CN111649465A - Automatic control method and system for air conditioning equipment - Google Patents

Automatic control method and system for air conditioning equipment Download PDF

Info

Publication number
CN111649465A
CN111649465A CN202010505056.8A CN202010505056A CN111649465A CN 111649465 A CN111649465 A CN 111649465A CN 202010505056 A CN202010505056 A CN 202010505056A CN 111649465 A CN111649465 A CN 111649465A
Authority
CN
China
Prior art keywords
air conditioning
conditioning equipment
moment
prediction model
determining
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010505056.8A
Other languages
Chinese (zh)
Other versions
CN111649465B (en
Inventor
赵泽明
刘京
周志刚
靳崇渝
薛普宁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Harbin Institute of Technology
Original Assignee
Harbin Institute of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Harbin Institute of Technology filed Critical Harbin Institute of Technology
Priority to CN202010505056.8A priority Critical patent/CN111649465B/en
Publication of CN111649465A publication Critical patent/CN111649465A/en
Application granted granted Critical
Publication of CN111649465B publication Critical patent/CN111649465B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • F24F11/89—Arrangement or mounting of control or safety devices
    • 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
    • F24F11/88—Electrical aspects, e.g. circuits
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • F24F2110/20—Humidity
    • 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
    • F24F2110/20—Humidity
    • F24F2110/22—Humidity 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
    • F24F2110/30—Velocity
    • 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

Abstract

The invention relates to an automatic control method and system for air conditioning equipment. The method comprises the steps of obtaining historical time data of the air conditioning equipment and corresponding historical time environment data; determining an automatic adjustment model of the air conditioning equipment by adopting a random forest model according to the data of the historical moment of the air conditioning equipment and the environmental data of the corresponding historical moment; acquiring environmental data of the air conditioning equipment at the current moment; determining the temperature of the air conditioning equipment at the next moment by utilizing an automatic adjusting model of the air conditioning equipment according to the environmental data at the current moment; and obtaining the environmental data of the air conditioning equipment at the next moment, returning the environmental data according to the current moment, and determining the temperature of the air conditioning equipment at the next moment by using an automatic adjustment model of the air conditioning equipment. The invention realizes the personalized and intelligent control of the intelligent household air conditioning equipment.

