CN108491893A - A kind of household electricity load classification recognition methods based on deep layer forest algorithm - Google Patents
A kind of household electricity load classification recognition methods based on deep layer forest algorithm Download PDFInfo
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Abstract
The household electricity load classification recognition methods based on deep layer forest algorithm that the invention discloses a kind of, it includes：Acquire the performance data of different home power load；Carry out the pretreatment of data and the calculating of important key parameters；The training dataset of deep layer forest algorithm is established using data in database and parameter, the training dataset includes set of data samples and test set；The parameter of deep layer forest algorithm is set, determines the generation algorithm of forest, decision tree number；It is trained using data sample set pair algorithm, forms the deep layer forest model for having and successively strengthening screening；The accuracy of the classification of deep layer forest model is tested using test set, determines the number of plies of model；After obtaining the deep layer forest model of training completion, the observation Value Data for the family's load for needing newly to access, the classification results of final output load type are inputted into model；Realize the processing to domestic consumer's electricity consumption data of magnanimity and Classification and Identification.
Description
Technical field
The invention belongs to household electricity load classifications to identify field, more particularly to a kind of family based on deep layer forest algorithm
Front yard power load classifying identification method.
Background technology
In face of power grid, presently, there are peak loads constantly to refresh historical high, and the present situation that peak-valley difference further widens is carried out
Demand-side electric energy management is the effective way solved the problems, such as.However residential households electricity consumption at present participate in the ratio of Load Regulation also compared with
It is small.One reason for this is that accurately the power load of domestic consumer can not be accurately identified at present, and for excavating not
The adjustable potentiality of same family.In order to cope with such a situation, intelligent classification identification is carried out to household electricity load, is new shape
An effective measure of demand Side Management is pushed under state.
Novel intelligent home gradually starts to come into huge numbers of families, and the household electricity information for obtaining magnanimity has no longer been a difficulty
Topic.But it is continuously increased with the classification of domestic electric appliances, it is unpractiaca artificially to carry out Classification and Identification to household electricity information.Cause
This, in face of the processing and classification of domestic consumer's electricity consumption data of magnanimity, electric power enterprise is badly in need of finding a method rapidly and efficiently
To classify to power consumer electricity consumption classification.
Invention content
The technical problem to be solved by the present invention is to：A kind of household electricity load classification knowledge based on deep layer forest algorithm is provided
Other method, to realize the processing to domestic consumer's electricity consumption data of magnanimity and Classification and Identification, to instruct electric power enterprise to be needed
It asks side pipe to manage, improves the regulation and control efficiency of flexible load.
The technical scheme is that：
A kind of household electricity load classification recognition methods based on deep layer forest algorithm, it includes：
Step S110, the performance data of different home power load is acquired；
Step S120, magnanimity household electricity load data is stored in server end, be carried out at the same time data pretreatment and
The calculating of important key parameters；
Step S130, the training dataset of deep layer forest algorithm, the training are established using data in database and parameter
Data set includes set of data samples and test set；
Step S140, the parameter of deep layer forest algorithm is set, determine the generation algorithm of forest, decision tree number；
Step S150, it is trained using data sample set pair algorithm, forms the deep layer forest for having and successively strengthening screening
Model；
Step S160, the accuracy of the classification of deep layer forest model is tested using test set, determines the layer of model
Number；
Step S180, after the deep layer forest model for obtaining training completion, it is negative that the family for needing newly to access is inputted into model
The observation Value Data of lotus, the classification results of final output load type.
The performance data of the household electricity load includes the voltage, electric current and harmonic data of all kinds of domestic electric appliances.
The important key parameters are：The Working mould of the power factor of electrical appliance, the power bound of electrical appliance, electrical appliance
The multiple of formula, electrical appliance starting current and rated current.
The parameter of the deep layer forest algorithm includes：It is the generating mode of forest, the type of forest, the number of forest, each
Decision tree number in forest.
There is described formed the method for deep layer forest model for successively strengthening screening to be:Using each layer of output result as
Enhancing vector, the forest being further processed into next layer reinforce screening dynamics, successively strengthen, improve the precision of classification.
The method of the number of plies of the determining model is：If the accuracy of classification reaches setting numerical value, or increases the number of plies to mould
Setting numerical value is not achieved in the accuracy promotion of type classification, then current layer number is the number of plies of model.
