CN112344521A - Energy efficiency improving method for heating and refrigerating system - Google Patents
Energy efficiency improving method for heating and refrigerating system Download PDFInfo
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- CN112344521A CN112344521A CN202011022698.9A CN202011022698A CN112344521A CN 112344521 A CN112344521 A CN 112344521A CN 202011022698 A CN202011022698 A CN 202011022698A CN 112344521 A CN112344521 A CN 112344521A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
- F24F11/47—Responding to energy costs
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—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
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Abstract
The invention discloses a method for improving the energy efficiency of a heating and refrigerating system, which can achieve the aim of quickly responding to the adjustment requirement and comprises the following steps: s1: carrying out temperature rise and temperature reduction tests under different working conditions to obtain an energy storage evaluation coefficient of each area; s2: carrying out acceleration performance tests under different working conditions to obtain a dynamic acceleration curve of each index of each device in the air conditioner; s3: establishing a general control model by using a dynamic curve of equipment as an input and output training neural network through neural network modeling; s4: obtaining actual temperature adjusting time corresponding to the energy storage performance evaluation coefficient according to actual data in the region, and obtaining an energy storage correction coefficient according to the actual temperature adjusting time and the system temperature adjusting time; s5: correcting the actual temperature adjusting time of each area through an energy storage correction coefficient; s6: and optimizing the adjusting mode of the air conditioner according to the corrected actual temperature adjusting time.
Description
Technical Field
The invention belongs to the technical field of air conditioner control.
Background
An air conditioner, i.e., an air conditioner, is a device that manually adjusts and controls parameters such as temperature, humidity, and flow rate of ambient air in a building or structure. The air conditioner is greatly convenient for people to live, so that people do not need to endure hot summer or cold winter. However, in the practical use of air conditioners, it is found that the conventional air conditioners still have certain disadvantages, for example, the functions of some buildings are special, the air conditioning system needs to respond quickly to the adjustment requirements, for example, places such as theaters, bathrooms and restaurants, which have large changes in people stream and high requirements for the cooling and heating performance of the indoor air conditioning system, and the requirements of some factory workshops for processes can generate severe fluctuation of cooling and heating loads, and have certain requirements for quick adjustment of the air conditioners.
In the prior art, the purpose of quickly adjusting the air conditioner can be achieved by some technical means, for example, the Chinese patent publication number is as follows: CN108302713B discloses a quick adjusting method for air conditioner settings, an air conditioner and a readable storage medium. The air conditioner rapid control method comprises the following steps: when a quick control instruction is detected, acquiring relevant operation parameters of an air conditioner appointed by the quick control instruction, and setting the relevant operation parameters as target parameters; acquiring identification information of a target air conditioner according to the quick control instruction, and acquiring operation parameters of the target air conditioner according to the identification information of the target air conditioner; and sending the target parameters to the target air conditioner, and controlling the target air conditioner to quickly adjust the related parameters according to the target parameters. According to the invention, the operation parameters of the plurality of air conditioners are quickly adjusted by taking the operation parameters of the specified air conditioners as references, so that the aim of quickly controlling the air conditioners is fulfilled, the repeated operation of users is reduced, and the use experience of the users is improved. Although the air conditioner setting quick adjusting method can adjust the air conditioner, the air conditioner setting quick adjusting method only adjusts the temperature of the air conditioner and cannot specifically adjust the temperature to the area covered by the air conditioner, and the method has great limitation.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a heating and refrigerating system energy efficiency improving method which can quickly respond to the demand according to the area covered by an air conditioner.
In order to solve the technical problems, the invention adopts the following technical scheme: a method for improving energy efficiency of a heating and cooling system, comprising the steps of:
s1: carrying out temperature rise and temperature reduction tests under different working conditions aiming at different air conditioner coverage areas to obtain an energy storage evaluation coefficient of each area;
s2: carrying out acceleration performance tests under different working conditions aiming at different air conditioner coverage areas to obtain a dynamic acceleration curve of each index of each device in the air conditioner;
s3: establishing a general control model by using a dynamic curve of equipment as an input and output training neural network through neural network modeling;
s4: obtaining actual temperature adjusting time corresponding to the energy storage performance evaluation coefficient according to actual data in the region, and obtaining an energy storage correction coefficient according to the actual temperature adjusting time and the system temperature adjusting time;
s5: correcting the actual temperature adjusting time of each area through an energy storage correction coefficient;
s6: and optimizing the adjusting mode of the air conditioner according to the corrected actual temperature adjusting time.
Preferably, the different working conditions in the steps S1 and S2 are external environments with different temperatures, including six typical working conditions of outdoor temperature of 35 ℃, 30 ℃, 25 ℃, 5 ℃ and 15 ℃ in summer.
Preferably, the energy storage evaluation coefficient is
TmaxiRepresents the maximum value of the temperature change T of each area covered by the air conditioner within a fixed time under a certain working conditioniRepresenting the temperature change in a fixed time under a certain condition in a certain area, the fixed time is set to 10 minutes.