Description

Automatic control method and system for air conditioning equipment
Technical Field
The invention relates to the field of intelligent home furnishing, in particular to an automatic control method and system for air conditioning equipment.
Background
In recent years, the construction and deployment of smart cities are greatly promoted, and the smart home industry is vigorously developed under the drive of intelligent and automatic high and new technologies. Currently, various scenes such as intelligent video and audio, intelligent lighting, home security, intelligent door locks, intelligent doors and windows and the like are applied to the fire explosion. The intelligent home scene related to the building heating ventilation air conditioning equipment serving as the main power for improving the indoor environment of the building and meeting the heat comfort requirement of residents is less in application, the developed function is relatively single, and only the remote control of the air conditioner can be realized, namely, a user remotely and manually opens or closes the air conditioner on a mobile phone, sets the temperature of the air conditioner and the like. Due to differences in metabolism levels of residents, personal habit preferences, clothing thermal resistance and the like, the difference in thermal environment adaptability is large. Therefore, personalized and intelligent heating, ventilating and air conditioning equipment customization services are still lack at the present stage from the behavior habits and individual requirements of the residents, so that the indoor thermal comfortable environment preferred by the residents is created.
The problem arises, on the one hand, from the difficulty of studying the behavior habits of the occupants at present. The behavior of the residents has strong randomness and complexity, and the difficulty in testing and recording the behavior of the residents is high; meanwhile, the behavior research of the residents often causes the residents to worry about personal privacy leakage, so the research data volume is seriously insufficient, and the habits of the air conditioning behavior of the residents are not fully recognized. On the other hand, the current research methods for the behavior habits of the occupants have limitations. At present, the method mainly depends on short-term actual measurement research and physical observation and analysis, but lacks of long-term and regular research based on big data and cloud platforms.
Therefore, the prior art cannot realize the personalized and intelligent control of the intelligent household air conditioning equipment.
Disclosure of Invention
The invention aims to provide an automatic control method and system of air conditioning equipment, which are used for realizing personalized and intelligent control of intelligent household air conditioning equipment.
In order to achieve the purpose, the invention provides the following scheme:
an automatic control method of an air conditioning device comprises the following steps:
acquiring data of historical moments of air conditioning equipment and environmental data of corresponding historical moments; the environment data comprises outdoor air temperature, outdoor relative humidity, indoor relative humidity, precipitation condition, air quality index and wind speed at historical time; the historical moment data comprises working conditions, working states and set temperatures of the air conditioning equipment at the corresponding moment under the environment data; the working condition comprises heating or cooling; the working state comprises opening or closing;
determining an automatic adjustment model of the air conditioning equipment by adopting a random forest model according to the data of the historical moment of the air conditioning equipment and the environmental data of the corresponding historical moment; the automatic adjusting model takes the environmental data at the current moment as input and takes the temperature at the next moment of the air conditioning equipment as output; the automatic adjusting model comprises a working condition prediction model, a working state prediction model and a temperature prediction model of the air conditioning equipment; the working condition prediction model of the air conditioning equipment takes the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity and the time as input and takes the working condition of the air conditioning equipment as output; the working state prediction model of the air conditioning equipment takes environmental data at the current moment and working conditions at the current moment as input, and takes the working state of the air conditioning equipment at the next moment as output; the temperature prediction model of the air conditioning equipment takes environmental data at the current moment, working conditions at the current moment and working states at the current moment as input, and takes the temperature at the next moment of the air conditioning equipment as output;
acquiring environmental data of the air conditioning equipment at the current moment;
determining the temperature of the air conditioning equipment at the next moment by utilizing an automatic adjusting model of the air conditioning equipment according to the environmental data at the current moment;
and obtaining the environmental data of the air conditioning equipment at the next moment, returning the environmental data according to the current moment, and determining the temperature of the air conditioning equipment at the next moment by using an automatic adjustment model of the air conditioning equipment.
Optionally, the acquiring data of the historical time of the air conditioning equipment and the environmental data of the corresponding historical time further includes:
and determining a time-varying matrix of the historical time data and the corresponding historical time environmental data of the air-conditioning equipment according to the historical time data and the corresponding historical time environmental data of the air-conditioning equipment.
Optionally, the determining, according to the data of the historical time of the air conditioning equipment and the environmental data of the corresponding historical time, an automatic adjustment model of the air conditioning equipment by using a random forest model specifically includes:
determining a working condition prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity and the indoor relative humidity of the air conditioning equipment at the historical moment and the working conditions at the corresponding moments;
determining a working state prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data at the historical moment and the working condition and the working state of the air conditioning equipment at the corresponding moment;
and determining a temperature prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data at the historical moment, the working condition, the working state and the set temperature of the air conditioning equipment at the corresponding moment.
Optionally, the determining, according to the environmental data at the historical time and the working condition and the working state of the air conditioning equipment at the corresponding time, a random forest model is used to determine a working state prediction model of the air conditioning equipment, which specifically includes:
when the working condition is heating, determining a first prediction model of the working state of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition and the working state at the historical moment;
when the working condition is cooling, determining a second prediction model of the working state of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition, the air quality index, the wind speed and the working state at the historical moment;
and determining the working state prediction model according to the working state first prediction model and the working state second prediction model.
Optionally, the random forest model includes 20 base learners.
An automatic control system of an air conditioning apparatus, comprising:
the historical data acquisition module is used for acquiring data of historical moments of the air conditioning equipment and environmental data of corresponding historical moments; the environment data comprises outdoor air temperature, outdoor relative humidity, indoor relative humidity, precipitation condition, air quality index and wind speed at historical time; the historical moment data comprises working conditions, working states and set temperatures of the air conditioning equipment at the corresponding moment under the environment data; the working condition comprises heating or cooling; the working state comprises opening or closing;
the automatic adjustment model determining module is used for determining an automatic adjustment model of the air conditioning equipment by adopting a random forest model according to the historical moment data of the air conditioning equipment and the corresponding historical moment environmental data; the automatic adjusting model takes the environmental data at the current moment as input and takes the temperature at the next moment of the air conditioning equipment as output; the automatic adjusting model comprises a working condition prediction model, a working state prediction model and a temperature prediction model of the air conditioning equipment; the working condition prediction model of the air conditioning equipment takes the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity and the time as input and takes the working condition of the air conditioning equipment as output; the working state prediction model of the air conditioning equipment takes environmental data at the current moment and working conditions at the current moment as input, and takes the working state of the air conditioning equipment at the next moment as output; the temperature prediction model of the air conditioning equipment takes environmental data at the current moment, working conditions at the current moment and working states at the current moment as input, and takes the temperature at the next moment of the air conditioning equipment as output;
the current-time environmental data acquisition module is used for acquiring current-time environmental data of the air conditioning equipment;
the temperature automatic adjusting module is used for determining the temperature of the air conditioning equipment at the next moment by utilizing an automatic adjusting model of the air conditioning equipment according to the environmental data at the current moment;
and the automatic control module is used for acquiring the environmental data of the air conditioning equipment at the next moment, returning the environmental data according to the current moment and determining the temperature of the air conditioning equipment at the next moment by using an automatic adjustment model of the air conditioning equipment.
Optionally, the method further includes:
and the matrix determining module is used for determining a time-varying matrix of the historical moment data and the corresponding historical moment environmental data of the air conditioning equipment according to the historical moment data and the corresponding historical moment environmental data of the air conditioning equipment.
Optionally, the automatic adjustment model determining module specifically includes:
the working condition prediction model determining unit is used for determining a working condition prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity and the indoor relative humidity of the air conditioning equipment at the historical moment and the working conditions at the corresponding moments;
the working state prediction model determining unit is used for determining a working state prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data of the historical moment and the working condition and the working state of the air conditioning equipment at the corresponding moment;
and the temperature prediction model determining unit is used for determining the temperature prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data of the historical moment and the working condition, the working state and the set temperature of the air conditioning equipment at the corresponding moment.
Optionally, the working state prediction model determining unit specifically includes:
the working state first prediction model determining subunit is used for determining a working state first prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition and the working state at the historical moment when the working condition is heating;
the working state second prediction model determining subunit is used for determining a working state second prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition, the air quality index, the wind speed and the working state at the historical moment when the working condition is cooling;
and the working state prediction model determining subunit is used for determining the working state prediction model according to the working state first prediction model and the working state second prediction model.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the automatic control method and the system for the air conditioning equipment, the automatic adjustment model of the air conditioning equipment is determined by adopting a random forest model according to the historical time data and the corresponding historical time environment data of the air conditioning equipment, the adjustment of the air conditioning equipment is updated in real time according to the automatic adjustment model and the real-time environment data and dynamically changes along with time, and the personal habit preference and the change of the demand of a resident can be dynamically reflected. Personnel operation is not needed, so that personalized and intelligent customized services of the intelligent household air conditioning equipment scene are effectively guided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an automatic control method for air conditioning equipment according to the present invention;
FIG. 2 is a schematic matrix diagram of historical time data and corresponding historical time environmental data of the air conditioning equipment, which are provided by the invention, changing along with time;
FIG. 3 is a schematic diagram of a base learner configuration according to the present invention;
FIG. 4 is a schematic diagram of the midpoint of the binary classification method;
FIG. 5 is a schematic diagram of a base learner for examining alternative internal nodes during pruning in accordance with the present invention;
FIG. 6 is a schematic view of the condition adjustment provided by the present invention;
fig. 7 is a schematic structural diagram of an automatic control system of an air conditioning apparatus according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The invention aims to provide an automatic control method and system of air conditioning equipment, which are used for realizing personalized and intelligent control of intelligent household air conditioning equipment.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow chart of an automatic control method for air conditioning equipment provided by the present invention, and as shown in fig. 1, the automatic control method for air conditioning equipment provided by the present invention includes:
s101, acquiring historical moment data of the air conditioning equipment and corresponding historical moment environment data; the environment data comprises outdoor air temperature, outdoor relative humidity, indoor relative humidity, precipitation condition, air quality index and wind speed at historical time; the historical moment data comprises working conditions, working states and set temperatures of the air conditioning equipment at the corresponding moment under the environment data; the working condition comprises heating or cooling; the working state comprises opening or closing.
The indoor and outdoor environment data acquisition system and the smart home platform are used for acquiring historical data of the air conditioning equipment and corresponding historical environmental data. The building outdoor environment data acquisition system is used for monitoring the outdoor air temperature, the outdoor relative humidity, the precipitation condition, the pollutant concentration (ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, PM10 and PM2.5) in the air and the wind speed in real time; each air-conditioned room should be provided with real-time monitoring of the indoor relative humidity. The smart home platform records and uploads the conditions of heating, cooling and temperature setting of the air conditioning equipment switched on and off by the residents in real time.
