CN109341020A - A kind of intelligent temperature control adjusting method based on big data - Google Patents
A kind of intelligent temperature control adjusting method based on big data Download PDFInfo
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- 238000010801 machine learning Methods 0.000 claims abstract description 7
- 238000004378 air conditioning Methods 0.000 claims description 30
- 238000005265 energy consumption Methods 0.000 claims description 15
- 230000004927 fusion Effects 0.000 claims description 14
- 230000006870 function Effects 0.000 claims description 12
- 230000002776 aggregation Effects 0.000 claims description 8
- 238000004220 aggregation Methods 0.000 claims description 8
- 238000012163 sequencing technique Methods 0.000 claims description 8
- 238000002790 cross-validation Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 5
- 238000012417 linear regression Methods 0.000 claims description 3
- 238000002156 mixing Methods 0.000 claims description 3
- 238000007500 overflow downdraw method Methods 0.000 claims description 3
- 238000006116 polymerization reaction Methods 0.000 claims 1
- 238000009423 ventilation Methods 0.000 abstract description 4
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/50—Air quality properties
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2130/00—Control inputs relating to environmental factors not covered by group F24F2110/00
- F24F2130/10—Weather information or forecasts
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2140/00—Control inputs relating to system states
- F24F2140/60—Energy consumption
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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- Engineering & Computer Science (AREA)
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- Mathematical Physics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The present invention provides a kind of intelligent temperature control adjusting method based on big data, including data prediction, Feature Engineering building operation, establish multiple machine learning models, the present invention collects user data using the information exchange of smart home, and analyze user data, to realize ventilation, adjust user's comfort temperature, guarantee indoor air quality and reach target for energy-saving and emission-reduction, the social value of energy-saving and emission-reduction can be realized significantly.
Description
Technical field
The present invention relates to big data analysis applied technical fields, are related in e-commerce, raw more particularly, to smart home
In work, meet a kind of intelligent temperature control adjusting side based on big data that consumer accurately adjusts indoor temperature and humidity and air cleanliness
Method.
Background technique
Smart home concept originates from the U.S. of early 1980s, and referred to as Smart Home experienced for 4 generations
Development: the first generation is to complete home intranet by coaxial line and two core wires, and then realize light, curtain and a small amount of security protection control
Deng；The second generation is can to complete the business of video intercom and security protection by bus and IP technology networking；The third generation is centralization
Intelligence control system is completed the function of security protection, metering etc. by middle control machine；Forth generation be then based on technology of Internet of things can according to
Family demand realizes personalized function, and compared with common household, smart home not only has traditional inhabitation function, while can
Information exchange function is provided, the final goal of smart home is to make domestic environment more comfortable, safer, more environmentally friendly, more convenient.
The appearance of Internet of Things is so that present smart home system function is more abundant, more diversified and personalized
System function is concentrated mainly on intelligent lighting controls, intelligent appliance control, Video chat and intelligent security guard etc., currently based on big data
Intelligent temperature control adjust, i.e., how using the information exchange of smart home to collect user data, and user data analyzed, thus real
Existing ventilation adjusts user's comfort temperature, guarantees indoor air quality and reach target for energy-saving and emission-reduction then to be current intelligence
The technical problem that household faces.
Summary of the invention
Aiming at the problem that above-mentioned background technique is illustrated, it is an object of the present invention to provide a kind of intelligent temperature controls based on big data
Adjusting method.
