CN112257779A - Method for acquiring self-learning working condition parameters of central air conditioner - Google Patents
Method for acquiring self-learning working condition parameters of central air conditioner Download PDFInfo
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- CN112257779A CN112257779A CN202011137831.5A CN202011137831A CN112257779A CN 112257779 A CN112257779 A CN 112257779A CN 202011137831 A CN202011137831 A CN 202011137831A CN 112257779 A CN112257779 A CN 112257779A
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- 238000000034 method Methods 0.000 title claims abstract description 15
- 238000004378 air conditioning Methods 0.000 claims abstract description 63
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 34
- 238000012417 linear regression Methods 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims abstract description 4
- 238000001914 filtration Methods 0.000 claims abstract description 4
- 238000012216 screening Methods 0.000 claims abstract description 4
- 238000010586 diagram Methods 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 10
- 239000000498 cooling water Substances 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 abstract description 3
- 238000004134 energy conservation Methods 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 238000007710 freezing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
- G06F18/24143—Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
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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
- F24F11/64—Electronic processing using pre-stored data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Abstract
The invention provides a method for acquiring self-learning working condition parameters of a central air conditioner, aiming at the technical problems in the prior art, and the method is characterized in that a data acquisition unit is connected with the air conditioner to acquire the operating parameters of air conditioning unit equipment; the data acquisition unit is used for filtering and screening the acquired data after completing data acquisition, obtaining the real-time chilled water flow of the air conditioning unit through calculation, and then calculating the real-time cold quantity value of the air conditioning unit so as to obtain the cold quantity data of the air conditioning unit and the load value of the air conditioning unit; the data acquisition unit sends the acquired data to the self-learning module and stores the data according to a table form, and when the quantity group number of the data stored by the self-learning module reaches a set threshold value, the self-learning module analyzes the stored data by adopting a K neighbor algorithm and a multiple linear regression algorithm, so that necessary professional data analysis support is provided for deep energy-saving control of the air conditioning unit equipment.
Description
Technical Field
The invention relates to the technical field of air conditioner control, in particular to a method for acquiring self-learning working condition parameters of a central air conditioner.
Background
In the energy consumption ratio of public buildings, the energy consumption ratio of a central air-conditioning system is more than 50%, and the structure of the central air-conditioning system mainly comprises three parts: refrigerating unit, air conditioning water system and air conditioning wind system. The refrigerating unit occupies about 50% of the overall energy consumption of the air conditioning system, so that the energy conservation of the building is in energy conservation of the central air conditioner, and the energy conservation of the central air conditioner is mainly in the refrigerating unit. Because the working conditions of the central air conditioning unit during operation are different every moment, the knowledge of the variable working condition parameters of the central air conditioning unit is one of the key points of the energy-saving control of the air conditioning unit.
Most of central air conditioning intelligent energy-saving control systems in the market are not air conditioning equipment brand manufacturers, so that multi-working condition performance parameters of air conditioning unit equipment in a central air conditioning machine room are not known, or the obtained parameters are not comprehensive, and deep energy-saving control of the air conditioning unit equipment cannot be achieved.
Disclosure of Invention
The invention provides a method for acquiring the self-learning working condition parameters of a central air conditioner aiming at the technical problems in the prior art, which adopts the traditional automatic monitoring means to generate a multi-working condition parameter characteristic curve and a database of air conditioning unit equipment by analyzing the acquired running data of the air conditioning unit equipment through software big data and a self-learning function, thereby providing necessary professional data analysis support for the deep energy-saving control of the air conditioning unit equipment and enabling the deep energy-saving control of the intelligent energy-saving control system of the central air conditioner aiming at the third-party brand air conditioning unit equipment to be possible.
The technical scheme for solving the technical problems is as follows: the invention discloses a method for acquiring self-learning working condition parameters of a central air conditioner, which is characterized in that a data acquisition unit is connected with the air conditioner to acquire the operating parameters of air conditioning unit equipment; the data acquisition unit is used for filtering and screening the acquired data after completing data acquisition, obtaining the real-time chilled water flow of the air conditioning unit through calculation, and then calculating the real-time cold quantity value of the air conditioning unit so as to obtain the cold quantity data of the air conditioning unit and the load value of the air conditioning unit; the data acquisition unit sends the acquired data to a self-learning module and stores the data according to a table form, when the quantity group number of the data stored by the self-learning module reaches a set threshold value, the self-learning module analyzes the stored data by adopting a K nearest neighbor algorithm and a multiple linear regression algorithm, and the specific analysis steps are as follows: firstly, processing data according to a K nearest neighbor algorithm, and analyzing and accurately classifying and storing two adjacent groups of data of the same type; then processing by adopting a multiple linear regression algorithm; the self-learning module forms a multi-coordinate scatter diagram and a multi-working-condition performance curve diagram from data processed by the K-nearest neighbor algorithm and the multiple linear regression algorithm, and outputs the result to the central air-conditioning energy-saving control system to provide a control data basis for the central air-conditioning energy-saving control system.
