CN112665125A - Air conditioning system control method and device and central air conditioner - Google Patents
Air conditioning system control method and device and central air conditioner Download PDFInfo
- Publication number
- CN112665125A CN112665125A CN202011464942.7A CN202011464942A CN112665125A CN 112665125 A CN112665125 A CN 112665125A CN 202011464942 A CN202011464942 A CN 202011464942A CN 112665125 A CN112665125 A CN 112665125A
- Authority
- CN
- China
- Prior art keywords
- time
- load prediction
- load
- prediction model
- air conditioning
- 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.)
- Pending
Links
Images
Landscapes
- Air Conditioning Control Device (AREA)
Abstract
The air conditioning system control method comprises the steps of constructing a time-by-time load prediction model, calculating an actual system load value, correcting the time-by-time load prediction model according to the actual system load to obtain comprehensive load prediction data, and controlling the system to operate according to the comprehensive load prediction data. The method and the device can realize actual and accurate time-by-time load prediction, are used for guiding the operation strategy of the wind-water linkage system of the central air conditioner, effectively improve the load hysteresis, improve the system stability and reduce the system energy consumption.
Description
Technical Field
The application belongs to the technical field of air conditioners, and particularly relates to an air conditioner system control method and device and a central air conditioner.
Background
The central air-conditioning system is a complex system with large time lag and numerous control intermediate links, and has key technical problems of overlong dynamic process, oscillating evaporation temperature, serious overshoot of controlled variables and the like in the group control of the central air-conditioning system. The load prediction control strategy in the control method of the air conditioning system is a feedforward control strategy, can effectively improve the hysteresis phenomenon of group control, and improves the stability and the high efficiency of the system. However, in the traditional air conditioning system control method, the load prediction result precision is low, the hysteresis of the prediction process is serious, the overshoot of the controlled variable is serious, the system stability is influenced, and the energy consumption waste of the system can be caused.
Disclosure of Invention
In order to overcome the problems that the accuracy of a load prediction result is low, the hysteresis of a prediction process is serious, the overshoot of a controlled variable is serious, the stability of a system is influenced and the energy consumption of the system is wasted in an air conditioning system control method in the traditional air conditioning system control method at least to a certain extent, the application provides an air conditioning system control method, an air conditioning system control device and a central air conditioner.
In a first aspect, the present application provides an air conditioning system control method, including:
constructing a time-by-time load prediction model;
calculating an actual system load value;
correcting the hourly load prediction model according to the actual system load to obtain comprehensive load prediction data;
and controlling the system to operate according to the comprehensive load prediction data.
Further, before constructing the time-by-time load prediction model, the method further comprises:
and acquiring system operation influence factors, wherein the influence factors comprise one or more of outdoor temperature and humidity, fresh air volume, equipment start and stop conditions and passenger flow.
Further, the constructing of the time-by-time load prediction model includes:
carrying out stabilization treatment on the influence factors;
and taking the influence factors after the stabilization processing as a time-by-time load prediction model training set, and training a time-by-time load prediction model by using the time-by-time load prediction model training set.
Further, the smoothing the influencing factors includes:
and carrying out differential smoothing process processing on the time series through differential transformation.
Further, the correcting the hourly load prediction model according to the actual system load to obtain comprehensive load prediction data includes:
calculating the difference value between the actual system load value and the preliminary load value output by the time-by-time load prediction model to obtain a real-time error;
calculating a load prediction error estimation value according to the real-time error;
and feeding back the load prediction error estimation value to the time-by-time load prediction model so that the corrected time-by-time load prediction model outputs comprehensive load prediction data.
Further, said controlling system operation based on said integrated load forecast data comprises:
judging whether the difference value between the current comprehensive load prediction data and the actual system load value at the last moment is greater than a preset difference value threshold value or not;
if so, adjusting the set value of the system operation parameter;
otherwise, maintaining the set value of the current system operation parameter.
Further, the system operating parameters include: one or more of the supply air temperature, the return air temperature and the cold machine water outlet temperature.
Further, after acquiring the system operation influencing factors, the method further comprises:
judging the effectiveness of the operation influence factors of the acquisition system;
if the current data is valid, recording the current data, and adding the current data to the end of the data set;
otherwise, deleting the current data and deleting the corresponding serial number used for identifying the current data record.
