CN115076765A - Carbon fiber electric heating indoor temperature control scheduling method and system based on correlation analysis - Google Patents
Carbon fiber electric heating indoor temperature control scheduling method and system based on correlation analysis Download PDFInfo
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- CN115076765A CN115076765A CN202210674980.8A CN202210674980A CN115076765A CN 115076765 A CN115076765 A CN 115076765A CN 202210674980 A CN202210674980 A CN 202210674980A CN 115076765 A CN115076765 A CN 115076765A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D19/00—Details
- F24D19/10—Arrangement or mounting of control or safety devices
- F24D19/1096—Arrangement or mounting of control or safety devices for electric heating systems
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D2200/00—Heat sources or energy sources
- F24D2200/08—Electric heater
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D2220/00—Components of central heating installations excluding heat sources
- F24D2220/20—Heat consumers
- F24D2220/2009—Radiators
- F24D2220/2036—Electric radiators
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Abstract
The invention discloses a carbon fiber electric heating indoor temperature control scheduling method and system based on correlation analysis.
Description
Technical Field
The invention relates to the technical field of heating, in particular to a carbon fiber electric heating indoor temperature control scheduling method and system based on correlation analysis.
Background
Along with the development of economy, the living standard of people is improved, the demand of society on electric energy is continuously increased, valley electricity can be fully utilized by using electric energy for heating, the electric loss can be reduced, and economic heating can be realized. Carbon fiber electric heating mainly adopts the commercial power to heat, according to the newest energy policy in our country, faces serious electric power energy and lacks in the short supply on the one hand, realizes the clean energy requirement of "carbon reaches peak carbon neutralization" on the other hand, and how to guarantee the heating requirement of residents in winter through reasonable control by temperature change scheduling strategy reduces the energy consumption simultaneously, is the problem that the field needs to be solved urgently.
Disclosure of Invention
Therefore, the invention provides a carbon fiber electric heating indoor temperature control scheduling method and system based on correlation analysis, and aims to solve the problems of ensuring heating and reducing energy consumption.
In order to achieve the above purpose, the invention provides the following technical scheme:
according to a first aspect of the embodiments of the present invention, a method for scheduling indoor temperature control of carbon fiber electric heating based on correlation analysis is provided, where the method includes:
creating a sample data set according to actual operation data of a user for heating by using carbon fiber electricity;
and mining the incidence relation of all factors on the sample data set based on an incidence analysis algorithm, and making a temperature control scheduling strategy according to the result to regulate and control the heating temperature.
Furthermore, the sample data includes the age of the householder, the average room temperature of heating, and the number of family population.
Further, the association analysis algorithm adopts an Apriori association analysis algorithm.
Further, mining the association relation of each factor to the sample data set based on an association analysis algorithm, specifically comprising:
generating a candidate set M1 from the sample data set; calculating and filtering data by using the support degree of M1 to generate a frequent item set F1, splicing the data items of F1 into M2 pairwise, starting from the candidate item set M2, generating F2 by support degree filtering, and splicing F2 into a candidate item set M3 according to the Apriori principle; m3 generates F3, … … through support degree filtering until there is only one or no data item in Fk, k being a natural number.
Further, mining the association relation of each factor to the sample data set based on an association analysis algorithm, and specifically comprising:
an association between the two terms is defined by confidence calculation and an association rule is generated.
Further, the support degree refers to the support degree of a certain thing in the set, that is, the proportion of the unit item appearing in the set is represented, and then the support degree of associating a thing with B is expressed by the formula:
S(A,B)＝num(A+B)/W(％)；
where W is the number of sets, and num (A + B) represents the number of occurrences of A and B together in W.
Further, the confidence coefficient refers to the probability of B occurring when A occurs, and the probability of B occurring is what, if the confidence coefficient of A- > B is 100%, it indicates that B will occur when A occurs;
the formula is as follows: c (a, B) ═ S (a, B)/S (a), S is the support.
