CN110567101A - Water chiller high-energy-efficiency control method based on support vector machine model - Google Patents
Water chiller high-energy-efficiency control method based on support vector machine model Download PDFInfo
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- CN110567101A CN110567101A CN201910748293.4A CN201910748293A CN110567101A CN 110567101 A CN110567101 A CN 110567101A CN 201910748293 A CN201910748293 A CN 201910748293A CN 110567101 A CN110567101 A CN 110567101A
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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
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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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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2140/00—Control inputs relating to system states
- F24F2140/20—Heat-exchange fluid temperature
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- Combustion & Propulsion (AREA)
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Abstract
The invention discloses a water chiller high energy efficiency control method based on a support vector machine model, which comprises the following steps: step 1, selecting various operation parameters which obviously affect the energy efficiency ratio of a water chilling unit from the operation parameters of the water chilling unit, and collecting historical data of the operation parameters; step 2, normalizing historical data of various operating parameters and energy efficiency ratios of the water chilling unit, and acquiring multiple groups of training data based on the energy efficiency ratio model of the support vector machine through a clustering algorithm; step 3, establishing a water chilling unit energy efficiency ratio model by adopting a support vector machine method, taking historical operation parameters as the input of the model, and taking historical energy efficiency ratio data as the output of the model; and 4, predicting the energy consumption of the water chilling unit under a certain working condition in the future in advance by using the energy efficiency ratio model. The method can be used for mining the relation between the energy efficiency ratio and the key operation parameters, and building the energy efficiency ratio model of the water chilling unit by using the key operation parameters and the historical data of the energy efficiency ratio.
Description
Technical Field
The invention belongs to the field of energy-saving optimization control of a central air conditioner, and particularly relates to a water chiller high-energy-efficiency control method based on a support vector machine model.
background
With the increase of the total building amount and the pursuit of the improvement of living comfort of people in recent years, the building energy consumption of China currently accounts for more than 33 percent of the total social energy consumption every year and is still continuously increased. In the building energy consumption, the energy consumption of the central air-conditioning system accounts for about 50 percent; the water chilling unit is used as a refrigeration core device of the central air conditioner, and the energy consumption accounts for more than 40% of that of the central air conditioner. Therefore, the method has important significance for the research on the energy-saving potential of the water chilling unit.
The energy efficiency ratio of the water chilling unit refers to the refrigerating capacity of the refrigerating unit under unit power, and the energy conversion efficiency is directly reflected by the energy efficiency ratio. The energy efficiency ratio is influenced by a plurality of factors, firstly, the energy efficiency ratio is directly influenced by the unit, the structure of the unit, a heat exchange interface/medium of a heat exchanger and other factors, and the influence factors are usually unchangeable in operation; the second is the operation parameters of the unit, which can be set manually, such as the inlet/outlet temperature of the evaporator, the inlet/outlet temperature of the condenser, the load of the unit, etc.
How to reasonably set operation parameters on the premise of meeting the refrigeration load to enable the water chilling unit to work under a high energy efficiency ratio is the key of energy conservation of the water chilling unit. The typical nonlinear relation exists between the energy efficiency ratio of the water chilling unit and each operation parameter, and the relation between the energy efficiency ratio and each operation parameter is difficult to accurately describe by a conventional linear modeling method.
Disclosure of Invention
The invention aims to provide a water chilling unit high energy efficiency control method based on a support vector machine model, which can be used for mining the relation between the energy efficiency ratio and the key operation parameter and establishing a water chilling unit energy efficiency ratio model by using the key operation parameter and the historical data of the energy efficiency ratio. The model is used for predicting the energy efficiency ratio of the water chilling unit under different operating parameter working conditions, a group of parameters which meet the cooling load requirement and have the highest efficiency of the water chilling unit is selected from the parameters, the setting of the parameters in the operation of the water chilling unit is guided, a basis is provided for obtaining better operating efficiency, and the purposes of saving energy and reducing consumption are achieved.