In a second aspect, the present application provides an air conditioning system control apparatus comprising:
the construction module is used for constructing a time-by-time load prediction model;
the calculation module is used for calculating the actual system load value;
the correction module is used for correcting the hourly load prediction model according to the actual system load to obtain comprehensive load prediction data;
and the control module is used for controlling the operation of the system according to the comprehensive load prediction data.
In a third aspect, the present application provides a central air conditioner, comprising:
the air conditioning system control device according to the second aspect.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
the air conditioning system control method comprises the steps of constructing a time-by-time load prediction model, calculating an actual system load value, correcting the time-by-time load prediction model according to the actual system load to obtain comprehensive load prediction data, and controlling the system to operate according to the comprehensive load prediction data, so that the time-by-time load prediction which is in line with the actual accuracy can be realized, the time-by-time load prediction is used for guiding the operation strategy of the wind-water linkage system of the central air conditioner, the load hysteresis phenomenon is effectively improved, the system stability is improved, and the system energy consumption is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of an air conditioning system control method according to an embodiment of the present application.
Fig. 2 is a flowchart of an air conditioning system control method according to another embodiment of the present application.
Fig. 3 is a flowchart of an air conditioning system control method according to another embodiment of the present application.
Fig. 4 is a flowchart of an air conditioning system control method according to another embodiment of the present application.
Fig. 5 is a flowchart of an air conditioning system control method according to another embodiment of the present application.
Fig. 6 is a functional block diagram of an air conditioning system control device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a flowchart of an air conditioning system control method according to an embodiment of the present application, and as shown in fig. 1, the air conditioning system control method includes:
s11: constructing a time-by-time load prediction model;
s12: calculating an actual system load value;
s13: correcting the hourly load prediction model according to the actual system load to obtain comprehensive load prediction data;
s14: and controlling the system to operate according to the comprehensive load prediction data.
In the traditional air conditioning system control method, the load prediction result is low in precision, the hysteresis of the prediction process is serious, the overshoot of the controlled variable is serious, the stability of the system is influenced, and the energy consumption of the system is wasted.
In the embodiment, the hourly load prediction model is constructed, the actual system load value is calculated, the hourly load prediction model is corrected according to the actual system load to obtain comprehensive load prediction data, and the system operation is controlled according to the comprehensive load prediction data, so that the actual and accurate hourly load prediction can be realized, the actual and accurate hourly load prediction can be used for guiding the operation strategy of the wind-water linkage system of the central air conditioner, the load hysteresis is effectively improved, the system stability is improved, and the system energy consumption is reduced.
An embodiment of the present application provides another air conditioning system control method, as shown in a flowchart of fig. 2, the air conditioning system control method includes:
s21: collecting system operation influence factors;
in some embodiments, the influencing factors include, but are not limited to, outdoor temperature and humidity, fresh air volume, equipment start and stop conditions, passenger flow volume, and the like.
S22: stabilizing the influence factors;
in some embodiments, smoothing the influencing factors includes:
the differential stationary process processing is carried out on the time series through differential transformation, and the following formula is shown:
Yt＝Xt－Xt-N
wherein, YtIs a differentiated stationary time sequence; x is a historical load value; n is 24, cycle time units (hours).
S23: and taking the influence factors after the stabilization processing as a time-by-time load prediction model training set, and training a time-by-time load prediction model by using the time-by-time load prediction model training set.
S24: calculating an actual system load value;
s25: calculating the difference value between the actual system load value and the preliminary load value output by the time-by-time load prediction model to obtain a real-time error;
s26: calculating a load prediction error estimation value according to the real-time error;
s27: and feeding back the load prediction error estimation value to the time-by-time load prediction model so that the corrected time-by-time load prediction model outputs comprehensive load prediction data.
S28: and controlling the system to operate according to the comprehensive load prediction data.
In some embodiments, controlling system operation based on the integrated load forecast data comprises:
s281: judging whether the difference value between the current comprehensive load prediction data and the actual system load value at the last moment is greater than a preset difference value threshold value or not;
s282: if so, adjusting the set value of the system operation parameter;
s283: otherwise, maintaining the set value of the current system operation parameter.
In some embodiments, the system operating parameters include: one or more of the supply air temperature, the return air temperature and the cold machine water outlet temperature.