Preferably, the dynamic acceleration curve of each index of each device in step S2 includes a water pump head dynamic acceleration curve, a water pump flow dynamic acceleration curve, a host cooling capacity dynamic acceleration curve, a host water supply temperature dynamic acceleration curve, and a host water return temperature dynamic acceleration curve.
Preferably, in step S3, the time for reaching the actual temperature adjustment of the system under different working conditions and the corresponding control parameters are obtained through a general control model.
Preferably, in step S4, the actual temperature adjustment time/the system temperature adjustment time is an energy storage correction coefficient.
Preferably, in step S5, the actual temperature adjustment time T ═ a ═ the system temperature adjustment time, and a is an energy storage correction coefficient.
Preferably, in step S6, the air conditioner is adjusted by T time ahead.
According to the technical scheme, the energy storage correction coefficient is obtained according to the actual temperature adjusting time and the system temperature adjusting time aiming at the specific area covered by the air conditioner, the actual temperature adjusting time can be corrected through the energy storage correction coefficient, more accurate actual temperature adjusting time is obtained, the adjusting mode of the air conditioner can be optimized through the corrected actual temperature adjusting time, the advance starting time of the air conditioner is set more accurately, the waste of resources is avoided, the adjustment of the air conditioner can be more accurate, the set temperature can be achieved in the area within the designated time, and the purpose of quickly responding to the adjustment requirement is achieved.
The following detailed description will explain the present invention and its advantages.
Drawings
The invention is further described with reference to the accompanying drawings and the detailed description below:
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments of the present invention, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. 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.
Referring to fig. 1, a method for improving energy efficiency of a heating and cooling system includes the following steps:
s1: carrying out temperature rise and temperature reduction tests under different working conditions aiming at different air conditioner coverage areas to obtain an energy storage evaluation coefficient of each area;
s2: carrying out acceleration performance tests under different working conditions aiming at different air conditioner coverage areas to obtain a dynamic acceleration curve of each index of each device in the air conditioner;
s3: establishing a general control model by using a dynamic curve of equipment as an input and output training neural network through neural network modeling;
s4: obtaining actual temperature adjusting time corresponding to the energy storage performance evaluation coefficient according to actual data in the region, and obtaining an energy storage correction coefficient according to the actual temperature adjusting time and the system temperature adjusting time;
s5: correcting the actual temperature adjusting time of each area through an energy storage correction coefficient;
s6: and optimizing the adjusting mode of the air conditioner according to the corrected actual temperature adjusting time.
Wherein, the different working conditions are external environments with different temperatures, including six typical working conditions of outdoor temperature of 35 ℃, 30 ℃, 25 ℃, 5 ℃ below zero and 15 ℃ in summer.
As a preferred embodiment of the present invention, the step S1 further includes the following steps:
s101: setting external environments with different temperatures;
s102: carrying out temperature rise and temperature drop tests on the area under external environments with different temperatures;
s103: and obtaining the energy storage evaluation coefficient of each area, wherein the highest energy storage evaluation coefficient is 100, and the lowest energy storage evaluation coefficient is 0.
The energy storage evaluation coefficient is
TmaxiRepresents the maximum value of the temperature change T of each area covered by the air conditioner within a fixed time under a certain working conditioniRepresenting the temperature change in a fixed time under a certain condition in a certain area, the fixed time is set to 10 minutes.
As a preferred embodiment of the present invention, the step S2 further includes the following steps:
s201: setting the internal environment of different temperatures;
s202: carrying out acceleration performance test on the air conditioner under the environment in different temperature areas;
s203: and obtaining a dynamic acceleration curve of each index of each device, wherein the dynamic acceleration curve comprises a water pump lift dynamic acceleration curve, a water pump flow dynamic acceleration curve, a host machine refrigerating capacity dynamic acceleration curve, a host machine water supply temperature dynamic acceleration curve and a host machine return water temperature dynamic acceleration curve.
As a preferred embodiment of the present invention, the step S3 further includes the following steps:
s301: establishing a general control model by using a dynamic curve of equipment as an input and output training neural network through neural network modeling;
the input data is a curve family formed by dynamic curves, and the output is a temperature change curve of different areas. For example, when the host machine refrigerating capacity acceleration performance is tested, the change curves of a water pump, a fan, water temperature and a valve can be obtained along with the change of time and are used as input parameters, the room temperature change in the period is used as output parameters, and the overall control model of the temperature adjusting time can be obtained through training.
S302: and obtaining the time for achieving the actual temperature adjustment of the system under different working conditions and corresponding control parameters through the overall control model.
The fastest temperature adjustment time and corresponding control parameters can be obtained by an exhaustion method or an intelligent optimization (genetic algorithm) method.
As a preferred embodiment of the present invention, the step S4 further includes the following steps:
s401: obtaining the actual temperature adjusting time corresponding to the energy storage evaluation coefficient according to the actual data in the region;
s402: and obtaining an energy storage correction coefficient through the actual temperature regulation time and the system temperature regulation time, wherein: and the actual temperature regulation time/system temperature regulation time is the energy storage correction coefficient.