In order to achieve the above purpose, the solution of the invention is:
a water chiller high energy efficiency control method based on a support vector machine model comprises the following steps:
Step 1, selecting various operation parameters which obviously affect the energy efficiency ratio of a water chilling unit from the operation parameters of the water chilling unit, and collecting historical data of the operation parameters;
Step 2, normalizing historical data of various operating parameters and energy efficiency ratios of the water chilling unit, and acquiring multiple groups of training data based on the energy efficiency ratio model of the support vector machine through a clustering algorithm;
step 3, establishing a water chilling unit energy efficiency ratio model by adopting a support vector machine method, taking historical operation parameters as the input of the model, and taking historical energy efficiency ratio data as the output of the model;
And 4, predicting the energy consumption of the water chilling unit under a certain working condition in the future in advance by using the energy efficiency ratio model.
in the step 1, a plurality of operation parameters which obviously affect the energy efficiency ratio of the water chilling unit are selected, wherein the operation parameters comprise unit load QloadEvaporator inlet temperature te_inEvaporator outlet temperature te_outcondenser inlet temperature tc_inAnd condenser outlet temperature tc_out。
In the step 1, the inlet/outlet temperatures of the evaporator and the condenser are directly collected.
In the step 1, the unit load QloadCalculated using the following formula:
Qload＝C·q·(te_in-te_out)/3.6/3.517
Wherein: qloadUnit load, unit RT
c-specific heat capacity of water, value 4.2 kJ/(kg. degree. C.)
q-flow of chilled water, unit t/h
te_in-evaporator inlet temperature in deg.C
te_out-evaporator outlet temperature in deg.C
3.6-conversion factor of kg/s and t/h
3.517-conversion factor of RT to kW.
In the step 2, each group of historical data is normalized respectively, and the specific method is as follows:
wherein: x, y ∈ Rn，xminMin (x) is the minimum value in the history data, xmaxmax (x) is the maximum value in the historical data, and the original data x is normalized to obtain y; after each group of operation parameter historical data is respectively normalized, all y is converted into [0,1 ]]In the meantime.
in the step 2, after the data are normalized, N sets of training sample sets a ═ a are obtained1,a2,···,aNIn which a isiObtaining N groups of clustering centers as training samples supporting vector regression by a clustering algorithm for a group of training samples, i is 1,2, N: { omega [ [ omega ] ]1,ω2,···,ωn}。
The specific content of the step 4 is to use the operation parameters (Q) of a group of water chilling unitsload,te_in,te_out,tc_in,tc_out) And (3) using the normalization processing of the original training data as an input vector, predicting by using the established water chilling unit energy efficiency ratio model to obtain output data, and obtaining a water chilling unit energy efficiency ratio predicted value under the operation parameter after reverse normalization processing.
By adopting the scheme, the historical operating data can be processed by utilizing a clustering algorithm to obtain a training sample of the support vector machine; the strong nonlinear fitting capability of the support vector machine is utilized to establish an energy efficiency ratio model of the water chilling unit, the energy efficiency ratio under the working condition can be predicted in advance by given operating parameters, and a foundation is laid for reasonably setting the operating parameters, efficiently operating the water chilling unit and performing subsequent coordination control by operating personnel.
Drawings
FIG. 1 is a schematic diagram of a modeling method of a water chiller model based on a support vector machine in the invention;
FIG. 2 is a schematic diagram of the prediction and control of the present invention using a chiller model.
Detailed Description
The technical solution and the advantages of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a water chiller high energy efficiency control method based on a support vector machine model, which comprises the following steps:
Step 1, selecting various operation parameters which obviously affect the energy efficiency ratio of the water chilling unit from the operation parameters of the water chilling unit, such as: the method comprises the following steps of taking the operating parameters as input parameters for predicting the energy efficiency ratio of the water chilling unit, collecting historical operating data of the operating parameters, specifically, directly collecting the inlet/outlet temperatures of the evaporator and the condenser, and obtaining the unit load by calculating the inlet/outlet temperature of the evaporator side and the flow rate of chilled water, wherein the operating parameters comprise the following specific parameters:
Qload＝C·q·(te_in-te_out)/3.6/3.517
Wherein: qloadUnit load, unit RT
C-specific heat capacity of water, value 4.2 kJ/(kg. degree. C.)
q-flow of chilled water, unit t/h
te_in-evaporator inlet temperature in deg.C
te_out-evaporator outlet temperatureIn units of DEG C
3.6-conversion factor of kg/s and t/h
3.517-conversion factor of RT to kW
step 2, normalizing historical data of various operating parameters and energy efficiency ratios of the water chilling unit, and acquiring multiple groups of training data based on the energy efficiency ratio model of the support vector machine through a clustering algorithm;
And 3, establishing an energy efficiency ratio model of the water chilling unit by adopting a support vector machine method, wherein the support vector machine idea is an algorithm idea based on a structural risk minimization criterion in a learning theory, has excellent generalization capability, and is applied to nonlinear and multidimensional identification and function fitting.