According to a second aspect of the embodiments of the present invention, a carbon fiber electric heating indoor temperature control scheduling system based on correlation analysis is provided, the system includes:
the data set creating module is used for creating a sample data set according to actual operation data of a user for heating by using carbon fiber electricity;
and the association analysis module is used for mining the association relation of all factors of the sample data set based on an association analysis algorithm and making a temperature control scheduling strategy according to the result to regulate and control the heating temperature.
Further, the association analysis algorithm adopts an Apriori association analysis algorithm.
The invention has the following advantages:
according to the carbon fiber electric heating indoor temperature control scheduling method and system based on the correlation analysis, through an Apriori correlation analysis algorithm, enough sample data are used for performing correlation analysis on the age group and the temperature of a user, the heating temperature is reasonably regulated and controlled, the energy consumption is saved on the premise of ensuring reasonable heating, the cost is reduced, and the economic benefit of an enterprise is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a schematic flowchart of a method for scheduling a temperature control in a carbon fiber electric heating room based on correlation analysis according to embodiment 1 of the present invention;
fig. 2 is a schematic flow diagram of a frequent item set generation process in a carbon fiber electric heating indoor temperature control scheduling method based on correlation analysis according to embodiment 1 of the present invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, this embodiment proposes a method for scheduling temperature control in a carbon fiber electric heating room based on correlation analysis, where the method includes:
s100, creating a sample data set according to actual operation data of a user for heating by using carbon fiber electricity;
s200, mining the association relation of all factors of the sample data set based on an association analysis algorithm, and making a temperature control scheduling strategy according to the result to regulate and control the heating temperature.
In this embodiment, an Apriori association analysis algorithm is used as the association analysis algorithm.
Correlation analysis is an unsupervised learning algorithm that finds potential correlations in large-scale datasets. These relationships can take two forms: a frequent set of items or association rules. Wherein a frequent item set is a collection of items that often appear in a block, and the association rule is to suggest that there may be a strong inherent non-surface association between two items. The frequency and correlation are defined in the correlation analysis by two parameters: support and confidence.
The support degree is as follows: the support degree of a certain object in the set, that is, the proportion of the unit item appearing in the set, is expressed, and the support degree of the two objects related to A and B is expressed by the formula:
S(A,B)＝num(A+B)/W(％),
wherein W is the number of sets, and num (A + B) represents the number of occurrences of A and B in W together.
Confidence coefficient: the probability of B appearing when A appears is referred to, and if the confidence of A < - > B is 100%, the probability indicates that B will appear when A appears (the reverse is not necessarily true).
The formula is as follows: c (a, B) ═ S (a, B)/S (a), S is the support.
The algorithm comprises the following steps:
(1) creating a data set: and selecting a proper data sample, and taking the age of the householder, the average room temperature and the family as a set, wherein in order to reduce the complexity of the data, the age uses a section representative in the calculation, such as 38 epsilon 30-40 sections. The temperature is 18-20 ℃, 20-22 ℃, 22-24 ℃ and 25 ℃ according to the energy consumption proportion.
(2) Generating a candidate item set: generating a candidate M1 from the above table data set; calculating and filtering data by using the support degree of M1 to generate a frequent item set F1, splicing the data items of F1 into M2 pairwise, starting from the candidate item set M2, generating F2 by support degree filtering, and splicing F2 into a candidate item set M3 according to the Apriori principle; m3 generates F3 … … through support filtering until there is only one or no data item in Fk (k is a natural number). As shown in fig. 2.
(3) The association rule data is generated according to an algorithm as follows:
20-30age；-->20-22℃；:1.0
60+age；-->25+℃；:1.0
40-50age；-->22-24℃；:1.0
50-60age；-->25+℃；:1.0
and if the confidence coefficient of the data set is 1, the data set is a valid credible set. The data can thus be concluded as: the room temperature of 20-30 years old age group is 20-22 ℃, the room temperature of 50-60+ years old age group is 25+ DEG C, the room temperature of 40-50 years old age group is 22-24 ℃, and the actual result is irrelevant to family population.
