construction unit includes: third
Building module, the neural network model for being constructed based on multiple history electricity needs response events, and according to neural network mould
Type exports history electricity needs and responds evaluation of estimate；First computing module, for calculating history electricity needs response evaluation of estimate and phase
The variance of the electricity needs response evaluation of estimate of prestige；First update module, for updating the load distance of various time points based on variance
From magnification index；Second update module, for updating the weight coefficient of various time points based on variance.
Optionally, in the processing unit of electric power data provided by the embodiments of the present application, it includes: first that third, which constructs module,
Submodule is constructed, it is practical in the electric system of input layer input i moment history electricity needs response events for constructing input layer
Load piWith power system objectives loadSecond building submodule calculates median in hidden layer for constructing hidden layer
hi:
Wherein, m indicates the magnification index of the load distance at i moment；
Third constructs submodule, for constructing output layer, calculates history electricity needs in output layer and responds evaluation of estimate oreal:
Wherein, wiIndicate that the weight coefficient at i moment, n indicate the number at the time point measured；
4th building submodule, for based on input layer, hidden layer and output layer building neural network model；Export submodule
Block, for responding evaluation of estimate o based on neural network model output history electricity needsreal。
Optionally, in the processing unit of electric power data provided by the embodiments of the present application, the first computing module includes:
Wherein, E indicates the variance of history electricity needs response evaluation of estimate and desired electricity needs response evaluation of estimate, otar
Indicate desired electricity needs response evaluation of estimate.
Optionally, in the processing unit of electric power data provided by the embodiments of the present application, the first update module includes: first
Computational submodule, for calculating variance about wiLocal derviation
Wherein,Indicate variance about orealLocal derviation,Indicate orealAbout wiLocal derviation；
Second computational submodule, for calculating the magnification index of i moment updated load distance
Wherein, ηwIndicate pace of learning.
Optionally, in the processing unit of electric power data provided by the embodiments of the present application, the second update module includes: third
Computational submodule, for calculating local derviation of the variance about m
Wherein,Indicate variance about orealLocal derviation,Indicate orealLocal derviation about m；
4th computational submodule, for calculating i moment updated weight coefficient m+:
Wherein, ηwIndicate pace of learning.
Optionally, in the processing unit of electric power data provided by the embodiments of the present application, updating unit includes: substitution mould
Block, in the case where update terminates, by the magnification index of the load distance of updated various time points, updated each
The weight coefficient at a time point brings the judgement schematics of electricity needs response system into, obtains updated judgement schematics；Obtain mould
Block, the electric system actual load and power system objectives for obtaining various time points target power demand response event are negative
Lotus；Second computing module, for bearing the electric system actual load of target power demand response event with power system objectives
Lotus brings updated judgement schematics into, calculates the evaluation of estimate for reaching target power demand response event.
The processing unit of electric power data provided by the embodiments of the present application, by the first construction unit 10 by various time points
The amplification of load distance, the load distance of various time points between electric system actual load and power system objectives load refers to
Several and various time points weight coefficient building electricity needs respond judgement schematics；Second construction unit 20 is based on multiple history
The parameters of electricity needs response events constructs model, with update the load distance of various time points magnification index and it is each when
Between the weight coefficient put, wherein parameter includes at least the electric system actual load and history of history electricity needs response events
The power system objectives load of electricity needs response events；Load distance of the updating unit 30 based on updated various time points
Magnification index, the weight coefficient at updated each time point and electricity needs response system judgement schematics, to target electricity
Power demand response event is evaluated, solve in the related technology in order to the stable operation of power grid need to respond electricity needs into
Row evaluation, but every suit electricity needs response system is required to customization evaluation of programme, and the not high problem of the pervasive degree of evaluation of programme is led to
Cross the magnification index to the load distance of various time points, the weight coefficient at each time point is updated, and substitutes into the electricity of creation
The judgement schematics of power demand response system, evaluate demand response, and then having reached more set electricity needs response systems can
To be applicable in a set of evaluation of programme, the general moderately high effect of evaluation of programme.
The processing unit of the electric power data includes processor and memory, and above-mentioned first construction unit 10, second constructs
Unit 20 and updating unit 30 etc. store in memory as program unit, are executed by processor stored in memory
Above procedure unit realizes corresponding function.
Include kernel in processor, is gone in memory to transfer corresponding program unit by kernel.Kernel can be set one
Or more, by adjusting kernel parameter come solve in the related technology in order to the stable operation of power grid need to respond electricity needs into
Row evaluation, but every suit electricity needs response system is required to customization evaluation of programme, the not high problem of the pervasive degree of evaluation of programme.
Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/
Or the forms such as Nonvolatile memory, if read-only memory (ROM) or flash memory (flash RAM), memory include that at least one is deposited
Store up chip.