In order to achieve the above object, the invention provides the following technical scheme:
A kind of intelligent temperature control adjusting method based on big data, includes the following steps:
Humidity, indoor air quality, outdoor temp is arranged according to date, history setting temperature, history in Q1, data prediction
Degree, air conditioning energy consumption carry out data preprocessing operation, data that treated and supply the numerical value lacked, using standardization, normalizing
Change processing；
Q2, division operation is carried out according to time of the act, by the time according to delimiting four seasons；
Q3, humidity, indoor air quality, outdoor temperature, air conditioning energy consumption are arranged according to date, history setting temperature, history
Data carry out Feature Engineering building operation, establish basic count feature, aggregation features, sequencing feature；
Q4, feature importance ranking is carried out special according to the feature distribution of feature obtained by Feature Engineering according to Feature Engineering
Whole operation is requisitioned, the non-uniform feature of feature distribution is standardized, normalized, standardization is according to eigenmatrix
The column vector column in the eigenmatrix being unevenly distributed are converted " unit vector " by column processing feature vector；
Q5, multiple machine learning models are established, and carry out mixing operation, select one using linear based on multiple single models plus
Weigh the method for fusion or the method for the Stacking Model Fusion based on multiple single models；
Q6, by the way that model has been established, according to the date, history setting temperature, history be arranged humidity, indoor air quality, room
Tri- outer temperature, air conditioning energy consumption data application SVM, LightGBM, XGBoost models, and carry out stacking Model Fusion with
And linear fusion, data are trained to obtain predicted temperature, air-conditioner temperature is adjusted according to predicted temperature, data is trained
Predicted temperature is obtained, air-conditioner temperature is adjusted according to predicted temperature.
The present invention is mainly by being arranged temperature, history setting humidity, indoor air quality, outdoor temp to date, history
The data such as degree, air conditioning energy consumption are pre-processed and are analyzed extraction feature, establish multiple machine learning models, to effectively adjust empty
Temperature regulating allows users to the temperature for experiencing the most comfortable；On the other hand energy consumption can also be effectively reduced, save electric energy.
In above-mentioned technical proposal, the delimiting four seasons are according to astronomical the standard of marking off the four seasons.
In above-mentioned technical proposal, in the Q3, the basis count feature refers to the number statistics of monthly outdoor temperature, the moon
The number statistics of degree history setting temperature, the number statistics of season outdoor temperature, is gone through in season the number statistics of monthly air-conditioning work number
The number statistics of temperature is arranged in history；The aggregation features refer to the statistics such as mean variance of monthly outdoor temperature feature, monthly
The statistics feature such as mean variance of temperature, the statistics feature, season room such as mean variance of monthly air-conditioning work number is arranged in history
The statistics such as mean variance of outer temperature feature, the mean variance statistics feature of season history setting temperature, season air-conditioning function
The mean variance statistics feature of consumption, the monthly least square correlation of outdoor temperature and history setting temperature, outdoor temperature with
History be arranged temperature season least square correlation, history setting temperature and air-conditioning power consumption least square correlation；It is described
Sequencing feature refers to history setting temperature and air-conditioning power consumption in the different different trend features, no of possessing in monthly and different season
The same period needs be ranked up different features, assign different weights.
In above-mentioned technical proposal, the linear weighted function fusion method based on multiple single models is as follows:
Assign tri- single model result x of SVM, LightGBM, XGBoost1、x2、x3Corresponding weight w1、w2、w3, Linear Quasi
Verifying collection true tag is closed, formula is as follows:
Y=x1*w1+x2*w2+x3*w3
In above-mentioned technical proposal, the method for the Stacking Model Fusion based on multiple single models is as follows:
First layer carries out three folding cross validations to model using SVM, LightGBM, XGBoost, obtains different engineerings
Model is practised, Y is verifying collection true tag, x1、x2、x3Three folding cross validations are carried out to model for SVM, LightGBM, XGBoost
Prediction result.Linear regression fit verifying collection true tag, acquires the parameter w of each model1、w2、w3。
Y=x1*w1+x2*w2+x3*w3
The second layer is x using SVM, LightGBM, XGBoost prediction result for test set1、x2、x3, predicted knot
Fruit obtains weight w multiplied by first layer respectively1、w2、w3, obtain final prediction result P.
P=X1*w1+X2*w2+X3*w3。
The present invention collects user data using the information exchange of smart home, and analyzes user data, to realize ventilation
Ventilation adjusts user's comfort temperature, guarantees indoor air quality and reach target for energy-saving and emission-reduction, can realize that significantly energy conservation subtracts
The social value of row.