It further includes：
Certain time interval is arranged, using the magnanimity household electricity data being continuously generated, to deep layer forest in step S170
Model is updated；Specifically update method is：It is combined using new magnanimity household electricity data and historical data base, structure is new
Training sample set, return to step S140, update can be used for the deep layer forest model of household electricity load classification identification, improve point
Class precision.
Advantageous effect of the present invention：
The present invention is based on the household electricity load classification recognition methods of deep layer forest algorithm, this method is gloomy by using deep layer
Woods algorithm analyzes household electricity load with electrical characteristics and part throttle characteristics, and then can be negative to the household electricity newly accessed
Lotus carries out Classification and Identification.The performance data of different home power load is carried out by home intelligent acquisition terminal first intelligent
Acquisition.Secondly mass data storage is carried out at the same time data prediction, is built again using wide area information server in server end
Vertical training dataset, and deep layer forest algorithm model is trained.The deep layer forest algorithm finally completed using training is to family
Front yard power load carries out intelligent classification identification.In addition, used deep layer forest algorithm is a kind of point theoretical with deep learning
Class algorithm, the algorithm have the advantages that deep layer characteristic features are excavated, can voluntarily determine that the screening number of plies, can be to having to reduce calculation amount
Effect carries out intelligent recognition to family's Overload Class, and acquired results can serve the various aspects such as grid DSM, electricity market.
To be conducive to improve the economic benefit of electric power enterprise.
Advantages of the present invention：
(1) the household electricity load classification recognition methods based on deep layer forest algorithm that the present invention designs, it is contemplated that magnanimity
The relationship between the different attribute of each electrical appliance is held in data acquisition and processing (DAP) using machine learning and deep learning theory.
(2) the household electricity load classification recognition methods based on deep layer forest algorithm that the present invention designs, it is gloomy using deep layer
Woods algorithm, this is a kind of newer machine learning data mining algorithm, have training speed it is fast, can with parallel training and excavate,
The advantages that accuracy of identification is high.
(3) the household electricity load classification recognition methods based on deep layer forest algorithm that the present invention designs, can be every one
The section time is updated disaggregated model, can be all to household electricity load into Mobile state online recognition.
The present invention realizes processing and Classification and Identification to domestic consumer's electricity consumption data of magnanimity, to instruct electric power enterprise into
Row demand side management improves the regulation and control efficiency of flexible load.
Description of the drawings
Fig. 1 is flow diagram of the present invention.
Specific implementation mode
A kind of household electricity load classification recognition methods based on deep layer forest algorithm of the present invention.It is mainly home-use with magnanimity
Based on electric appliance load data, intelligent excavating is carried out to family's part throttle characteristics using deep layer forest algorithm.It is calculated based on deep layer forest
The power consumer electricity consumption classifying identification method of method includes the following steps：
Step S110 carries out intelligence to the performance data of different home power load by home intelligent acquisition terminal and adopts
Collection.The concrete function of above-mentioned home intelligent acquisition terminal predominantly record the domestic electric appliances being currently accessed real-time voltage,
Electric current, active power export and the data such as voltage, current harmonic content.The function module that the terminal should be uploaded with information,
Data are uploaded to server end in real time and are stored to household electricity load database.
Magnanimity household electricity load data is stored in server end by step S120, be carried out at the same time data pretreatment and
The calculating of important key parameters.The normalized of numeric data is predominantly rejected in pretreatment, eliminates the dimension shadow between index
It rings.It is described in detail below：
Wherein xminFor the maximum value of sample data, xmaxFor the minimum value of sample data, x*To return to sample data
One result changed；X is sample data.
Important key parameters mainly include power factor (PF), power bound, loadtype, electrical appliance the cycle of operation,
The multiple of inrush current and normal operating current.Individual Parameters Calculation methods are as follows：
Wherein P is active power, and Q is reactive power,For power factor (PF).
Pass throughState can determine the loadtype of the load.
Wherein I ' is inrush current value when electrical appliance accesses, that is, starts the current maxima of moment.I0For electricity consumption
Electric current when device works normally, γ are that starting current impacts multiple.
For the categorical data of electrical appliance, artificial settings numeric data is needed to correspond therewith so that computer can
It is identified.
Step S130 establishes the training dataset of deep layer forest algorithm using data in database and important key parameters.
Using the corresponding attribute data of each power load as the output of model, the output of the classification of household electricity load as model,
Establish the set of data samples and test set of training.Training data sample set and test set respectively account for the 80% and 20% of sample total.