In this embodiment, the operating parameters are used as inputs to the model, and the energy efficiency ratio is used as an output of the model:
The input parameters of the energy efficiency ratio model are as follows: load Q of unitloadEvaporator inlet temperature te_inevaporator outlet temperature te_outcondenser inlet temperature tc_incondenser outlet temperature tc_out
The target outputs of the energy efficiency ratio model are: energy efficiency ratio EER of water chilling unit
The formula for calculating the energy efficiency ratio EER is as follows:
wherein: w-unit power consumption in kW
The water chilling unit energy efficiency ratio prediction model based on the support vector machine can be expressed as follows:
EER＝f(Qload,te_in,te_out,tc_in,tc_out)
the object of the invention is EER and (Q) based on multiple sets of historical dataload,te_in,te_out,tc_in,tc_out) And fitting the load prediction model by using a support vector machine method.
After removing the obviously abnormal data, carrying out normalization processing on all the historical data:
Wherein: x, y ∈ Rn，xmin＝min(x)，xmaxAfter normalization processing, N sets of training sample sets a ═ a (x) are obtained1,a2,···,aNIn which a isithe method is characterized in that the method is a group of training samples, i is 1,2, N, the number of samples of historical data is often large, N groups of clustering centers are obtained through a clustering algorithm and are used as training samples supporting vector regression: { omega [ [ omega ] ]1,ω2,···,ωnAnd each training sample group simultaneously contains a model input vector (Q)load,te_in,te_out,tc_in,tc_out) And a target output EER.
energy efficiency ratio EER and input vector (Q) of water chilling unitload,te_in,te_out,tc_in,tc_out) There is no simple linear relationship between them and a kernel function needs to be introduced to deal with the non-linearity problem. The support vector machine algorithm kernel function in the invention selects a radial basis kernel function (RBF):
Wherein, σ is a kernel function width coefficient, and the smaller the σ value is, the higher the fitting performance of the radial basis kernel function is, and the worse the generalization ability is.
The training modeling of the support vector machine is a function fitting process, and a penalty factor C is introduced to represent the tolerance of fitting errors. Therefore, two parameters, namely the width coefficient sigma of the radial basis kernel function and the penalty factor C of the training error, are determined in the modeling process through an optimization method, and finally a model of the energy efficiency ratio of the water chilling unit is obtained:
Wherein:The lagrange multiplier, mostly 0,For training input vectors, where the lagrange coefficients are either support vectors or support vectors, and b is a bias constant, a set of vectors can be constructed using the modelObtain a correspondence
And 4, predicting the energy consumption of the water chilling unit under a certain working condition in the future in advance by using the energy efficiency ratio model. As shown in FIG. 2, the operating parameters (Q) of a group of chiller units are setload,te_in,te_out,tc_in,tc_out) Normalization rule processing using raw training data as input vectoroutput data is obtained by utilizing the established energy efficiency ratio model of the water chilling unit to predict And obtaining a predicted value EER of the energy efficiency ratio of the water chilling unit under the operation parameter after reverse normalization treatment. Changing the numerical value of the operation parameters, repeating the process to obtain the energy efficiency ratios of the water chilling unit under various operation conditions, selecting a group of parameters with the highest energy efficiency ratio as the set parameters of the water chilling unit, and assisting the operators to reasonably set the operation parameters to enable the water chilling unit to operate at high efficiency.
According to the water chiller high-energy-efficiency control method based on the support vector machine model, the historical data is preprocessed through normalization, cluster analysis and the like, and a modeling sample is obtained; reasonably determining training parameters, and establishing an energy efficiency ratio prediction model of the water chilling unit by utilizing the strong nonlinear fitting capacity of the support vector machine; the operation parameters are changed based on the model, the energy efficiency ratio predicted values of the water chilling unit under different set working conditions can be obtained, the predicted values are used as the basis for reasonably setting the operation parameters, so that the water chilling unit is always operated under the high energy efficiency ratio working condition, and a foundation is laid for subsequent coordination control.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (7)
1. A water chiller high energy efficiency control method based on a support vector machine model is characterized by comprising the following steps:
Step 1, selecting various operation parameters which obviously affect the energy efficiency ratio of a water chilling unit from the operation parameters of the water chilling unit, and collecting historical data of the operation parameters;
Step 2, normalizing historical data of various operating parameters and energy efficiency ratios of the water chilling unit, and acquiring multiple groups of training data based on the energy efficiency ratio model of the support vector machine through a clustering algorithm;
Step 3, establishing a water chilling unit energy efficiency ratio model by adopting a support vector machine method, taking historical operation parameters as the input of the model, and taking historical energy efficiency ratio data as the output of the model;
And 4, predicting the energy consumption of the water chilling unit under a certain working condition in the future in advance by using the energy efficiency ratio model.