The working condition, the working state and the set temperature in the historical data of the air conditioning equipment are all according to the corresponding environmental data, and the behavior habit and the personal preference strategy data which are made by the resident are in accordance with the resident. That is, the behavior, operating state, and set temperature of the data of the air conditioning equipment at the historical time reflect the habits of the occupants.
The historical time data of the air conditioning equipment and the environmental data of the corresponding historical time are shown in table 1:
Figure BDA0002526241820000071
the Air Quality Index (AQI) is calculated according to the regulation of environmental Air Quality Index (AQI) technical regulation (trial) HJ633-2012, and the calculation method comprises the following steps of:
AQI＝max{IAQI1,IAQI2,IAQI3,...,IAQI6} (1)
AQI-air quality index;
IAQI-air mass fraction index of a pollutant item.
Figure BDA0002526241820000081
IAQI in the formulaiAir quality indicator of pollutant item i, Ci-mass concentration value of contaminant item i, BPHi-in Table 2 with CiHigh value of close contaminant concentration limit, BPLi-in Table 2 with CiLow value of close contaminant concentration limit, IAQIHiIn Table 2 with BPHiCorresponding air quality index, IAQILiIn Table 2 with BPLiCorresponding air mass fraction index, table 2 is air mass fraction index and corresponding pollutant item concentration limit, table 2 is as follows:
TABLE 2
Figure BDA0002526241820000082
In order to improve the modeling efficiency of an automatic adjustment model of the air conditioning equipment, the automatic control method of the air conditioning equipment provided by the invention determines a time-varying matrix of historical time data and corresponding historical time environmental data of the air conditioning equipment according to the historical time data and the corresponding historical time environmental data of the air conditioning equipment. The matrix is shown in fig. 2.
S102, determining an automatic adjustment model of the air conditioning equipment by adopting a random forest model according to the historical moment data of the air conditioning equipment and the corresponding historical moment environment data; the automatic adjusting model takes the environmental data at the current moment as input and takes the temperature at the next moment of the air conditioning equipment as output; the automatic adjusting model comprises a working condition prediction model, a working state prediction model and a temperature prediction model of the air conditioning equipment; the working condition prediction model of the air conditioning equipment takes the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity and the time as input and takes the working condition of the air conditioning equipment as output; the working state prediction model of the air conditioning equipment takes environmental data at the current moment and working conditions at the current moment as input, and takes the working state of the air conditioning equipment at the next moment as output; the temperature prediction model of the air conditioning equipment takes environmental data at the current moment, working conditions at the current moment and working states at the current moment as input, and takes the temperature at the next moment of the air conditioning equipment as output.
The specific prediction process of the working condition prediction model of the air conditioning equipment comprises the following steps:
acquiring 2 nearest samples of 'next-moment air conditioner heating state starting' and 'next-moment air conditioner cooling state starting' according to the historical moment data of the air conditioner and the corresponding historical moment environmental data (or 2 nearest samples of 'next-moment air conditioner cooling state starting' and 'next-moment air conditioner heating state starting'); and judging whether the states of the air-conditioning equipment among the 2 samples are all closed states, wherein the number c of the closed state samples is not less than (1440/a-1), wherein a is a sampling time interval (min), namely, the time interval among the 2 samples is not less than 1 day. If the conditions are met, judging that the air conditioner off state sample in the 2 samples is 0 or neutral at the next moment; the sample which is the nearest to the 'heating (cooling) state of the air conditioner started at the next moment before the closing state' and all samples in the previous year determine that 'the working condition of the air conditioner at the next moment is 1, and heating (═ 2 and cooling)'; the sample closest to the "cooling (heating) state of the air conditioning equipment started at the next time" after the off state and all samples in the following year are determined as "the air conditioning operating condition at the next time is 2, and cooling (1, heating)" is performed. As shown in fig. 6.
If no 'neutral position' is identified in the historical time data, namely only a sample of 'the heating state of the air conditioner started at the next moment' or only a sample of 'the cooling state of the air conditioner started at the next moment' is identified in the historical time data, namely only the heating (or cooling) working condition of the air conditioner exists in the room, all the historical samples are judged to be 'the working condition of the air conditioner at the next moment is 1, and the heating (or 2, cooling) is realized'.
The random forest model comprises 20 base learners, and a structural schematic diagram of the base learners is shown in FIG. 3. The construction method of each base learner is the same, and the specific construction steps of any base learner i (i is more than or equal to 1 and less than or equal to 20) are as follows:
1) and determining a sample set D according to the data of the air conditioning equipment at the historical moment and the environmental data of the corresponding historical moment.
2) Randomly extracting a plurality of attributes from the sample set D as candidate partition attributes; an optimal partition attribute is selected from the candidate partition attributes. The optimal partition attribute is placed at a root node (as shown in fig. 3), and the branches of the root node are the categories of the attribute partition respectively; when the attribute is a continuous variable, the attribute category is not clearly divided, and the program automatically realizes the second classification by taking the median points of all values of the continuous variable as boundaries, as shown in fig. 4.
The specific selection process of the optimal partition attribute comprises the following steps: firstly, calculating the 'information entropy' of a sample set D, as shown in formula (3); on the basis, calculating 'information gain' of each candidate partition attribute, as shown in formula (4); then calculating the gain rate of each candidate partition attribute, as shown in formula (5); among the attributes in which the information gain value is higher than the average information gain, the attribute with the largest gain ratio is selected as the optimal division attribute.
Figure BDA0002526241820000101
Wherein D is a sample set D; ent (D) -the entropy of the information of the set of samples D; k-class of sample label, k ═ 1,2, …, m; p is a radical ofk-probability of occurrence of class k sample label.
Figure BDA0002526241820000102
Where a-any candidate partition attribute a; gain (D, a) -the information Gain when the attribute a divides the sample D; l D-the number of samples in the set D; ent (D) -the entropy of the information of the set of samples D; v is any branch of the attribute a, V is 1,2, …, and V is the total branch number of the attribute a; dv-sample set of v branches in attribute a; i DvL-set of samples DvThe number of samples of (a); ent (D)v) Set of samples DvThe entropy of information of (1).
Figure BDA0002526241820000103
Where Gain _ ratio (D, a) -the Gain rate at which attribute a divides sample D; gain (D, a) -the information Gain when the attribute a divides the sample D; iv (a) -intrinsic values of the property a,
Figure BDA0002526241820000104