Preferably, the data collector comprises an intelligent electric meter for monitoring the electric quantity and power of the air conditioning unit, a chilled water temperature sensor for collecting the inlet and outlet temperature of chilled water, a cooling water temperature sensor for collecting the inlet and outlet temperature of cooling water, and a water pressure difference sensor for collecting the inlet and outlet water pressure of chilled water.
Preferably, the threshold value of the number of sets of data amount stored by the self-learning module is set to be not less than 50 sets.
Preferably, the self-learning module comprises a database, and the data collector and the data in the data processing process are stored in the database.
The invention has the beneficial effects that: the invention provides a method for acquiring parameters of a central air conditioner self-learning working condition, which adopts the traditional automatic monitoring means to generate a characteristic curve and a database of multi-working condition parameters of air conditioner unit equipment by analyzing and self-learning the collected operating data of the air conditioner unit equipment through software big data, thereby providing necessary professional data analysis support for the deep energy-saving control of the air conditioner unit equipment and enabling the deep energy-saving control of a central air conditioner intelligent energy-saving control system aiming at the third-party brand air conditioner unit equipment to be possible.
Drawings
FIG. 1 is a first schematic diagram of a data storage form of the present invention;
FIG. 2 is a diagram of a second embodiment of the present invention;
FIG. 3 is a third schematic diagram of a data storage form according to the present invention;
FIG. 4 is a diagram of a fourth embodiment of the present invention;
FIG. 5 is a schematic flow chart of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the embodiment discloses a method for acquiring self-learning operating condition parameters of a central air conditioner, which is characterized in that a data acquisition unit is connected with an air conditioner to acquire operating parameters of air conditioning unit equipment; the data collector comprises an intelligent electric meter for monitoring the electric quantity and power of the air conditioning unit, a chilled water temperature sensor for collecting the inlet and outlet temperature of chilled water, a cooling water temperature sensor for collecting the inlet and outlet temperature of cooling water, and a water pressure difference sensor for collecting the inlet and outlet water pressure of the chilled water. The data acquisition unit is used for filtering and screening the acquired data after completing data acquisition, obtaining the real-time chilled water flow of the air conditioning unit through calculation, and then calculating the real-time cold quantity value of the air conditioning unit so as to obtain the cold quantity data of the air conditioning unit and the load value of the air conditioning unit; wherein the chilled water pressure difference is represented by the formula Δ P ═ S × Vs2(S is the ratio of the rated pressure difference square of the air conditioning unit to the rated flow), calculating the real-time chilled water flow Vs of the air conditioning unit, and calculating the formula Q (Q: heat load, Cp: constant pressure specific heat, r: specific gravity, Vs: water flow, Delta T: water temperature difference Delta T (T: T) by combining the chilled water inlet and outlet water temperature parameters and the cold quantityTemperature of inlet water-TTemperature of water outlet) Calculating the real-time cold quantity value of the air conditioning unit to obtain cold quantity data Q of the air conditioning unit to be stored; in addition, the data acquisition unit acquires the load value L of the air conditioning unit in a communication mode. The data collector is used for collecting and calculatingChilled water inlet temperature T of treated air conditioning unitFreezing inlet waterThe inlet water temperature T of the chilled waterFreezing water outletCooling water inlet temperature TCooling the water inlet,Cooling water outlet temperature TCold water inlet and outletThe load value L, the real-time cold quantity value Q and the electric power P are transmitted to the self-learning module, and the self-learning module classifies data according to 4 forms of figures 1 to 4 and stores the data into a database.
When the number of data volume groups stored by the self-learning module reaches a set threshold, the number of the same data group is set to be at least 50, the self-learning module analyzes the stored data by adopting a K-nearest neighbor algorithm and a multiple linear regression algorithm, and the specific analysis steps are as follows: firstly, processing data according to a K nearest neighbor algorithm, and analyzing and accurately classifying and storing two adjacent groups of data of the same type; then processing by adopting a multiple linear regression algorithm; the self-learning module forms a multi-coordinate scatter diagram and a multi-working-condition performance curve diagram from data processed by the K-nearest neighbor algorithm and the multiple linear regression algorithm, and outputs the result to the central air-conditioning energy-saving control system to provide a control data basis for the central air-conditioning energy-saving control system.