Step S140 needs the parameter that deep layer forest algorithm is arranged according to user, determines the generation algorithm of forest, decision tree
Number etc..The parameter of deep layer forest algorithm includes：In the generating mode of forest, the type of forest, the number of forest, each forest
Decision tree number.Design parameter is as follows：
Algorithm types：CART algorithms, C4.5 algorithms a variety of are used in mixed way
The classification of forest：Completely random tree forest, random forest；
The number of forest：Even number generally is taken, it is preferable using 2/4/8 classifying quality.
Decision tree number in each forest：Acquiescence 500, ranging from (0,1000]；
Step S150 is trained using training sample set pair deep layer forest algorithm, is formed to have and is successively strengthened screening
Deep layer forest model.It is described in detail below：
The deep layer forest model completed to training trains input vector, and each layer of forest set, which will generate, enhances vectorial conduct
Next layer of input.It successively screens, effective information is successively transmitted and constantly accumulated, until obtaining final classification recognition result.
Step S160 tests the accuracy of the classification of deep layer forest model using test set, determines the layer of model
Number.If the accuracy of classification reaches certain numerical value, or increases the number of plies and promote less the accuracy of category of model, then stop increasing
The number of plies of deep layer forest.
Certain time interval is arranged, using the magnanimity household electricity data being continuously generated, to deep layer forest in step S170
Model is updated.It is described in detail below：
It is combined using new magnanimity household electricity data and historical data base, builds new training sample set, return to step
Rapid S140, update can be used for the deep layer forest model of household electricity load classification identification, improve nicety of grading.
After obtaining the deep layer forest model of training completion, it is negative that the family for needing newly to access is inputted into model by step S180
The observation Value Data of lotus, the classification results of final output load type.
Claims (7)
1. a kind of household electricity load classification recognition methods based on deep layer forest algorithm, it includes：
Step S110, the performance data of different home power load is acquired；
Step S120, magnanimity household electricity load data is stored in server end, is carried out at the same time the pretreatment of data and important
The calculating of key parameters；
Step S130, the training dataset of deep layer forest algorithm, the training data are established using data in database and parameter
Collection includes set of data samples and test set；
Step S140, the parameter of deep layer forest algorithm is set, determine the generation algorithm of forest, decision tree number；
Step S150, it is trained using data sample set pair algorithm, forms the deep layer forest model for having and successively strengthening screening；
Step S160, the accuracy of the classification of deep layer forest model is tested using test set, determines the number of plies of model；
Step S180, after the deep layer forest model for obtaining training completion, the family's load for needing newly to access is inputted into model
Observe Value Data, the classification results of final output load type.
2. a kind of household electricity load classification recognition methods based on deep layer forest algorithm according to claim 1, special
Sign is that the performance data of the household electricity load includes the voltage, electric current and harmonic data of all kinds of domestic electric appliances.
3. a kind of household electricity load classification recognition methods based on deep layer forest algorithm according to claim 1, special
Sign is that the important key parameters are：The Working mould of the power factor of electrical appliance, the power bound of electrical appliance, electrical appliance
The multiple of formula, electrical appliance starting current and rated current.
4. the household electricity load classification recognition methods according to claim 1 based on deep layer forest algorithm, feature exist
In the parameter of the deep layer forest algorithm includes：In the generating mode of forest, the type of forest, the number of forest, each forest
Decision tree number.
5. the household electricity load classification recognition methods according to claim 1 based on deep layer forest algorithm, feature exist
In the method for the formation with the deep layer forest model for successively strengthening screening is:Using each layer of output result as enhancing
Vector, processing reinforce screening dynamics into next layer of forest, successively strengthen, improve the precision of classification.
6. the household electricity load classification recognition methods according to claim 1 based on deep layer forest algorithm, feature exist
In the method for the number of plies of the determining model is：If the accuracy of classification reaches setting numerical value, or increases the number of plies to category of model
Accuracy promotion setting numerical value is not achieved, then current layer number be model the number of plies.
7. the household electricity load classification recognition methods according to claim 1 based on deep layer forest algorithm, feature exist
In：It further includes：
Certain time interval is arranged, using the magnanimity household electricity data being continuously generated, to deep layer forest model in step S170
It is updated；Specifically update method is：It is combined using new magnanimity household electricity data and historical data base, builds new instruction
Practice sample set, return to step S140, update can be used for the deep layer forest model of household electricity load classification identification, improve classification essence
Degree.
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