CN108920790A - A kind of public building electricity consumption pattern recognition model method for building up based on historical data - Google Patents
A kind of public building electricity consumption pattern recognition model method for building up based on historical data Download PDFInfo
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Abstract
The invention discloses a kind of public building electricity consumption pattern recognition model method for building up based on historical data, includes the following steps：S1, missing data in history electricity consumption data library and the data beyond threshold range are substituted with corresponding field average value, and feature vector [x is established according to user's history hour maximum power consumption load x and user's history hourly average power load y, y], each user one feature vector [x, y] of each hour correspondence；S2 carries out K-means clustering to all feature vectors [x, y], finds the corresponding cluster centre of each feature vector, and each cluster centre corresponding one is used power mode；S3 finds each optimal Split Attribute and optimal Split Attribute value with power mode and other power modes, establishes electricity consumption pattern recognition model according to the decision tree based on CART algorithm.
Description
Technical field
The present invention relates to a kind of public building electricity consumption pattern recognition model method for building up based on historical data.
Background technique
Under economic globalization, the overall background of global warming, the Energy situation got worse has obtained height all over the world
Degree is paid attention to.China is maximum developing country in the world, between the electricity consumption and resource relative shortage of sharp increase in recent years
Contradiction seriously constrains the development of country.The energy is that the motive power of socio-economic development mentions to push the progress of power-saving technology
The utilization efficiency of high-energy source promotes energy conservation and optimization energy structure, construction " resource-conserving, environmentally friendly " amphitypy society
Meeting, municipal government of province national and at different levels have put into effect multinomial energy saving policy, regulation in succession, and it is long to gradually form energy conservation with Chinese characteristics
Effect mechanism and management system.
It monitors the production and service condition of the energy such as water, electricity, oil, the air and heat of enterprise on-line, accurately holds same industry list
The average electricity consumption level of position product, be governmental science formulate the important evidences of industry energy-saving emission reduction targets.By to enterprise and
The energy units such as utilities building carry out electricity consumption monitoring and energy efficiency evaluation, and energy unit can both have been helped to find out itself energy consumption aspect
Notch, allow its in energy-saving and emission-reduction shoot the arrow at the target, not in order to reach country energy conservation object and ground power cuts to limit consumption, and be government
Administrative department appraises and decides energy-saving benefit and provides scientific basis.
The Evaluation on Energy Saving of industrial user and public building is a complicated system engineering, is related to national energy-saving correlation method
Rule, regulation, industry energy-saving design standard, with energy technique, product, equipment choosing, the various aspects such as suitability of power-saving technology.It removes
Except a series of engineering problems, technical problem, the problems such as also including administrative decision.Evaluation on Energy Saving and examination in each system
In the process, it needs to be analyzed using the appraisal procedure of science.Existing main appraisal procedure includes criterion keying method, analogy work
Journey analytic approach, expertise determining method and unit area index method etc..The above method is mostly subjective, is more focused on simultaneously
It is analyzed from the angle of energy consumption system planning construction, is associated with less with its practical operation situation.The Evaluation on Energy Saving applied at present
Platform acquires all in data and counts greatly the primary stage shown, for the research work for carrying out mining algorithm with energy information in real time
Make to carry out few.
As the construction of China's Evaluation on Energy Saving platform is promoted, it will collect a large amount of electricity consumption data, how fast and effeciently
Therefrom analysis mining goes out valuable information, establishes the model of electricity consumption pattern-recognition, and then find the energy-saving potential of energy unit,
It proposes effective Saving energy, for improving energy resources utilization efficiency, ensures that the sustainable development of society is of great significance.
As the arrival of big data era and the construction of China's Evaluation on Energy Saving platform are promoted, it will there is the electricity consumption number of magnanimity
According to facilitating the model for establishing electricity consumption pattern-recognition using data mining technology, pointedly propose energy-saving scheme.Deeply open
The monitoring of exhibition high energy-consuming enterprises's electric energy efficiency and evaluation work, one side administrative department can improve electric energy management system, sufficiently dig
Energy-saving potential is dug, targeted strategy of Saving Energy and measure is taken, the energy-saving and emission-reduction of enterprise is pushed to work.Another aspect enterprise
Itself existing electrical problem can be understood in depth, implement specific energy conservation measure for these problems, further imitated according to cost
Benefit analysis, that makes future uses energy conduct programming, to reach enterprise and social two-win target.