3) based on the division condition of the root node in step 2, if the program recognizes that the sample "label" of a certain branch belongs to the same type, the sample of the branch cannot be further divided, and the program will automatically mark the end of the branch as a "leaf node" (as shown in fig. 3). If the sample set of a branch is identified to be continuously divided, the program will continuously and automatically randomly extract 3 attributes from the input attributes as candidate division attributes. If the attribute of the node (root node) at the upper level of the node is a classification variable, the attribute cannot be used as the candidate partition attribute again; if the attribute of the node at the previous level of the node (such as the root node) is a continuous variable, the node can be continuously used as the attribute of the candidate partition at the level on the basis of the partition at the previous level. Based on the partition attribute determination, the program will automatically select the optimal partition attribute to be placed at the "internal node" (as shown in fig. 3, the selection method is the same as that in fig. 2). Based on the above dividing principle, the program will continue to automatically divide the sample and mark the nodes until there are no more available division attributes or the sample can not be further divided, and mark the branches as leaf nodes. Finally, a base learner-decision tree as in FIG. 3 is formed.
4) From sample set a, the complement of sample set D in step 1 (named sample set B) is taken. And (3) automatically substituting the samples in the set B into the base learner constructed in the step (3) by the program, and traversing each sample in the set B through the base learner to obtain the label category of each sample, namely the 'prediction result' based on the base learner. The program will automatically count the "predicted results" of the samples in set B against their original values.
In a specific embodiment, the output variable "heating and cooling conditions of the air conditioner at the next time of the room" is a classification variable, and the program automatically calculates the prediction Accuracy (Accuracy) based on the base learner, and the calculation formula is as shown in formula 6.
Figure BDA0002526241820000111
In the formula ncorrect-the number of correctly sorted samples in the set; n istotal-the total number of samples in the set.
5) Starting from the "internal node" at the bottom, as shown in fig. 3, the following operations are performed on the "internal nodes" one by one from bottom to top: as shown in fig. 5, taking the internal node r as an example, the internal node r is replaced by a "leaf node", that is, all samples under the internal node are marked as the label category with the largest number under the current node; and then automatically substituting the samples in the set B into the base learner constructed in the step 3, so that each sample in the set B traverses the base learner to obtain the label category of each sample, and obtaining a 'prediction result' based on the base learner. The program will automatically count the new "predicted result" of the samples in set B against their original values. The prediction accuracy 2 of this time base learner is calculated. If the prediction accuracy rate 2 of the base learner is higher than the prediction accuracy rate 1 of the base learner in the step 5, replacing the current 'internal node' with a 'leaf node', namely performing 'pruning'; otherwise, no replacement is performed, i.e., no "pruning".
6) And (4) completing pruning, namely completing the construction of a base learner i (i is more than or equal to 1 and less than or equal to 20).
And determining a working condition prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity and the indoor relative humidity of the air conditioning equipment at the historical moment and the working conditions at the corresponding moments.
And determining a working state prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data at the historical moment and the working condition and the working state of the air conditioning equipment at the corresponding moment.
And when the working condition is heating, determining a first prediction model of the working state of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition and the working state at the historical moment.
And when the working condition is cooling, determining a second prediction model of the working state of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition, the air quality index, the wind speed and the working state at the historical moment.
And determining the working state prediction model according to the working state first prediction model and the working state second prediction model.
And determining a temperature prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data at the historical moment, the working condition, the working state and the set temperature of the air conditioning equipment at the corresponding moment. The temperature prediction model of the air conditioning equipment is used for predicting the heating temperature of the air conditioning equipment and also used for predicting the cooling temperature of the air conditioning equipment according to different working conditions of heating or cooling.
And S103, acquiring the environmental data of the air conditioning equipment at the current moment.
And S104, determining the temperature of the air conditioning equipment at the next moment by utilizing an automatic adjusting model of the air conditioning equipment according to the environmental data at the current moment.
And taking the prediction results of the heating/cooling of the air conditioning equipment and the heating/cooling set temperature of the air conditioning equipment which are switched on and off at the next moment as the set values for automatically regulating and controlling the air conditioner at the next moment. Meanwhile, a user can intervene in the regulation and control of the air conditioning equipment through a control panel of the air conditioning equipment independently, and the system records the real on-off and temperature set values of the air conditioning equipment as historical data of modeling at the subsequent moment (namely when the user does not intervene in the regulation and control of the air conditioning equipment, the system automatically regulates and controls based on a prediction result and records the regulation and control conditions of the air conditioning at each moment, when the user intervenes in the regulation and control of the air conditioning, the regulation and control of the user controls the setting of the air conditioning, and the system records the real regulation and control and use.
And S105, acquiring the environmental data of the air conditioning equipment at the next moment, returning to the step of determining the temperature of the air conditioning equipment at the next moment by utilizing the automatic adjustment model of the air conditioning equipment according to the environmental data at the current moment.
Fig. 7 is a schematic structural diagram of an automatic control system of an air conditioning apparatus according to the present invention, and as shown in fig. 7, the automatic control system of an air conditioning apparatus according to the present invention includes: a historical data acquisition module 701, an automatic adjustment model determination module 702, an environmental data acquisition module 703 at the current moment, an automatic temperature adjustment module 704 and an automatic control module 705.
The historical data acquisition module 701 is used for acquiring historical time data of the air conditioning equipment and corresponding environmental data of the historical time; the environment data comprises outdoor air temperature, outdoor relative humidity, indoor relative humidity, precipitation condition, air quality index and wind speed at historical time; the historical moment data comprises working conditions, working states and set temperatures of the air conditioning equipment at the corresponding moment under the environment data; the working condition comprises heating or cooling; the working state comprises opening or closing.