The embodiment of the invention provides a kind of storage mediums, are stored thereon with program, real when which is executed by processor
The processing method of the existing electric power data.
The embodiment of the invention provides a kind of processor, the processor is for running program, wherein described program operation
The processing method of electric power data described in Shi Zhihang.
The embodiment of the invention provides a kind of equipment, equipment include processor, memory and storage on a memory and can
The program run on a processor, processor realize following steps by the electric system actual negative of various time points when executing program
The magnification index of load distance, the load distance of various time points between lotus and power system objectives load and each time
The weight coefficient building electricity needs of point responds judgement schematics；Parameter based on multiple history electricity needs response events constructs mould
Type, to update the magnification index of the load distance of various time points and the weight coefficient of various time points, wherein parameter is at least
The electric system mesh of electric system actual load and history electricity needs response events including history electricity needs response events
Mark load；The magnification index of load distance based on updated various time points, the weight coefficient at updated each time point
And the judgement schematics of electricity needs response system, target power demand response event is evaluated.
By the load distance between the electric system actual load and power system objectives load of various time points, Ge Geshi
Between the magnification index of load distance put and the weight coefficient of various time points building electricity needs response judgement schematics include:
Construct the load distance between i moment electric system actual load and power system objectives load
Wherein, piIndicate the electric system actual load at i moment,Indicate the power system objectives load at i moment；
According toThe magnification index of the load distance of various time points and the weight coefficient of various time points
Determine target load distance
Wherein, wiIndicate that the weight coefficient at i moment, m indicate that the magnification index of the load distance at i moment, n indicate measurement
The number at time point；
Based on target load distanceIt constructs electricity needs and responds judgement schematics s:
Parameter based on multiple history electricity needs response events constructs model, to update the load distance of various time points
Magnification index and various time points weight coefficient include: based on multiple history electricity needs response events building nerve
Network model, and history electricity needs is exported according to neural network model and responds evaluation of estimate；The response of history electricity needs is calculated to comment
The variance of value and desired electricity needs response evaluation of estimate；The amplification for updating the load distance of various time points based on variance refers to
Number；The weight coefficient of various time points is updated based on variance.
Based on the neural network model of multiple history electricity needs response events building, and exported according to neural network model
History electricity needs responds evaluation of estimate
Input layer is constructed, in the electric system actual load p of input layer input i moment history electricity needs response eventsi
With power system objectives load
Hidden layer is constructed, calculates median h in hidden layeri:
Wherein, m indicates the magnification index of the load distance at i moment；
Output layer is constructed, history electricity needs is calculated in output layer and responds evaluation of estimate oreal:
Wherein, wiIndicate that the weight coefficient at i moment, n indicate the number at the time point measured；
Based on input layer, hidden layer and output layer building neural network model；Based on neural network model output history electricity
Power demand response evaluation of estimate oreal。
It calculates history electricity needs response evaluation of estimate and the variance of desired electricity needs response evaluation of estimate includes:
Wherein, E indicates the variance of history electricity needs response evaluation of estimate and desired electricity needs response evaluation of estimate, otar
Indicate desired electricity needs response evaluation of estimate.
The magnification index of load distance for updating various time points based on variance includes:
Variance is calculated about wiLocal derviation
Wherein,Indicate variance about orealLocal derviation,Indicate orealAbout wiLocal derviation；
Calculate the magnification index of i moment updated load distance
Wherein, ηwIndicate pace of learning.
The weight coefficient for updating each time point based on variance includes:
Calculate local derviation of the variance about m
Wherein,Indicate variance about orealLocal derviation,Indicate orealLocal derviation about m；
Calculate i moment updated weight coefficient m+:
Wherein, ηwIndicate pace of learning.
The magnification index of load distance based on updated various time points, the weight coefficient at updated each time point
And the judgement schematics of electricity needs response system, carrying out evaluation to target power demand response event includes: to terminate in update
In the case where, by the magnification index of the load distance of updated various time points, the weight system of updated various time points
Number brings the judgement schematics of electricity needs response system into, obtains updated judgement schematics；Obtain various time points target power
The electric system actual load and power system objectives load of demand response event；By the electric power of target power demand response event
System actual load and power system objectives load bring updated judgement schematics into, and calculating reaches target power demand response thing
The evaluation of estimate of part.Equipment herein can be server, PC, PAD, mobile phone etc..
Present invention also provides a kind of computer program products, when executing on data processing equipment, are adapted for carrying out just
The program of beginningization there are as below methods step: by between the electric system actual load and power system objectives load of various time points
Load distance, various time points load distance magnification index and various time points weight coefficient construct electricity needs
Respond judgement schematics；Parameter based on multiple history electricity needs response events constructs model, to update the negative of various time points
The magnification index of lotus distance and the weight coefficient of various time points, wherein parameter includes at least history electricity needs and responds thing
The electric system actual load of part and the power system objectives load of history electricity needs response events；Based on updated each
The magnification index of the load distance at time point, the weight coefficient at updated each time point and electricity needs response system are commented
Valence formula evaluates target power demand response event.