Summary of the invention
The purpose of the invention is to overcome the deficiencies of the prior art and provide a kind of public building use based on historical data
Power mode identification model method for building up carries out clustering with power mode to user, to being divided with power mode for user
Class determines different with power mode and other attributes and attribute value for being distinguished with power mode, the foundation identification of power mode
Model provides conveniently for the subsequent multiplexing electric abnormality to user into monitoring.
Realizing a kind of technical solution of above-mentioned purpose is：A kind of public building electricity consumption pattern-recognition mould based on historical data
Type method for building up, includes the following steps：
S1, to the missing data in history electricity consumption data library and the data beyond threshold range with corresponding field average value into
Row substitution, and feature vector is established according to user's history hour maximum power consumption load x and user's history hourly average power load y
[x, y], each user one feature vector [x, y] of each hour correspondence；
S2 carries out K-means clustering to all feature vectors [x, y], finds the corresponding cluster of each feature vector
Center, each cluster centre corresponding one is used power mode；
S3 finds each optimal division with power mode and other power modes according to the decision tree based on CART algorithm
Attribute and optimal Split Attribute value, establish electricity consumption pattern recognition model.
Further, in S2 step, first standardize to all feature vectors [x, y], obtain standardization feature vector z
=[zx,zy], then to standardization feature vector z=[zx,zy] K-means clustering is carried out, the formula of standardization is：
With
μ (x) and μ (y) respectively represents any history acquisition time, and the user's history hour maximum power consumption of all users is negative
The average value of the user's history hourly average power load of the average value of lotus and all users, σ (x) and σ (y) respectively represent this and go through
History acquisition time, the standard deviation of the user's history hour maximum power consumption load of all users and the user's history of all users are small
The standard deviation of Shi Pingjun power load.
Further, to standardization feature vector z=[z in S2 stepx,zy] carry out the specific of K-means clustering
Step is：
S21, using user hour maximum power consumption load as abscissa, user's hourly average power load is the straight of ordinate
In angular coordinate system, k cluster centre u is picked upi(j), wherein j=0, i=1,2 ... ..., k；
S22 chooses n standardization feature vector, is denoted as zt, wherein t=1,2 ... ..., n, calculate each ztGather to each
Class center ui(j) Euclidean distance；
S23 is found out about each ztThe cluster centre u of minimum euclidean distancei(j), ztInto corresponding to the cluster centre
Cluster, and the geometric center of each cluster is denoted as new cluster centre ui(j+1)；
S24 calculates all ztAbout new cluster centre u corresponding with cluster where iti(j+1) square mistake of Euclidean distance
Poor Ej, determine square error EjWhether in range of set value；
S25, if square error EjIn range of set value, then current all cluster centre u are exportedi(j+1) and it is right
Answer all standardization feature vector z in clustert, each cluster centre uses power mode as one；
S26, if square error EjNot in range of set value, then j=j+1, and return step S23 are enabled.
Further, to by standardization feature vector z in S3 stept, wherein t=1,2 ... ..., n, the sample constituted
This collection S, using the smallest attribute of the value of Gain_GINI and attribute value as optimal Split Attribute and optimal Split Attribute value.
It is also further, for sample set S,
Wherein oiI-th of probability occurred with power mode in presentation class result；
Sample set S is divided into two parts according to user's history hour maximum power consumption load, Gain_GINI calculates as follows：
Optimal two offshoot program is
It is also further, for sample set S,
Wherein oiI-th of probability occurred with power mode in presentation class result；
Sample set S is divided into two parts according to user's history hourly average power load, Gain_GINI calculates as follows：
Optimal two offshoot program is
Using a kind of public building electricity consumption pattern recognition model method for building up based on historical data of the invention, including
The following steps：S1, the data corresponding field average value to the missing data in history electricity consumption data library and beyond threshold range
Substituted, and according to user's history hour maximum power consumption load x and user's history hourly average power load y establish feature to
It measures [x, y], each user one feature vector [x, y] of each hour correspondence；S2 carries out K- to all feature vectors [x, y]
Means clustering finds the corresponding cluster centre of each feature vector, and each cluster centre corresponding one is used power mode；S3,
According to the decision tree based on CART algorithm, each optimal Split Attribute with power mode and other power modes and optimal is found
Split Attribute value establishes electricity consumption pattern recognition model.Its technical effect is that：The model of building electricity consumption pattern-recognition is established, side
Just building electricity consumption is monitored in real time, usage mode is relatively simple, engineering in practice, have wide applicability.