The automatic adjustment model determination module 702 is configured to determine an automatic adjustment model of the air conditioning equipment by using a random forest model according to the historical time data of the air conditioning equipment and the environmental data of the corresponding historical time; the automatic adjusting model takes the environmental data at the current moment as input and takes the temperature at the next moment of the air conditioning equipment as output; the automatic adjusting model comprises a working condition prediction model, a working state prediction model and a temperature prediction model of the air conditioning equipment; the working condition prediction model of the air conditioning equipment takes the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity and the time as input and takes the working condition of the air conditioning equipment as output; the working state prediction model of the air conditioning equipment takes environmental data at the current moment and working conditions at the current moment as input, and takes the working state of the air conditioning equipment at the next moment as output; the temperature prediction model of the air conditioning equipment takes environmental data at the current moment, working conditions at the current moment and working states at the current moment as input, and takes the temperature at the next moment of the air conditioning equipment as output.
The current environmental data obtaining module 703 is configured to obtain current environmental data of the air conditioning device.
The temperature automatic adjustment module 704 is configured to determine the temperature of the air conditioning equipment at the next time by using an automatic adjustment model of the air conditioning equipment according to the environmental data at the current time.
The automatic control module 705 is configured to obtain environmental data of the air conditioning device at a next time, return the environmental data according to the current time, and determine a temperature of the air conditioning device at the next time by using an automatic adjustment model of the air conditioning device.
The invention provides an automatic control system of air conditioning equipment, which further comprises: and a matrix determination module.
The matrix determining module is used for determining a time-varying matrix of the historical moment data and the corresponding historical moment environmental data of the air conditioning equipment according to the historical moment data and the corresponding historical moment environmental data of the air conditioning equipment.
The automatic adjustment model determining module specifically includes: the device comprises a working condition prediction model determining unit, a working state prediction model determining unit and a temperature prediction model determining unit.
The working condition prediction model determining unit is used for determining the working condition prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity and the indoor relative humidity of the air conditioning equipment at the historical moment and the working conditions at the corresponding moments.
And the working state prediction model determining unit is used for determining a working state prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data of the historical moment and the working condition and the working state of the air conditioning equipment at the corresponding moment.
And the temperature prediction model determining unit is used for determining a temperature prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data at the historical moment and the working condition, the working state and the set temperature of the air conditioning equipment at the corresponding moment.
The working state prediction model determining unit specifically includes: the device comprises an operating state first prediction model determining subunit, an operating state second prediction model determining subunit and an operating state prediction model determining subunit.
And the working state first prediction model determining subunit is used for determining a working state first prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition and the working state at the historical moment when the working condition is heating.
And the working state second prediction model determining subunit is used for determining a working state second prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition, the air quality index, the wind speed and the working state at the historical moment when the working condition is cooling.
The working state prediction model determining subunit is configured to determine the working state prediction model according to the working state first prediction model and the working state second prediction model.
The automatic control method and the system for the air conditioning equipment have strong universality and wide application range. Since the behavior habits of the occupants vary greatly, the thermal comfort preference of the occupants, the building performance and the equipment performance of the room, and the like are also different. Therefore, the invention is not limited by the geographical position of the building, the house type, the room function, the personnel number and the like, can effectively establish the relevant model of the air conditioning regulation behavior of the residents, and realizes the behavior prediction of the residents.
The prediction result of the invention can sufficiently reflect the personal habit preference and the thermal comfort requirement of the resident, can carry out dynamic prediction along with the change of the behavior habit of the resident, and has strong capability of adapting to the behavior change of the resident. The model is established based on historical data of air conditioning regulation behaviors of room residents, the historical data contains living work and rest, habit preference, thermal comfort requirements and the like of the residents, and the machine learning algorithm can effectively extract the information so as to reflect the information in a prediction result. Meanwhile, the method is used for downloading and updating the modeled data set from the cloud platform in real time and dynamically changing along with time, so that the prediction method can dynamically reflect the personal habit preference and the change of the demand of a resident.
The modeling and predicting method is simple and convenient to operate, high in practicability, simple in model, powerful in function and practical. By means of the smart home platform and the indoor and outdoor environment monitoring system for acquiring data, the system can automatically complete the establishment of a behavior model and the behavior prediction without personnel operation. Meanwhile, the model is simple and practical, the input and output variables of the model are easy to measure and obtain, required parameters can be acquired by the smart home platform, the model can reflect the influence of real physical environment, time factors and the like on the behavior of the residents, and the practical significance is high. The system can automatically realize the prediction of the cooling and heating working conditions of the room air-conditioning equipment, the prediction of the cooling or heating behaviors of the air-conditioning equipment switched on and off by the residents and the prediction of the heating and cooling temperature regulation behaviors of the residents at one time, and has comprehensive functions.
By adopting the invention to model and predict the behavior of the residents, the personal rights and interests of the users can be effectively ensured, and the problem of exposure of personal identity information of the users can not be involved. The method is characterized in that the room is used as a research unit for modeling, information difference of residents in each room in a general model does not need to be eliminated, acquisition of personal identity information of the residents can be reduced, and personal privacy interests of users can be protected better.
Through practical case verification, the method has the advantages of good prediction accuracy and high efficiency, can meet the real-time and accurate prediction requirements on the air conditioning regulation behavior of residents, and has good model stability. This is determined by a random forest algorithm, a modeling and prediction method. The random forest algorithm-based model can meet the requirements for real-time and accurate prediction of the behavior of the residents, and has strong practical significance and application value.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (9)