By the load distance between the electric system actual load and power system objectives load of various time points, Ge Geshi
Between the magnification index of load distance put and the weight coefficient of various time points building electricity needs response judgement schematics include:
Construct the load distance between i moment electric system actual load and power system objectives load
Wherein, piIndicate the electric system actual load at i moment,Indicate the power system objectives load at i moment；
According toThe magnification index of the load distance of various time points and the weight coefficient of various time points
Determine target load distance
Wherein, wiIndicate that the weight coefficient at i moment, m indicate that the magnification index of the load distance at i moment, n indicate measurement
The number at time point；
Based on target load distanceIt constructs electricity needs and responds judgement schematics s:
Parameter based on multiple history electricity needs response events constructs model, to update the load distance of various time points
Magnification index and various time points weight coefficient include: based on multiple history electricity needs response events building nerve
Network model, and history electricity needs is exported according to neural network model and responds evaluation of estimate；The response of history electricity needs is calculated to comment
The variance of value and desired electricity needs response evaluation of estimate；The amplification for updating the load distance of various time points based on variance refers to
Number；The weight coefficient of various time points is updated based on variance.
Based on the neural network model of multiple history electricity needs response events building, and exported according to neural network model
History electricity needs responds evaluation of estimate
Input layer is constructed, in the electric system actual load p of input layer input i moment history electricity needs response eventsi
With power system objectives load
Hidden layer is constructed, calculates median h in hidden layeri:
Wherein, m indicates the magnification index of the load distance at i moment；
Output layer is constructed, history electricity needs is calculated in output layer and responds evaluation of estimate oreal:
Wherein, wiIndicate that the weight coefficient at i moment, n indicate the number at the time point measured；
Based on input layer, hidden layer and output layer building neural network model；Based on neural network model output history electricity
Power demand response evaluation of estimate oreal。
It calculates history electricity needs response evaluation of estimate and the variance of desired electricity needs response evaluation of estimate includes:
Wherein, E indicates the variance of history electricity needs response evaluation of estimate and desired electricity needs response evaluation of estimate, otar
Indicate desired electricity needs response evaluation of estimate.
The magnification index of load distance for updating various time points based on variance includes:
Variance is calculated about wiLocal derviation
Wherein,Indicate variance about orealLocal derviation,Indicate orealAbout wiLocal derviation；
Calculate the magnification index of i moment updated load distance
Wherein, ηwIndicate pace of learning.
The weight coefficient for updating each time point based on variance includes:
Calculate local derviation of the variance about m
Wherein,Indicate variance about orealLocal derviation,Indicate orealLocal derviation about m；
Calculate i moment updated weight coefficient m+:
Wherein, ηwIndicate pace of learning.
The magnification index of load distance based on updated various time points, the weight coefficient at updated each time point
And the judgement schematics of electricity needs response system, carrying out evaluation to target power demand response event includes: to terminate in update
In the case where, by the magnification index of the load distance of updated various time points, the weight system of updated various time points
Number brings the judgement schematics of electricity needs response system into, obtains updated judgement schematics；Obtain various time points target power
The electric system actual load and power system objectives load of demand response event；By the electric power of target power demand response event
System actual load and power system objectives load bring updated judgement schematics into, and calculating reaches target power demand response thing
The evaluation of estimate of part.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
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 Jie
The example of matter.
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 should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
It include so that the process, method, commodity or the equipment that include a series of elements not only include those elements, but also to wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including element
There is also other identical elements in process, method, commodity or equipment.
It will be understood by those skilled in the art that embodiments herein can provide as method, system or computer program product.
Therefore, complete hardware embodiment, complete software embodiment or embodiment combining software and hardware aspects can be used in the application
Form.It is deposited moreover, the application can be used to can be used in the computer that one or more wherein includes computer usable program code
The shape for the computer program product implemented on storage media (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
Formula.
The above is only embodiments herein, are not intended to limit this application.To those skilled in the art,
Various changes and changes are possible in this application.It is all within the spirit and principles of the present application made by any modification, equivalent replacement,
Improve etc., it should be included within the scope of the claims of this application.
Claims (10)
1. a kind of processing method of electric power data characterized by comprising
By load distance, the various time points between the electric system actual load and power system objectives load of various time points
Load distance magnification index and various time points weight coefficient building electricity needs respond judgement schematics；
Parameter based on multiple history electricity needs response events constructs model, to update the load distance of various time points
Magnification index and various time points the weight coefficient, wherein the parameter include at least the history electricity needs
The power system objectives load of the electric system actual load of response events and the history electricity needs response events；
The magnification index of the load distance based on updated various time points, the weight at updated each time point
The judgement schematics of coefficient and the electricity needs response system evaluate target power demand response event.