Specifically, firstly, the array between the two statistical columns in the database is processed by adopting a K nearest neighbor algorithm, and the data between the two statistical columns is analyzed and then accurately classified and stored, wherein the specific algorithm is as follows:
where x and y are data of adjacent 2 columns, respectively.
And (3) processing the data processed by adopting the k nearest neighbor algorithm by applying a multiple linear regression model algorithm, wherein the specific algorithm model is as follows:
Yi＝β0+β1X1i+β2X2i+…+βkXki+μi i＝1,2,…,n
where k is the number of explanatory variables, and β j (j ═ 1,2, …, k) is called a regression coefficient. The above expression is also referred to as a random expression of the overall regression function.
Its non-random expression is E (Y | X1i, X2i, … Xki,) β 0+ β 1X1i + β 2X2i + … + β kXki (β j is also called partial regression coefficient).
Wherein Y is the refrigerating capacity of the air conditioning unit, and X is the input power of the air conditioning unit.
The self-learning module generates a multi-coordinate scatter diagram (wherein the coordinate axes are respectively the X axis of the multi-coordinate scatter diagram which is the cooling water inlet temperature-chilled water outlet temperature, the Y axis of the multi-coordinate scatter diagram which is the air conditioning unit load, and the Z axis of the multi-coordinate scatter diagram which is the unit air conditioning unit refrigerating capacity/input power which is the air conditioning unit COP) from the database obtained after the 2 big data processing steps, and outputs the result to the central air conditioning intelligent energy-saving control system to serve as a data basis for the energy-saving control of the multi-condition performance parameters of the air conditioning unit, so that the deep energy-saving control of the central air conditioning intelligent energy-saving control system for the third-party brand. The multi-working-condition performance parameters of the air conditioning unit equipment are obtained by the self-learning method, so that the deep energy-saving control of the intelligent energy-saving control system of the central air conditioner aiming at the third-party brand air conditioning unit is realized, the users of the central air conditioner can be effectively helped to better realize energy and electricity saving, and the economic benefit is indirectly brought to the users.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A method for acquiring self-learning working condition parameters of a central air conditioner is characterized in that a data acquisition unit is connected with the air conditioner to acquire the operating parameters of air conditioning unit equipment; the data acquisition unit is used for filtering and screening the acquired data after completing data acquisition, obtaining the real-time chilled water flow of the air conditioning unit through calculation, and then calculating the real-time cold quantity value of the air conditioning unit so as to obtain the cold quantity data of the air conditioning unit and the load value of the air conditioning unit; the data acquisition unit sends the acquired data to a self-learning module and stores the data according to a table form, when the quantity group number of the data stored by the self-learning module reaches a set threshold value, the self-learning module analyzes the stored data by adopting a K nearest neighbor algorithm and a multiple linear regression algorithm, and the specific analysis steps are as follows: firstly, processing data according to a K nearest neighbor algorithm, and analyzing and accurately classifying and storing two adjacent groups of data of the same type; then processing by adopting a multiple linear regression algorithm; the self-learning module forms a multi-coordinate scatter diagram and a multi-working-condition performance curve diagram from data processed by the K-nearest neighbor algorithm and the multiple linear regression algorithm, and outputs the result to the central air-conditioning energy-saving control system to provide a control data basis for the central air-conditioning energy-saving control system.
2. The method as claimed in claim 1, wherein the data collector comprises a smart meter for monitoring electric quantity and power of the air conditioning unit, a chilled water temperature sensor for collecting chilled water inlet and outlet temperature, a cooling water temperature sensor for collecting cooling water inlet and outlet temperature, and a water pressure difference sensor for collecting chilled water inlet and outlet pressure.
3. The method as claimed in claim 2, wherein the threshold for the number of sets of data stored by the self-learning module is not less than 50.
4. The method as claimed in claim 3, wherein the self-learning module comprises a database, and the data collector and the data during the data processing are stored in the database.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114484731A (en) * | 2021-12-27 | 2022-05-13 | 浙江英集动力科技有限公司 | Method and device for diagnosing faults of central air conditioner based on stacking fusion algorithm |
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