Detailed description of the invention
Fig. 1 is a kind of process of public building electricity consumption pattern recognition model method for building up based on historical data of the invention
Figure.
Specific embodiment
Referring to Fig. 1, the present inventor in order to preferably understand technical solution of the present invention, is led to below
Specifically embodiment is crossed, and will be described in detail with reference to the accompanying drawings：
A kind of public building electricity consumption pattern recognition model method for building up based on historical data of the invention includes following step
Suddenly：
S1, data prediction step, including：
Wrong data processing step：
Missing data in history electricity consumption data library and the data beyond threshold range are carried out with corresponding field average value
Substitution.
Data normalization step：
Feature vector [x, y] is extracted from history electricity consumption data library, wherein it is negative that x represents user's history hour maximum power consumption
Lotus, y represent user's history hourly average power load.The corresponding feature vector of each hour generation one of each user [x,
y]。
Standardize to each feature vector, obtains standardization feature vector z=[zx,zy], the formula of standardization is：
With
Wherein z=[zx,zy] standardization feature vector is represented, μ (x) and μ (y) respectively represent any history acquisition time,
The average value of the user's history hour maximum power consumption load of all users and the user's history hourly average electricity consumption of all users are negative
The average value of lotus, σ (x) and σ (y) respectively represent history acquisition time, the user's history hour maximum power consumption of all users
The standard deviation of the standard deviation of load and the user's history hourly average power load of all users.
S2, electricity consumption data clustering step：
S21, using user hour maximum power consumption load as abscissa, user's hourly average power load is the straight of ordinate
In angular coordinate system, k cluster centre u is picked upi(j), wherein j=0, i=1,2 ... ..., k；
S22 chooses n standardization feature vector z=[zx,zy], it is denoted as zt, wherein t=1,2 ... ..., n, calculate each
ztTo each cluster centre ui(j) Euclidean distance；
S23 is found out about each ztThe cluster centre u of minimum euclidean distancei(j), ztInto corresponding to the cluster centre
Cluster, and the geometric center of each cluster is denoted as new cluster centre ui(j+1)；
S24 calculates all ztAbout new cluster centre u corresponding with cluster where iti(j+1) square mistake of Euclidean distance
Poor Ej, determine square error EjWhether in range of set value；
S25, if square error EjIn range of set value, then current all cluster centre u are exportedi(j+1) and it is right
Answer all standardization feature vector z in clustert, each cluster centre uses power mode as one；
S26, if square error EjNot in range of set value, then j=j+1, and return step S23 are enabled.
S3 establishes electricity consumption pattern recognition model：
According to each standardization feature vector ztAnd each standardization feature vector ztThe corresponding time is based on CART
The decision tree of algorithm is classified.Cart classification tree selects the smallest attribute of the value of Gain_GINI and attribute value as each use
Power mode carries out optimal Split Attribute and optimal Split Attribute value with power mode with other.The value of Gain_GINI is smaller, explanation
" degree of purity " of subsample is higher after two points, that is, illustrates to select the attribute value better as the effect of Split Attribute value.
For by standardization feature vector ztSample set S, the GINI calculating formula constituted is as follows：
oiI-th of probability occurred with power mode in presentation class result.
For the sample set S containing n sample, sample set S is divided into according to user's history hour maximum power consumption load
Two parts are then divided into after two parts, and Gain_GINI calculates as follows：
Or for the sample set S containing n sample, sample set S is divided according to user's history hourly average power load
It at two parts, is then divided into after two parts, Gain_GINI calculates as follows：
For sample set S, calculates optimal two offshoot program of all properties and choose wherein minimum value, as sample set S's
Optimal two offshoot program：
I.e.：Or
The as optimal Split Attribute of sample set S and optimal Split Attribute value.
A kind of public building electricity consumption pattern recognition model method for building up based on historical data of the invention is used based on history
Electric data carry out clustering with power mode to user, to being classified with power mode for user, determine and different use power mode
The attribute and attribute value distinguished with other with power mode provides conveniently for the subsequent multiplexing electric abnormality to user into monitoring.