Specific embodiment
Below in conjunction with the embodiment of the present invention, technical solution of the present invention is clearly and completely described, it is clear that institute
The embodiment of description is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention,
Every other embodiment obtained by those of ordinary skill in the art without making creative efforts, belongs to this hair
The range of bright protection.
The present invention is illustrated a kind of intelligent temperature control adjusting method based on big data, is included the following steps: with embodiment
A kind of intelligent temperature control adjusting method based on big data, includes the following steps:
Humidity, indoor air quality, outdoor temp is arranged according to date, history setting temperature, history in Q1, data prediction
Degree, air conditioning energy consumption carry out data preprocessing operation, data that treated and supply the numerical value lacked, using standardization, normalizing
Change processing；
Wherein essential information data include User ID, room ID, date, history setting temperature, history setting humidity, interior
Air quality, outdoor temperature, air conditioning energy consumption, season, if 2 intelligent temperature control of table is adjusted shown in data information data table, table 2 is intelligence
Temperature control adjusts data field and explains table:
1 intelligent temperature control of table adjusts data field and explains table
2 intelligent temperature control of table adjusts data information data table
Essential information data are adjusted to intelligent temperature control and supply the numerical value lacked, using standardization, normalized.To missing
Time filling are as follows:
The data of missing are carried out filling out 0, the exceptional value excessively high to floor is deleted, and humidity is normalized；
Q2, division operation is carried out according to time of the act, by the time according to delimiting four seasons；
Data are grouped according to season, and every group of time presses sequence from small to large:
time1≤time2≤time3≤…≤timen
Q3, humidity, indoor air quality, outdoor temperature, air conditioning energy consumption are arranged according to date, history setting temperature, history
Data carry out Feature Engineering building operation, establish basic count feature, aggregation features, sequencing feature；
Before extracting feature, training set data A is divided, 80% is used as training set T, and 20% as verifying collection V, i.e., full
Sufficient condition,Using verifying collection assessment models error after training model with training set, as right
The estimation of extensive error.Collection, and the data test collection for finally needing to predict are verified to the training set of division, extracted respectively special
Sign, count feature, aggregation features, sequencing feature.
Count feature: pressing user id, and season grouping carries out the total degree, most of statistics appearance to user using the number of air-conditioning
Big value, minimum value, variance, median；
Aggregation features: pressing user id, and season grouping carries out the temperature of statistics setting to user using air-conditioning setting temperature/humidity
Degree/humidity average value, standard deviation, maximum value, median；Statistical average is carried out by air conditioning energy consumption of the id grouping in room to room
Value, standard deviation, maximum value, median；
Sequencing feature: the maximum value and minimum value count to aggregation features is ranked up.
Q4, feature importance ranking is carried out special according to the feature distribution of feature obtained by Feature Engineering according to Feature Engineering
Whole operation is requisitioned, the non-uniform feature of feature distribution is standardized, normalized, standardization is according to eigenmatrix
The column vector column in the eigenmatrix being unevenly distributed are converted " unit vector " by column processing feature vector；
Q5, multiple machine learning models are established, and carry out mixing operation, select one using linear based on multiple single models plus
Weigh the method for fusion or the method for the Stacking Model Fusion based on multiple single models；
Q6, by the way that model has been established, according to the date, history setting temperature, history be arranged humidity, indoor air quality, room
Tri- outer temperature, air conditioning energy consumption data application SVM, LightGBM, XGBoost models, and carry out stacking Model Fusion with
And linear fusion, data are trained to obtain predicted temperature, air-conditioner temperature is adjusted according to predicted temperature.
The present invention is mainly by being arranged temperature, history setting humidity, indoor air quality, outdoor temp to date, history
The data such as degree, air conditioning energy consumption are pre-processed and are analyzed extraction feature, establish multiple machine learning models, to effectively adjust empty
Temperature regulating allows users to the temperature for experiencing the most comfortable；On the other hand energy consumption can also be effectively reduced, save electric energy.
In above-mentioned technical proposal, the delimiting four seasons are according to astronomical the standard of marking off the four seasons.