2. the method according to claim 1, wherein by the electric system actual load and electric power of various time points
Load distance, the magnification index of the load distance of various time points and the weight of various time points between aims of systems load
Coefficient building electricity needs responds judgement schematics and includes:
Construct the load distance described in the i moment between electric system actual load and the power system objectives load
Wherein, piIndicate the electric system actual load at i moment,Indicate the power system objectives load at i moment；
According toThe magnification index of the load distance of various time points and the weight system of various time points
Number determines target load distance
Wherein, wiIndicate that the weight coefficient at i moment, m indicate that the magnification index of the load distance at i moment, n indicate the time measured
The number of point；
Based on the target load distanceIt constructs electricity needs and responds judgement schematics s:
3. the method according to claim 1, wherein the parameter structure based on multiple history electricity needs response events
Established model, to update the magnification index of the load distance of various time points and the weight coefficient packet of various time points
It includes:
Based on the neural network model of the multiple history electricity needs response events building, and according to the neural network model
It exports history electricity needs and responds evaluation of estimate；
Calculate the variance of the history electricity needs response evaluation of estimate and desired electricity needs response evaluation of estimate；
The magnification index of the load distance of various time points is updated based on the variance；
The weight coefficient of various time points is updated based on the variance.
4. according to the method described in claim 3, it is characterized in that, being constructed based on the multiple history electricity needs response events
Neural network model, and according to the neural network model export history electricity needs response evaluation of estimate include:
Input layer is constructed, the electric system actual negative of the history electricity needs response events described in the input layer input i moment
Lotus piWith power system objectives load
Hidden layer is constructed, calculates median h in the hidden layeri:
Wherein, m indicates the magnification index of the load distance at i moment；
Output layer is constructed, the history electricity needs is calculated in the output layer and responds evaluation of estimate oreal:
Wherein, wiIndicate that the weight coefficient at i moment, n indicate the number at the time point measured；
Based on neural network model described in the input layer, the hidden layer and the output layer building；
The history electricity needs, which is exported, based on the neural network model responds evaluation of estimate oreal。
5. according to the method described in claim 4, it is characterized in that, calculating the history electricity needs response evaluation of estimate and expectation
Electricity needs response evaluation of estimate variance include:
Wherein, E indicates the variance of the history electricity needs response evaluation of estimate and desired electricity needs response evaluation of estimate, otar
Indicate the desired electricity needs response evaluation of estimate.
6. according to the method described in claim 5, it is characterized in that, updating the load of various time points based on the variance
The magnification index of distance includes:
The variance is calculated about wiLocal derviation
Wherein,Indicate the variance about orealLocal derviation,Indicate orealAbout wiLocal derviation；
Calculate the magnification index of the i moment updated load distance
Wherein, ηwIndicate pace of learning.
7. according to the method described in claim 5, it is characterized in that, updating the weight system at each time point based on the variance
Number includes:
Calculate local derviation of the variance about m
Wherein,Indicate the variance about orealLocal derviation,Indicate orealLocal derviation about m；
Calculate the i moment updated weight coefficient m+:
Wherein, ηwIndicate pace of learning.
8. the method according to claim 1, wherein the load distance based on updated various time points
Magnification index, the weight coefficient at updated each time point and the electricity needs response system judgement schematics,
Carrying out evaluation to target power demand response event includes:
In the case where update terminates, by the magnification index of the load distance of updated various time points, updated
The weight coefficient of various time points brings the judgement schematics of the electricity needs response system into, and it is public to obtain updated evaluation
Formula；
Electric system actual load and the power system objectives for obtaining target power demand response event described in various time points are negative
Lotus；
By the electric system actual load of the target power demand response event and power system objectives load bring into it is described more
Judgement schematics after new calculate the evaluation of estimate for reaching target power demand response event.
9. a kind of processing unit of electric power data characterized by comprising
First construction unit, for negative between the electric system actual load and power system objectives load by various time points
The weight coefficient building electricity needs response of lotus distance, the magnification index of the load distance of various time points and various time points
Judgement schematics；
Second construction unit constructs model for the parameter based on multiple history electricity needs response events, when updating each
Between the magnification index of the load distance put and the weight coefficient of various time points, wherein the parameter is at least wrapped
Include the electric system actual load of the history electricity needs response events and the electric power of the history electricity needs response events
Aims of systems load；
Analytical unit, for the load distance based on updated various time points magnification index, it is updated each when
Between the weight coefficient put and the judgement schematics of the electricity needs response system, to target power demand response event into
Row evaluation.
10. a kind of processor, which is characterized in that the processor is for running program, wherein right of execution when described program is run
Benefit require any one of 1 to 8 described in electric power data processing method.
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Application publication date: 20190920 |