A kind of public building electricity consumption pattern recognition model method for building up based on historical data of the invention has and has as follows
Beneficial effect：
The basis of building electricity consumption pattern-recognition is established, convenient to monitor in real time to building electricity consumption, usage mode is more
Simplicity, engineering in practice, have wide applicability.
Those of ordinary skill in the art it should be appreciated that more than embodiment be intended merely to illustrate the present invention,
And be not used as limitation of the invention, as long as the change in spirit of the invention, to embodiment described above
Change, modification will all be fallen within the scope of claims of the present invention.
Claims (6)
1. a kind of public building electricity consumption pattern recognition model method for building up based on historical data, includes the following steps：
S1 replaces the missing data in history electricity consumption data library and the data beyond threshold range with corresponding field average value
Generation, and according to user's history hour maximum power consumption load x and user's history hourly average power load y establish feature vector [x,
Y], each user one feature vector [x, y] of each hour correspondence；
S2 carries out K-means clustering to all feature vectors [x, y], finds the corresponding cluster centre of each feature vector,
Each cluster centre corresponding one is used power mode；
S3 finds each optimal Split Attribute with power mode and other power modes according to the decision tree based on CART algorithm
And optimal Split Attribute value, establish electricity consumption pattern recognition model.
2. a kind of public building electricity consumption pattern recognition model method for building up based on historical data according to claim 1,
It is characterized in that：
In S2 step, first standardize to all feature vectors [x, y], obtains standardization feature vector z=[zx,zy], then it is right
Standardize feature vector z=[zx,zy] K-means clustering is carried out, the formula of standardization is：
With
μ (x) and μ (y) respectively represent any history acquisition time, the user's history hour maximum power consumption load of all users
The average value of the user's history hourly average power load of average value and all users, σ (x) and σ (y) respectively represent the history and adopt
Collect time point, the standard deviation of the user's history hour maximum power consumption load of all users and the user's history hour of all users are flat
The standard deviation of equal power load.
3. a kind of public building electricity consumption pattern recognition model method for building up based on historical data according to claim 2,
It is characterized in that：To standardization feature vector z=[z in S2 stepx,zy] carry out K-means clustering the specific steps are：
S21, using user hour maximum power consumption load as abscissa, user's hourly average power load is that the right angle of ordinate is sat
In mark system, k cluster centre u is picked upi(j), wherein j=0, i=1,2 ... ..., k；
S22 chooses n standardization feature vector, is denoted as zt, wherein t=1,2 ... ..., n, calculate each ztInto each cluster
Heart ui(j) Euclidean distance；
S23 is found out about each ztThe cluster centre u of minimum euclidean distancei(j), ztInto cluster corresponding to the cluster centre, and
The geometric center of each cluster is denoted as new cluster centre ui(j+1)；
S24 calculates all ztAbout new cluster centre u corresponding with cluster where iti(j+1) the square error E of Euclidean distancej,
Determine square error EjWhether in range of set value；
S25, if square error EjIn range of set value, then current all cluster centre u are exportediAnd corresponding cluster (j+1),
Interior all standardization feature vector zt, each cluster centre uses power mode as one；
S26, if square error EjNot in range of set value, then j=j+1, and return step S23 are enabled.
4. a kind of public building electricity consumption pattern recognition model method for building up based on historical data according to claim 3,
It is characterized in that：To by standardization feature vector z in S3 stept, wherein t=1,2 ... ..., n, the sample set S constituted, with
The smallest attribute of the value of Gain_GINI and attribute value are as optimal Split Attribute and optimal Split Attribute value.
5. a kind of public building electricity consumption pattern recognition model method for building up based on historical data according to claim 4,
It is characterized in that：
For sample set S,
Wherein oiI-th of probability occurred with power mode in presentation class result；
Sample set S is divided into two parts according to user's history hour maximum power consumption load, Gain_GINI calculates as follows：
Optimal two offshoot program is
6. a kind of public building electricity consumption pattern recognition model method for building up based on historical data according to claim 4,
It is characterized in that：
For sample set S,
Wherein oiI-th of probability occurred with power mode in presentation class result；
Sample set S is divided into two parts according to user's history hourly average power load, Gain_GINI calculates as follows：
Optimal two offshoot program is
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Application publication date: 20181130 |