2. The energy-efficient control method of the water chiller based on the support vector machine model according to claim 1, characterized in that: in the step 1, a plurality of operation parameters which obviously affect the energy efficiency ratio of the water chilling unit are selected, wherein the operation parameters comprise unit load QloadEvaporator inlet temperature te_inEvaporator outlet temperature te_outCondenser inlet temperature tc_inAnd condenser outlet temperature tc_out。
3. The energy-efficient control method of the water chiller based on the support vector machine model according to claim 2, characterized in that: in the step 1, the inlet/outlet temperatures of the evaporator and the condenser are directly collected.
4. The energy-efficient control method of the water chiller based on the support vector machine model according to claim 2, characterized in that: in the step 1, the unit load QloadCalculated using the following formula:
Qload＝C·q·(te_in-te_out)/3.6/3.517
Wherein: qloadUnit load, unit RT
C-specific heat capacity of water, value 4.2 kJ/(kg. degree. C.)
q-flow of chilled water, unit t/h
te_in-evaporator inlet temperature in deg.C
te_out-evaporator outlet temperature in deg.C
3.6-conversion factor of kg/s and t/h
3.517-conversion factor of RT to kW.
5. the energy-efficient control method of the water chiller based on the support vector machine model according to claim 2, characterized in that: in the step 2, normalization processing is performed on each group of historical data, and the specific method is as follows:
wherein: x, y ∈ Rn，xminMin (x) is the minimum value in the history data, xmaxMax (x) is the maximum value in the historical data, and the original data x is normalized to obtain y; after each group of operation parameter historical data is respectively normalized, all y is converted into [0,1 ]]In the meantime.
6. The method as claimed in claim 2the high-energy-efficiency control method of the water chilling unit supporting the vector machine model is characterized by comprising the following steps: in step 2, after the data are normalized, N sets of training sample sets a ═ a are obtained1,a2,…,aNIn which a isiObtaining N groups of clustering centers as training samples supporting vector regression by a clustering algorithm for a group of training samples, i is 1,2, N: { omega [ [ omega ] ]1,ω2,…,ωn}。
7. The energy-efficient control method of the water chiller based on the support vector machine model according to claim 2, characterized in that: the specific content of the step 4 is to use the operation parameters (Q) of a group of water chilling unitsload,te_in,te_out,tc_in,tc_out) And (3) using the normalization processing of the original training data as an input vector, predicting by using the established water chilling unit energy efficiency ratio model to obtain output data, and obtaining a water chilling unit energy efficiency ratio predicted value under the operation parameter after reverse normalization processing.
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| CN111256313A (en) * | 2020-01-21 | 2020-06-09 | 田雨辰 | Intelligent refrigeration quality adjusting algorithm |
| CN111536639A (en) * | 2020-05-08 | 2020-08-14 | 东南大学 | Water chilling unit operation optimization control method based on Lagrange multiplier method |
| CN112270068A (en) * | 2020-09-28 | 2021-01-26 | 天津科技大学 | Falling film evaporation dynamic simulation method based on combination of heat transfer mechanism and SVM |
| CN112303819A (en) * | 2020-09-24 | 2021-02-02 | 青岛海信日立空调***有限公司 | Air conditioner and control method |
| CN112747418A (en) * | 2021-01-05 | 2021-05-04 | 青岛海信日立空调***有限公司 | Air conditioner and cloud server |
| CN113268873A (en) * | 2021-05-25 | 2021-08-17 | 云南电网有限责任公司电力科学研究院 | Method and system for obtaining energy efficiency ratio of ground source heat pump based on multi-factor influence |
| CN113899055A (en) * | 2020-06-22 | 2022-01-07 | 青岛海信日立空调***有限公司 | Control system of water chilling unit |
| WO2022246627A1 (en) * | 2021-05-25 | 2022-12-01 | 罗伯特·博世有限公司 | Method and apparatus for controlling refrigerating device |
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