As a preferred embodiment of the present invention, the step S5 further includes the following steps:
s501: setting an energy storage correction coefficient as a;
s502: and correcting the actual temperature regulation time of each region through an energy storage correction coefficient a, wherein the actual temperature regulation time is a system temperature regulation time.
As a preferred technical solution of the present invention, the specific step of optimizing the adjustment mode of the air conditioner further includes:
setting the corrected actual temperature adjusting time as T;
and adjusting the air conditioner in advance of T time.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in other forms without departing from the spirit or essential characteristics thereof. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.
Claims (8)
1. A heating and refrigerating system energy efficiency improving method is characterized in that: the method comprises the following steps:
s1: carrying out temperature rise and temperature reduction tests under different working conditions aiming at different air conditioner coverage areas to obtain an energy storage evaluation coefficient of each area;
s2: carrying out acceleration performance tests under different working conditions aiming at different air conditioner coverage areas to obtain a dynamic acceleration curve of each index of each device in the air conditioner;
s3: establishing a general control model by using a dynamic curve of equipment as an input and output training neural network through neural network modeling;
s4: obtaining actual temperature adjusting time corresponding to the energy storage performance evaluation coefficient according to actual data in the region, and obtaining an energy storage correction coefficient according to the actual temperature adjusting time and the system temperature adjusting time;
s5: correcting the actual temperature adjusting time of each area through an energy storage correction coefficient;
s6: and optimizing the adjusting mode of the air conditioner according to the corrected actual temperature adjusting time.
2. The method for improving the energy efficiency of a heating and cooling system according to claim 1, wherein: the different working conditions in the steps S1 and S2 are external environments with different temperatures, including six typical working conditions of outdoor temperature of 35 ℃, 30 ℃, 25 ℃, 5 ℃ below zero and 15 ℃ in summer.
3. The method for improving the energy efficiency of the heating and cooling system according to claim 2, wherein: the energy storage evaluation coefficient is
TmaxiRepresents the maximum value of the temperature change T of each area covered by the air conditioner within a fixed time under a certain working conditioniRepresenting the temperature change in a fixed time under a certain condition in a certain area, the fixed time is set to 10 minutes.
4. The method for improving the energy efficiency of the heating and cooling system according to claim 2, wherein: the dynamic acceleration curve of each index of each device in the step S2 includes a water pump lift dynamic acceleration curve, a water pump flow dynamic acceleration curve, a host refrigeration capacity dynamic acceleration curve, a host water supply temperature dynamic acceleration curve, and a host water return temperature dynamic acceleration curve.
5. The method for improving the energy efficiency of the heating and cooling system according to claim 2, wherein: and in the step S3, the time for reaching the actual temperature regulation of the system under different working conditions and corresponding control parameters are obtained through the overall control model.
6. The method for improving the energy efficiency of a heating and cooling system according to claim 1, wherein: in step S4, the actual temperature adjustment time/the system temperature adjustment time is an energy storage correction coefficient.
7. The method for improving the energy efficiency of the heating and cooling system according to claim 6, wherein the method comprises the following steps: in step S5, the actual temperature adjustment time T is a system temperature adjustment time, and a is an energy storage correction coefficient.
8. The method for improving energy efficiency of a heating and cooling system according to claim 7, wherein: in step S6, the air conditioner is adjusted by T time ahead.
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| CN202011022698.9A CN112344521A (en) | 2020-09-25 | 2020-09-25 | Energy efficiency improving method for heating and refrigerating system |
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| CN202011022698.9A CN112344521A (en) | 2020-09-25 | 2020-09-25 | Energy efficiency improving method for heating and refrigerating system |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWM255929U (en) * | 2004-04-19 | 2005-01-21 | Huei-Jiun Chen | Energy storage apparatus for building materials |
| CN109959123A (en) * | 2019-03-11 | 2019-07-02 | 浙江工业大学 | A kind of energy-saving method for air conditioner based on genetic algorithm and shot and long term memory Recognition with Recurrent Neural Network |
| CN110805997A (en) * | 2019-11-14 | 2020-02-18 | 中金新源（天津）科技有限公司 | Energy-saving control method for central air-conditioning system |
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2020
- 2020-09-25 CN CN202011022698.9A patent/CN112344521A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWM255929U (en) * | 2004-04-19 | 2005-01-21 | Huei-Jiun Chen | Energy storage apparatus for building materials |
| CN109959123A (en) * | 2019-03-11 | 2019-07-02 | 浙江工业大学 | A kind of energy-saving method for air conditioner based on genetic algorithm and shot and long term memory Recognition with Recurrent Neural Network |
| CN110805997A (en) * | 2019-11-14 | 2020-02-18 | 中金新源（天津）科技有限公司 | Energy-saving control method for central air-conditioning system |
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