In above-mentioned technical proposal, in the Q3, the basis count feature refers to the number statistics of monthly outdoor temperature, the moon
The number statistics of degree history setting temperature, the number statistics of season outdoor temperature, is gone through in season the number statistics of monthly air-conditioning work number
The number statistics of temperature is arranged in history；The aggregation features refer to the statistics such as mean variance of monthly outdoor temperature feature, monthly
The statistics feature such as mean variance of temperature, the statistics feature, season room such as mean variance of monthly air-conditioning work number is arranged in history
The statistics such as mean variance of outer temperature feature, the mean variance statistics feature of season history setting temperature, season air-conditioning function
The mean variance statistics feature of consumption, the monthly least square correlation of outdoor temperature and history setting temperature, outdoor temperature with
History be arranged temperature season least square correlation, history setting temperature and air-conditioning power consumption least square correlation；It is described
Sequencing feature refers to history setting temperature and air-conditioning power consumption in the different different trend features, no of possessing in monthly and different season
The same period needs be ranked up different features, assign different weights.
In above-mentioned technical proposal, the linear weighted function fusion method based on multiple single models is as follows:
Assign tri- single model result x of SVM, LightGBM, XGBoost1、x2、x3Corresponding weight w1、w2、w3, Linear Quasi
Verifying collection true tag is closed, formula is as follows:
Y=x1*w1+x2*w2+x3*w3
Weight is as follows:
Finally take weight ratio optimal result:
| Model | Weights omegai |
| SVM | 0.05 |
| XGBoost | 0.7 |
| LightGBM | 0.25 |
In above-mentioned technical proposal, the method for the Stacking Model Fusion based on multiple single models is as follows:
First layer carries out three folding cross validations to model using SVM, LightGBM, XGBoost, obtains different engineerings
Model is practised, Y is verifying collection true tag, x1、x2、x3Three folding cross validations are carried out to model for SVM, LightGBM, XGBoost
Prediction result.Linear regression fit verifying collection true tag, acquires the parameter w of each model1、w2、w3。
Y=x1*w1+x2*w2+x3*w3
The second layer is X using SVM, LightGBM, XGBoost prediction result for test set1、X2、X3, predicted knot
Fruit obtains weight w multiplied by first layer respectively1、w2、w3, obtain final prediction result P.
P=X1*w1+X2*w2+X3*w3
Prediction result P is smart lock with the presence or absence of abnormal probability, when probability P >=0.5 when, we judge that temperature control exists
It is abnormal；When probability P < 0.5, we judge temperature control, and there is no abnormal.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. a kind of intelligent temperature control adjusting method based on big data, characterized by the following steps: Q1, data prediction,
It carries out data according to date, history setting temperature, history setting humidity, indoor air quality, outdoor temperature, air conditioning energy consumption and locates in advance
Reason operation, data that treated and supplies the numerical value lacked, using standardization, normalized；
Q2, division operation is carried out according to time of the act, by the time according to delimiting four seasons；
Q3, humidity, indoor air quality, outdoor temperature, air conditioning energy consumption data are arranged according to date, history setting temperature, history
Feature Engineering building operation is carried out, basic count feature, aggregation features, sequencing feature are established；
Q4, feature tune is carried out according to the feature distribution of feature obtained by Feature Engineering to feature importance ranking according to Feature Engineering
Whole operation is standardized the non-uniform feature of feature distribution, normalized, and standardization is at the column according to eigenmatrix
Feature vector is managed, converts " unit vector " for the column vector column in the eigenmatrix being unevenly distributed；
Q5, multiple machine learning models are established, and carries out mixing operation, selected one and melted using the linear weighted function based on multiple single models
The method of the method for conjunction or the Stacking Model Fusion based on multiple single models；
Q6, by the way that model has been established, according to the date, history setting temperature, history be arranged humidity, indoor air quality, outdoor temp
Tri- degree, air conditioning energy consumption data application SVM, LightGBM, XGBoost models, and carry out stacking Model Fusion and line
Property fusion, data are trained to obtain predicted temperature, according to predicted temperature adjust air-conditioner temperature data are trained to obtain
Predicted temperature adjusts air-conditioner temperature according to predicted temperature.
2. a kind of intelligent temperature control adjusting method based on big data according to claim 1, it is characterised in that: the four seasons
It divides according to astronomical the standard of marking off the four seasons.
3. a kind of intelligent temperature control adjusting method based on big data according to claim 1, it is characterised in that: the Q3
In, the basis count feature refers to the number statistics of monthly outdoor temperature, the number of monthly history setting temperature counts, is monthly
Number statistics, the number statistics of season outdoor temperature, the number statistics of season history setting temperature of air-conditioning work number；The polymerization
Feature refers to the statistics such as the mean variance of the statistics such as mean variance of monthly outdoor temperature feature, monthly history setting temperature
The statistics features, season such as the mean variance of feature, the statistics feature such as mean variance of monthly air-conditioning work number, season outdoor temperature
Spend mean variance statistics feature, mean variance statistics feature, the outdoor temperature of season air-conditioning power consumption of history setting temperature
The season least square correlation of temperature is set with monthly least square correlation, outdoor temperature and the history of history setting temperature
Property, history setting temperature and air-conditioning power consumption least square correlation；The sequencing feature refers to history setting temperature and air-conditioning
Power consumption possesses different trend features, different period needs monthly and different season to different feature progress in different
It sorts, assign different weights.
4. a kind of intelligent temperature control adjusting method based on big data according to claim 1, it is characterised in that: described to be based on
The linear weighted function fusion method of multiple single models is as follows:
Assign tri- single model result x of SVM, LightGBM, XGBoost1、x2、x3Corresponding weight w1、w2、w3, linear fit tests
Card collection true tag, formula are as follows:
Y=x1*w1+x2*w2+x3*w3
5. a kind of intelligent temperature control adjusting method based on big data according to claim 1, it is characterised in that: described to be based on
The method of the Stacking Model Fusion of multiple single models is as follows:
First layer carries out three folding cross validations to model using SVM, LightGBM, XGBoost, obtains different machine learning moulds
Type, Y are verifying collection true tag, x1、x2、x3The prediction of three folding cross validations is carried out to model for SVM, LightGBM, XGBoost
As a result.Linear regression fit verifying collection true tag, acquires the parameter w of each model1、w2、w3。
Y=x1*w1+x2*w2+x3*w3
The second layer is x using SVM, LightGBM, XGBoost prediction result for test set1、x2、x3, by its prediction result point
Weight w is not obtained multiplied by first layer1、w2、w3, obtain final prediction result P.
P=X1*w1+X2*w2+X3*w3。
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Cited By (9)
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| CN111043726A (en) * | 2019-12-30 | 2020-04-21 | 青岛海尔空调器有限总公司 | Intelligent air conditioner regulation and control method and air conditioner |
| CN111121237A (en) * | 2019-12-27 | 2020-05-08 | 广东美的白色家电技术创新中心有限公司 | Air conditioner, control method thereof, server, and computer-readable storage medium |
| CN111271829A (en) * | 2019-05-08 | 2020-06-12 | 宁波奥克斯电气股份有限公司 | Air conditioner intelligent refrigeration comfort control method based on deep learning and air conditioner |
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| CN112254287A (en) * | 2020-09-01 | 2021-01-22 | 深圳达实智能股份有限公司 | Variable-weight multi-model comprehensive prediction central air conditioner tail end air supply control method |
| CN112418010A (en) * | 2020-11-09 | 2021-02-26 | 电子科技大学 | Equipment temperature monitoring method based on LightGBM fusion model |
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| CN112728739A (en) * | 2020-12-28 | 2021-04-30 | 博锐尚格科技股份有限公司 | Method and device for determining set value of air supply temperature of air conditioning box based on algorithm prediction |
| CN117606109A (en) * | 2024-01-22 | 2024-02-27 | 南京群顶科技股份有限公司 | Method and system for judging optimal temperature of air conditioner in machine room |
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