As shown in fig. 3, in this embodiment, load prediction real-time control in an air conditioning system is implemented by combining data acquisition, load prediction model training, and a load prediction control algorithm, taking a wind-water linkage control system as an example, a specific control process of the apparatus includes:
data acquisition process
The collection system operation influence factors mainly comprise outdoor temperature and humidity, fresh air volume, equipment start and stop conditions, passenger flow and other factors.
Load prediction model construction process
And calculating a load predicted value at the next moment according to the load historical data, and simultaneously measuring and calculating the actual air conditioner cold load, wherein the load data taking 24 hours as a period presents a periodic motion trend, so that the staple food load prediction model takes hours as precision, and the prediction precision is more refined.
The accuracy of load prediction is improved by analyzing and processing the prediction error. And feeding back the real-time load prediction error estimation value to the time-by-time load prediction model, performing modeling and prediction again according to the load prediction error estimation value, and overlapping the error prediction result and the preliminary prediction result to obtain error-adjusted comprehensive load prediction data, so that the prediction result is more accurate.
And accumulating the load prediction error estimation values to form a data set, using the data set for fixedly recording data, training a load prediction algorithm by combining the data set, calculating according to load prediction input quantity, and outputting a load prediction value at the next moment or in the next period.
And calculating the load predicted value of the next control period by utilizing the time-by-time load prediction model to automatically adjust the set values of the air supply temperature and the air return temperature. When the load predicted value changes and the difference value between the load predicted value and the actual load value of the current control period is larger than the error set value, adjusting the set value of the air returning temperature and the set value of the water outlet temperature of the refrigerator, otherwise, keeping the set values of the air supplying temperature, the air returning temperature and the water outlet temperature of the refrigerator unchanged.
And executing a wind system control strategy and a water system control strategy according to the set values of the system operation parameters to realize wind-water linkage control.
It should be noted that, in the embodiment, the hourly load prediction is performed on other control systems except for the wind-water linkage control system, and the present application is not limited thereto.
In the embodiment, the time-by-time load prediction model is corrected in real time, comprehensive load prediction data is output, the set value of the system operation parameter is adjusted according to the comprehensive load prediction data, the time lag is reduced, and the optimization of the wind-water linkage control system is realized.
An embodiment of the present application provides another air conditioning system control method, as shown in a flowchart of fig. 4, the air conditioning system control method includes:
s41: judging the effectiveness of the operation influence factors of the acquisition system;
s42: if the current data is valid, recording the current data, and adding the current data to the end of the data set;
s43: otherwise, deleting the current data, and deleting the corresponding serial number for identifying the current data record
S44: and generating the valid data into a data set.
As shown in fig. 5, the load prediction data collection and processing process includes: the method comprises the steps of forming a data record after collecting system operation influence factors, judging the validity of the influence factor data after preprocessing the influence factors, deleting the first data record if the data are invalid, reducing the serial numbers of all the data records by one, and adding new data to the end of a data set if the data are valid.
In the embodiment, the data can be screened by judging whether the collected data is effective or not, the accuracy of the prediction result is ensured, and the data is processed to form a data set, so that the training set of the time-by-time load prediction model is increased, and the accuracy of the prediction result is further ensured.
Fig. 6 is a functional structure diagram of an air conditioning system control device according to an embodiment of the present application, and as shown in fig. 6, the air conditioning system control device includes:
a construction module 61, configured to construct a time-by-time load prediction model;
a calculation module 62 for calculating an actual system load value;
the correcting module 63 is used for correcting the hourly load prediction model according to the actual system load to obtain comprehensive load prediction data;
and a control module 64 for controlling system operation based on the integrated load forecast data.
In some embodiments, further comprising:
and the acquisition module 65 is used for acquiring system operation influence factors, wherein the influence factors comprise one or more of outdoor temperature and humidity, fresh air volume, equipment start and stop conditions and passenger flow volume.
A preprocessing module 66, configured to smooth the influencing factors.
In the embodiment, the hourly load prediction model is built through the building module, the actual system load value is calculated through the calculating module, the hourly load prediction model is corrected through the correcting module according to the actual system load to obtain comprehensive load prediction data, the control module controls the system to operate according to the comprehensive load prediction data, actual and accurate hourly load prediction can be achieved, the hourly load prediction model is used for guiding an operation strategy of a central air-conditioning wind-water linkage system, the load hysteresis phenomenon is effectively improved, the system stability is improved, and the system energy consumption is reduced.