Through Apriori correlation analysis algorithm, correlation analysis is carried out to user age group and temperature use through enough sample data to the heating temperature is regulated and control rationally, energy saving under the prerequisite of guaranteeing reasonable heating, and reduce cost promotes enterprise economic benefits.
Example 2
Corresponding to the foregoing embodiment 1, this embodiment proposes a carbon fiber electric heating indoor temperature control scheduling system based on correlation analysis, where the system includes:
the data set creating module is used for creating a sample data set according to actual operation data of a user for heating by using carbon fiber electricity;
and the association analysis module is used for mining the association relation of all factors of the sample data set based on an association analysis algorithm and making a temperature control scheduling strategy according to the result to regulate and control the heating temperature.
Further, the association analysis algorithm adopts an Apriori association analysis algorithm.
The functions executed by each component in the carbon fiber electric heating indoor temperature control scheduling system based on the correlation analysis provided by the embodiment of the present invention are described in detail in the above embodiment 1, and therefore, redundant description is not repeated here.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (9)
1. A carbon fiber electric heating indoor temperature control scheduling method based on correlation analysis is characterized by comprising the following steps:
creating a sample data set according to actual operation data of a user for heating by using carbon fiber electricity;
and mining the incidence relation of all factors on the sample data set based on an incidence analysis algorithm, and making a temperature control scheduling strategy according to the result to regulate and control the heating temperature.
2. The carbon fiber electric heating indoor temperature control scheduling method based on the correlation analysis of claim 1, wherein the sample data includes a family owner age, a heating average room temperature, and a family population number.
3. The carbon fiber electric heating indoor temperature control scheduling method based on correlation analysis of claim 1, wherein the correlation analysis algorithm adopts Apriori correlation analysis algorithm.
4. The carbon fiber electric heating indoor temperature control scheduling method based on correlation analysis according to claim 1, wherein mining the correlation relation of each factor to the sample data set based on a correlation analysis algorithm specifically comprises:
generating a candidate set M1 from the sample data set; calculating and filtering data by using the support degree of M1 to generate a frequent item set F1, splicing the data items of F1 into M2 pairwise, starting from the candidate item set M2, generating F2 by support degree filtering, and splicing F2 into a candidate item set M3 according to the Apriori principle; m3 generates F3, … … through support degree filtering until there is only one or no data item in Fk, k being a natural number.
5. The carbon fiber electric heating indoor temperature control scheduling method based on correlation analysis according to claim 1, wherein mining the correlation relation of each factor to the sample data set based on a correlation analysis algorithm, specifically further comprises:
an association between the two terms is defined by confidence calculation and an association rule is generated.
6. The method according to claim 4, wherein the support degree is the support degree of a certain thing in the set, that is, the support degree represents the proportion of unit items appearing in the set, and the support degree of the two things related to A and B is expressed by the formula:
S(A,B)＝num(A+B)/W(％)；
wherein W is the number of sets, and num (A + B) represents the number of occurrences of A and B in W together.
7. The carbon fiber electric heating indoor temperature control scheduling method based on the correlation analysis is characterized in that the confidence coefficient refers to the probability of B occurring when A occurs, and the probability of B occurring is what, if the confidence coefficient of A-B > is 100%, B is definitely occurred when A occurs;
the formula is as follows: c (a, B) ═ S (a, B)/S (a), and S is a support.
8. A carbon fiber electric heating indoor temperature control dispatching system based on correlation analysis is characterized by comprising:
the data set creating module is used for creating a sample data set according to actual operation data of a user for heating by using the carbon fiber electricity;
and the association analysis module is used for mining the association relation of all factors of the sample data set based on an association analysis algorithm and making a temperature control scheduling strategy according to the result to regulate and control the heating temperature.
9. The carbon fiber electric heating indoor temperature control dispatching system based on correlation analysis as claimed in claim 8, wherein the correlation analysis algorithm adopts Apriori correlation analysis algorithm.
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Cited By (1)
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| CN115659845A (en) * | 2022-12-08 | 2023-01-31 | 江苏擎天工业互联网有限公司 | Carbon emission calculation method and device based on electric power data |
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