1. An automatic control method of an air conditioning device is characterized by comprising the following steps:
acquiring data of historical moments of air conditioning equipment and environmental data of corresponding historical moments; the environment data comprises outdoor air temperature, outdoor relative humidity, indoor relative humidity, precipitation condition, air quality index and wind speed at historical time; the historical moment data comprises working conditions, working states and set temperatures of the air conditioning equipment at the corresponding moment under the environment data; the working condition comprises heating or cooling; the working state comprises opening or closing;
determining an automatic adjustment model of the air conditioning equipment by adopting a random forest model according to the data of the historical moment of the air conditioning equipment and the environmental data of the corresponding historical moment; the automatic adjusting model takes the environmental data at the current moment as input and takes the temperature at the next moment of the air conditioning equipment as output; the automatic adjusting model comprises a working condition prediction model, a working state prediction model and a temperature prediction model of the air conditioning equipment; the working condition prediction model of the air conditioning equipment takes the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity and the time as input and takes the working condition of the air conditioning equipment as output; the working state prediction model of the air conditioning equipment takes environmental data at the current moment and working conditions at the current moment as input, and takes the working state of the air conditioning equipment at the next moment as output; the temperature prediction model of the air conditioning equipment takes environmental data at the current moment, working conditions at the current moment and working states at the current moment as input, and takes the temperature at the next moment of the air conditioning equipment as output;
acquiring environmental data of the air conditioning equipment at the current moment;
determining the temperature of the air conditioning equipment at the next moment by utilizing an automatic adjusting model of the air conditioning equipment according to the environmental data at the current moment;
and obtaining the environmental data of the air conditioning equipment at the next moment, returning the environmental data according to the current moment, and determining the temperature of the air conditioning equipment at the next moment by using an automatic adjustment model of the air conditioning equipment.
2. The automatic control method of the air conditioning equipment according to claim 1, wherein the obtaining of the data of the historical time of the air conditioning equipment and the environmental data of the corresponding historical time further comprises:
and determining a time-varying matrix of the historical time data and the corresponding historical time environmental data of the air-conditioning equipment according to the historical time data and the corresponding historical time environmental data of the air-conditioning equipment.
3. The automatic control method of the air conditioning equipment according to claim 1, wherein the determining the automatic adjustment model of the air conditioning equipment by using a random forest model according to the historical time data of the air conditioning equipment and the corresponding historical time environmental data specifically comprises:
determining a working condition prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity and the indoor relative humidity of the air conditioning equipment at the historical moment and the working conditions at the corresponding moments;
determining a working state prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data at the historical moment and the working condition and the working state of the air conditioning equipment at the corresponding moment;
and determining a temperature prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data at the historical moment, the working condition, the working state and the set temperature of the air conditioning equipment at the corresponding moment.
4. The automatic control method of the air conditioning equipment according to claim 3, wherein the determining of the working state prediction model of the air conditioning equipment by using a random forest model according to the environmental data of the historical time and the working condition and the working state of the air conditioning equipment at the corresponding time specifically comprises:
when the working condition is heating, determining a first prediction model of the working state of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition and the working state at the historical moment;
when the working condition is cooling, determining a second prediction model of the working state of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition, the air quality index, the wind speed and the working state at the historical moment;
and determining the working state prediction model according to the working state first prediction model and the working state second prediction model.
5. The automatic control method of air conditioning equipment as claimed in claim 1, wherein the random forest model comprises 20 base learners.
6. An automatic control system for air conditioning equipment, comprising:
the historical data acquisition module is used for acquiring data of historical moments of the air conditioning equipment and environmental data of corresponding historical moments; the environment data comprises outdoor air temperature, outdoor relative humidity, indoor relative humidity, precipitation condition, air quality index and wind speed at historical time; the historical moment data comprises working conditions, working states and set temperatures of the air conditioning equipment at the corresponding moment under the environment data; the working condition comprises heating or cooling; the working state comprises opening or closing;
the automatic adjustment model determining module is used for determining an automatic adjustment model of the air conditioning equipment by adopting a random forest model according to the historical moment data of the air conditioning equipment and the corresponding historical moment environmental data; the automatic adjusting model takes the environmental data at the current moment as input and takes the temperature at the next moment of the air conditioning equipment as output; the automatic adjusting model comprises a working condition prediction model, a working state prediction model and a temperature prediction model of the air conditioning equipment; the working condition prediction model of the air conditioning equipment takes the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity and the time as input and takes the working condition of the air conditioning equipment as output; the working state prediction model of the air conditioning equipment takes environmental data at the current moment and working conditions at the current moment as input, and takes the working state of the air conditioning equipment at the next moment as output; the temperature prediction model of the air conditioning equipment takes environmental data at the current moment, working conditions at the current moment and working states at the current moment as input, and takes the temperature at the next moment of the air conditioning equipment as output;
the current-time environmental data acquisition module is used for acquiring current-time environmental data of the air conditioning equipment;
the temperature automatic adjusting module is used for determining the temperature of the air conditioning equipment at the next moment by utilizing an automatic adjusting model of the air conditioning equipment according to the environmental data at the current moment;
and the automatic control module is used for acquiring the environmental data of the air conditioning equipment at the next moment, returning the environmental data according to the current moment and determining the temperature of the air conditioning equipment at the next moment by using an automatic adjustment model of the air conditioning equipment.
7. An automatic control system of air conditioning equipment according to claim 6, characterized by further comprising:
and the matrix determining module is used for determining a time-varying matrix of the historical moment data and the corresponding historical moment environmental data of the air conditioning equipment according to the historical moment data and the corresponding historical moment environmental data of the air conditioning equipment.
8. The automatic control system of air conditioning equipment according to claim 6, wherein the automatic adjustment model determining module specifically comprises:
the working condition prediction model determining unit is used for determining a working condition prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity and the indoor relative humidity of the air conditioning equipment at the historical moment and the working conditions at the corresponding moments;
the working state prediction model determining unit is used for determining a working state prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data of the historical moment and the working condition and the working state of the air conditioning equipment at the corresponding moment;
and the temperature prediction model determining unit is used for determining the temperature prediction model of the air conditioning equipment by adopting a random forest model according to the environmental data of the historical moment and the working condition, the working state and the set temperature of the air conditioning equipment at the corresponding moment.
9. The automatic control system of air conditioning equipment according to claim 8, wherein the operating state prediction model determining unit specifically includes:
the working state first prediction model determining subunit is used for determining a working state first prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition and the working state at the historical moment when the working condition is heating;
the working state second prediction model determining subunit is used for determining a working state second prediction model of the air conditioning equipment by adopting a random forest model according to the outdoor air temperature, the outdoor relative humidity, the indoor relative humidity, the precipitation condition, the air quality index, the wind speed and the working state at the historical moment when the working condition is cooling;
and the working state prediction model determining subunit is used for determining the working state prediction model according to the working state first prediction model and the working state second prediction model.
CN202010505056.8A 2020-06-05 2020-06-05 Automatic control method and system for air conditioning equipment Active CN111649465B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010505056.8A CN111649465B (en) 2020-06-05 2020-06-05 Automatic control method and system for air conditioning equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010505056.8A CN111649465B (en) 2020-06-05 2020-06-05 Automatic control method and system for air conditioning equipment