An embodiment of the present application provides a central air conditioner, including:
the air conditioning system control device described above.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
It should be noted that the present invention is not limited to the above-mentioned preferred embodiments, and those skilled in the art can obtain other products in various forms without departing from the spirit of the present invention, but any changes in shape or structure can be made within the scope of the present invention with the same or similar technical solutions as those of the present invention.
Claims (10)
1. An air conditioning system control method, comprising:
constructing a time-by-time load prediction model;
calculating an actual system load value;
correcting the hourly load prediction model according to the actual system load to obtain comprehensive load prediction data;
and controlling the system to operate according to the comprehensive load prediction data.
2. The air conditioning system control method according to claim 1, before constructing the time-by-time load prediction model, further comprising:
and acquiring system operation influence factors, wherein the influence factors comprise one or more of outdoor temperature and humidity, fresh air volume, equipment start and stop conditions and passenger flow.
3. The air conditioning system control method according to claim 2, wherein the constructing of the time-by-time load prediction model includes:
carrying out stabilization treatment on the influence factors;
and taking the influence factors after the stabilization processing as a time-by-time load prediction model training set, and training a time-by-time load prediction model by using the time-by-time load prediction model training set.
4. The air conditioning system control method according to claim 3, wherein the smoothing of the influencing factors includes:
and carrying out differential smoothing process processing on the time series through differential transformation.
5. The air conditioning system control method according to claim 1, wherein the correcting the time-by-time load prediction model according to the actual system load to obtain comprehensive load prediction data comprises:
calculating the difference value between the actual system load value and the preliminary load value output by the time-by-time load prediction model to obtain a real-time error;
calculating a load prediction error estimation value according to the real-time error;
and feeding back the load prediction error estimation value to the time-by-time load prediction model so that the corrected time-by-time load prediction model outputs comprehensive load prediction data.
6. The air conditioning system control method according to claim 1, wherein the controlling the system operation according to the integrated load prediction data includes:
judging whether the difference value between the current comprehensive load prediction data and the actual system load value at the last moment is greater than a preset difference value threshold value or not;
if so, adjusting the set value of the system operation parameter;
otherwise, maintaining the set value of the current system operation parameter.
7. The air conditioning system control method according to claim 6, wherein the system operation parameters include: one or more of the supply air temperature, the return air temperature and the cold machine water outlet temperature.
8. The air conditioning system control method according to claim 2, wherein after collecting the system operation influencing factors, the method further comprises:
judging the effectiveness of the operation influence factors of the acquisition system;
if the current data is valid, recording the current data, and adding the current data to the end of the data set;
otherwise, deleting the current data and deleting the corresponding serial number used for identifying the current data record.
9. An air conditioning system control device, characterized by comprising:
the construction module is used for constructing a time-by-time load prediction model;
the calculation module is used for calculating the actual system load value;
the correction module is used for correcting the hourly load prediction model according to the actual system load to obtain comprehensive load prediction data;
and the control module is used for controlling the operation of the system according to the comprehensive load prediction data.
10. A central air conditioner, comprising: the air conditioning system control device according to claim 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011464942.7A CN112665125A (en) | 2020-12-14 | 2020-12-14 | Air conditioning system control method and device and central air conditioner |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011464942.7A CN112665125A (en) | 2020-12-14 | 2020-12-14 | Air conditioning system control method and device and central air conditioner |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112665125A true CN112665125A (en) | 2021-04-16 |
Family
ID=75405606
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011464942.7A Pending CN112665125A (en) | 2020-12-14 | 2020-12-14 | Air conditioning system control method and device and central air conditioner |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112665125A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113739397A (en) * | 2021-08-31 | 2021-12-03 | 广州汇电云联互联网科技有限公司 | Central air-conditioning system and energy-saving control method and readable storage medium thereof |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002022239A (en) * | 2000-07-11 | 2002-01-23 | Mitsubishi Heavy Ind Ltd | Load prediction device |
| CN102052739A (en) * | 2010-12-27 | 2011-05-11 | 重庆大学 | Central air conditioner intelligent control system based on wireless sensor network and method |
| CN106403207A (en) * | 2016-10-24 | 2017-02-15 | 珠海格力电器股份有限公司 | Load-prediction-based control system and method for heating ventilation air-conditioning system |
| CN109871987A (en) * | 2019-01-28 | 2019-06-11 | 中建八局第三建设有限公司 | A kind of intelligent building heating and ventilating equipment synthesis energy saving control method |
| WO2019114489A1 (en) * | 2017-12-15 | 2019-06-20 | Midea Group Co., Ltd. | Machine learning control of environmental systems |
| CN109945420A (en) * | 2019-03-26 | 2019-06-28 | 南京南瑞继保电气有限公司 | Air conditioning control method, device and computer storage medium based on load prediction |
-
2020
- 2020-12-14 CN CN202011464942.7A patent/CN112665125A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002022239A (en) * | 2000-07-11 | 2002-01-23 | Mitsubishi Heavy Ind Ltd | Load prediction device |
| CN102052739A (en) * | 2010-12-27 | 2011-05-11 | 重庆大学 | Central air conditioner intelligent control system based on wireless sensor network and method |
| CN106403207A (en) * | 2016-10-24 | 2017-02-15 | 珠海格力电器股份有限公司 | Load-prediction-based control system and method for heating ventilation air-conditioning system |
| WO2019114489A1 (en) * | 2017-12-15 | 2019-06-20 | Midea Group Co., Ltd. | Machine learning control of environmental systems |
| CN109871987A (en) * | 2019-01-28 | 2019-06-11 | 中建八局第三建设有限公司 | A kind of intelligent building heating and ventilating equipment synthesis energy saving control method |
| CN109945420A (en) * | 2019-03-26 | 2019-06-28 | 南京南瑞继保电气有限公司 | Air conditioning control method, device and computer storage medium based on load prediction |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113739397A (en) * | 2021-08-31 | 2021-12-03 | 广州汇电云联互联网科技有限公司 | Central air-conditioning system and energy-saving control method and readable storage medium thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111237989B (en) | Building ventilation air conditioner control method and device based on load prediction | |
| CN108317670B (en) | Refrigeration system energy-saving control method and system based on machine learning | |
| CN110781969B (en) | Air conditioner air volume control method, device and medium based on deep reinforcement learning | |
| CN109974231B (en) | Ice storage air conditioner ice amount planning method, device, equipment and medium | |
| CN109708267B (en) | Control method and system for electronic expansion valve of fresh air handling unit | |
| CN113739365A (en) | Central air-conditioning cold station group control energy-saving control method, device, equipment and storage medium | |
| CN105485853A (en) | Setting method and device of operation frequency of air conditioner compressor | |
| CN108320042A (en) | The optimization method and device of circulation | |
| CN113757952B (en) | Air conditioner control method and device and air conditioner | |
| CN114963414A (en) | Air conditioning system intelligent regulation and control device based on AI data analysis | |
| CN112696798A (en) | Parameter setting method of air conditioner, air conditioning apparatus, and computer-readable storage medium | |
| CN114154677A (en) | Air conditioner operation load model construction and prediction method, device, equipment and medium | |
| CN112665125A (en) | Air conditioning system control method and device and central air conditioner | |
| CN112611076A (en) | Subway station ventilation air conditioner energy-saving control system and method based on ISCS | |
| CN114440409A (en) | Self-adaptive energy-saving control method for central air-conditioning system | |
| CN113110056B (en) | Heat supply intelligent decision-making method and intelligent decision-making machine based on artificial intelligence | |
| CN112180733B (en) | Fuzzy logic-based building energy consumption system prediction control parameter setting method | |
| CN113722889A (en) | Energy efficiency online analysis system and method based on artificial intelligence | |
| CN110567092A (en) | temperature control method and system applied to air handling unit | |
| CN114011564B (en) | Coal mill control optimization method based on model offline planning | |
| CN113608443A (en) | Sewage treatment control method based on enhanced PI control | |
| CN117128618A (en) | Model-free heating ventilation air conditioner energy-saving control method based on maximum likelihood state estimation | |
| JP2992714B2 (en) | Banana ripening control method and device | |
| CN114626299A (en) | Control system based on data-driven aggregation air conditioner load external characteristic modeling | |
| CN115793444A (en) | APC (automatic Power control) and RTO (real time operation) online cooperation method for uranium enrichment auxiliary process air conditioning box system |
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 |