Publications (2)

Publication Number Publication Date
CN111649465A true CN111649465A (en) 2020-09-11
CN111649465B CN111649465B (en) 2022-04-08

Family

ID=72344172

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010505056.8A Active CN111649465B (en) 2020-06-05 2020-06-05 Automatic control method and system for air conditioning equipment

Country Status (1)

Country Link
CN (1) CN111649465B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112963946A (en) * 2021-02-26 2021-06-15 南京邮电大学 Heating, ventilating and air conditioning system control method and device for shared office area
CN113325762A (en) * 2021-05-25 2021-08-31 西安交通大学 Intelligent building personalized energy utilization control method, system, device and equipment
CN116774749A (en) * 2023-08-25 2023-09-19 北京拓普尔通信技术有限公司 Intelligent temperature-control electric power cabinet

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108990383A (en) * 2018-08-15 2018-12-11 北京建筑大学 A kind of data center's air-conditioning system forecast Control Algorithm
CN109405195A (en) * 2018-10-31 2019-03-01 四川长虹电器股份有限公司 Air conditioner intelligent control system and method
CN110030704A (en) * 2019-05-20 2019-07-19 珠海格力电器股份有限公司 Control method, device, storage medium and the air-conditioning of air-conditioning
US20190293318A1 (en) * 2018-03-23 2019-09-26 Carrier Corporation Prognostics system and method for hvac system comfort failure forecasting
CN110425698A (en) * 2019-08-19 2019-11-08 同济大学 A kind of air conditioning control method and device of user preference self study
CN110494697A (en) * 2017-03-30 2019-11-22 三星电子株式会社 Data learning server and for generate and using its learning model method
CN110836509A (en) * 2018-08-17 2020-02-25 珠海格力电器股份有限公司 Household equipment control method and device, control equipment and readable storage medium
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

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110494697A (en) * 2017-03-30 2019-11-22 三星电子株式会社 Data learning server and for generate and using its learning model method
US20190293318A1 (en) * 2018-03-23 2019-09-26 Carrier Corporation Prognostics system and method for hvac system comfort failure forecasting
CN108990383A (en) * 2018-08-15 2018-12-11 北京建筑大学 A kind of data center's air-conditioning system forecast Control Algorithm
CN110836509A (en) * 2018-08-17 2020-02-25 珠海格力电器股份有限公司 Household equipment control method and device, control equipment and readable storage medium
CN109405195A (en) * 2018-10-31 2019-03-01 四川长虹电器股份有限公司 Air conditioner intelligent control system and method
CN110030704A (en) * 2019-05-20 2019-07-19 珠海格力电器股份有限公司 Control method, device, storage medium and the air-conditioning of air-conditioning
CN110425698A (en) * 2019-08-19 2019-11-08 同济大学 A kind of air conditioning control method and device of user preference self study
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

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112963946A (en) * 2021-02-26 2021-06-15 南京邮电大学 Heating, ventilating and air conditioning system control method and device for shared office area
CN112963946B (en) * 2021-02-26 2022-06-17 南京邮电大学 Heating, ventilating and air conditioning system control method and device for shared office area
CN113325762A (en) * 2021-05-25 2021-08-31 西安交通大学 Intelligent building personalized energy utilization control method, system, device and equipment
CN116774749A (en) * 2023-08-25 2023-09-19 北京拓普尔通信技术有限公司 Intelligent temperature-control electric power cabinet
CN116774749B (en) * 2023-08-25 2023-11-03 北京拓普尔通信技术有限公司 Intelligent temperature-control electric power cabinet

Also Published As

Publication number Publication date
CN111649465B (en) 2022-04-08

Similar Documents

Publication Publication Date Title
CN111649465B (en) Automatic control method and system for air conditioning equipment
CN110298487B (en) Indoor temperature prediction method for meeting personalized demands of users
CN110836514B (en) Control method and device of air conditioning unit
CN113110082B (en) Method and device for controlling household appliance and household appliance
CN112050397A (en) Method and system for regulating and controlling temperature of machine room
CN108917111A (en) A kind of intelligent air conditioner and its control method
CN104133427A (en) Intelligent household control method and system
CN111486554B (en) Air conditioner temperature non-sensitive control method based on online learning
CN109405195A (en) Air conditioner intelligent control system and method
CN110726229A (en) Control method and device of air conditioner, storage medium and processor
CN113325762B (en) Intelligent building personalized energy consumption control method, system, device and equipment
CN111623498A (en) Automatic air conditioner temperature determination method and system
KR102369350B1 (en) Predicting System of Energy Consumption and Data of Air Conditioning Equipment Using Artificial Neural Network and Method Thereof
CN109883016A (en) A kind of air pleasant degree adjusting method and equipment
CN114838470A (en) Control method and system for heating, ventilating and air conditioning
CN116774599A (en) Intelligent equipment control method based on knowledge graph, computer device and computer readable storage medium
CN112015106A (en) Smart home control system and control method
CN108036473B (en) Intelligent temperature and humidity control method and device
CN110726216B (en) Air conditioner, control method, device and system thereof, storage medium and processor
CN115758680A (en) Permanent magnet direct-drive low-speed large fan and central air conditioning system linkage regulation and control method and system
CN203443020U (en) Multi-dimensional comfort level indoor environment control system based on learning of user behaviors
CN110598916A (en) Method and system for constructing user behavior model
Chaudhary et al. Predicting the performance of hybrid ventilation in buildings using a multivariate attention-based biLSTM Encoder–Decoder
CN113268098A (en) Indoor environment regulation and control method and system
Wang et al. Dynamic and real-time simulation of BMS and air-conditioning system as a ‘living’environment for learning/training

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant