CN116412498A - Method for identifying and rectifying energy consumption deviation of cold water air conditioning system - Google Patents
Method for identifying and rectifying energy consumption deviation of cold water air conditioning system Download PDFInfo
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- 238000005265 energy consumption Methods 0.000 title claims abstract description 76
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- 238000004378 air conditioning Methods 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 38
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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
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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
- F24F5/00—Air-conditioning systems or apparatus not covered by F24F1/00 or F24F3/00, e.g. using solar heat or combined with household units such as an oven or water heater
- F24F5/0003—Exclusively-fluid systems
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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
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
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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
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
- F24F2110/22—Humidity of the outside air
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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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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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Abstract
The invention discloses a method for identifying and correcting energy consumption deviation of a cold water air conditioning system, which is suitable for controlling and adapting an existing building cold water unit, and the technical scheme of the invention is that a regression model is established by carrying out big data analysis on environmental parameters of the cold water unit and historical data of control parameters, and an energy consumption value is predicted according to the current parameters, and when the deviation between actual energy consumption and predicted energy consumption is larger than a set threshold value, alarm information is recorded; and according to the regression model, the control parameter value with the minimum energy consumption under the current environment parameter is obtained, and the system control parameter is corrected. The method of the invention searches the minimum energy consumption sequence of the control parameters of the water chilling unit by the method of optimizing and searching the minimum value of the historical data, so that the whole energy consumption of the water chilling unit is always maintained at a lower level, and the method has a certain practical significance for the energy-saving construction of the air conditioner of the building.
Description
Technical Field
The invention relates to the technical field of building energy-saving management, in particular to a method for reducing energy consumption of an existing building water chiller.
Background
The invention adopts a big data modeling method, calculates and obtains the optimal energy-saving operation control parameters which accord with the existing building water chilling unit by a limit value searching method, thereby improving the operation efficiency of the water chilling unit and achieving the purpose of saving energy consumption.
Disclosure of Invention
The invention aims to provide a method for identifying and correcting energy consumption deviation of a cold water air conditioning system, which has the following effects: the energy consumption deviation recognition program generates an alarm and timely processes and maintains abnormal equipment; and searching the minimum energy consumption sequence of the control parameters of the water chilling unit, so that the overall energy consumption of the water chilling unit is always kept at a lower level.
The invention discloses a method for identifying and correcting energy consumption deviation of a cold water air conditioning system, which is shown in a figure 1 and is characterized by comprising the following steps:
collecting historical energy consumption data, environment data and chiller control parameter data of a building air conditioning system; wherein the environmental data comprises: outdoor temperature, outdoor humidity, air conditioning refrigerating capacity, chilled water outlet temperature, chilled water return temperature, cooling water outlet temperature, cooling water return temperature and the like; the control parameters of the water chilling unit comprise a chilled water outlet temperature set value, a most unfavorable point pressure difference set value (chilled water circulation), a cooling water temperature difference set value, a wet bulb temperature approach set value (cooling tower), a switch unit number and the like;
the collected data are cleaned, abnormal data caused by reasons such as abnormal starting-up time period data of the water chilling unit and communication faults are removed;
modeling based on a random forest algorithm, and building an air conditioning system energy consumption deviation recognition model by taking environmental data as a feature vector and historical energy consumption data as target features; building an energy consumption correction model of the air conditioning system by taking outdoor temperature, outdoor humidity, air conditioning refrigerating capacity and control parameters of a water chilling unit as characteristic vectors and historical energy consumption data as target characteristics;
setting a threshold for energy consumption departure warning: obtaining a standard deviation sigma according to a historical deviation (=an actual energy consumption value-an energy consumption predicted value), setting a threshold initial value as sigma, and gradually increasing the threshold to 3 sigma after the system tends to be stable; the set function is that the current air conditioner energy consumption actual value is larger than the energy consumption predicted value output by the air conditioner system energy consumption deviation recognition model and exceeds the set threshold value, the system initiates an alarm and carries out deviation correction calculation.
Adopting an improved simulated annealing algorithm to obtain a water chilling unit control parameter value when an energy consumption correction model of an air conditioning system outputs an energy consumption minimum value under the conditions of outdoor temperature, outdoor humidity and air conditioning refrigerating capacity determination; the improved simulated annealing algorithm for solving the minimum value of the air conditioner energy consumption specifically comprises the following substeps:
(1) Setting an initial annealing temperature T Initial Termination annealing temperature T Final The Markov chain length is the internal circulation running times M and the searching step length S 0 Setting up upper and lower limits of state search space Min and Max]；
(2) Initialization state X 0 The minimum value of the search space range is taken, and the current state X is given n =X 0 And inputting the energy consumption model of the air conditioning system, and predicting to obtain the initial electricity consumption f (X) of the air conditioning system n )；
(3) The outer layer (annealing temperature) controls the change of the neighborhood, and the inner layer (Markov chain length) perturbs in the neighborhood, thereby randomly generating a new state X at each iteration n+1 The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the disturbance formula of inlayer is:
X=X 0 +S 0 * (Max-Min) random formula (1)
S=c*S 0 Formula (2)
Where rand is a neighborhood function, and a random gaussian distribution probability density function is used in the experiment. The method greatly improves the searching efficiency, and judges which areas are more likely to have global optimal values by utilizing the information of the searched areas; c is a reduction factor, and the step length is reduced in each iteration, so that the search area is gradually reduced, and the search precision is improved;
(4) Calculate the difference Δd=f (X n+1 )-f(X n ) Accepting X when ΔD is less than 0 n+1 Updated to the current state X n And record the optimal state X best And an optimal value f (X best ) The method comprises the steps of carrying out a first treatment on the surface of the If DeltaD is greater than 0, X is accepted with a certain probability P n+1 Updated to the current state X n The method comprises the steps of carrying out a first treatment on the surface of the Wherein the probability P is formulated according to the MCMC principle:
p=min (1, e (- ΔD/T)) equation (3)
If P > Random (), accepting the inferior state, otherwise rejecting the inferior state; random () represents a Random function between 0 and 1, which can effectively avoid trapping local minima;
(5) After the inner layer iterates M times, the optimal state X is updated best And an optimal value f (X best )；
(6) Updating the temperature T if T>= T Final Continuously executing the steps (3) - (5), otherwise, stopping the circulation, and outputting the optimal state X best And an optimal value f (X best ) The program ends, wherein the formula for updating the temperature is:
T=T Initial * e (a) K (1/n)) formula (4)
Wherein a is an attenuation coefficient, K is iteration times, and n is the number of state parameters; according to the physical principle, the form of the exponential function accords with the essence of cooling, and the method improves the space traversing capacity and improves the algorithm operation efficiency.
The beneficial effects of the invention are as follows: the minimum energy consumption sequence of the control parameters of the water chilling unit is searched by adopting an improved simulated annealing algorithm, so that the overall energy consumption of the water chilling unit is always maintained at a lower level.
Drawings
The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification. In the accompanying drawings:
FIG. 1 is a flow chart for identifying and correcting deviation of energy consumption of a cold water air conditioning system according to the present invention;
FIG. 2 is a flow chart of the improved simulated annealing algorithm of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific embodiments, and the flow chart is shown in fig. 1.
Collecting data: firstly, collecting historical energy consumption data, environment data and chiller control parameter data of an air conditioning system of a target building for 6-8 months at a time interval of 10 min; wherein the environmental data comprises: outdoor temperature, outdoor humidity, air conditioning refrigerating capacity, chilled water outlet temperature, chilled water return temperature, cooling water outlet temperature, cooling water return temperature and the like; the control parameters of the water chilling unit comprise a chilled water outlet temperature set value, a most unfavorable point pressure difference set value (chilled water circulation), a cooling water temperature difference set value, a wet bulb temperature approach set value (cooling tower), a switch unit number and the like.
Data cleaning: and cleaning the collected original data, removing data of the chiller in an abnormal starting period, and removing abnormal data caused by communication faults and the like to obtain a group of data with higher reliability.
Modeling based on a random forest algorithm, and building an air conditioning system energy consumption deviation recognition model by taking environmental data as a feature vector and historical energy consumption data as target features; and building an energy consumption correction model of the air conditioning system by taking the outdoor temperature, the outdoor humidity, the air conditioning refrigerating capacity and the control parameters of the water chilling unit as characteristic vectors and the historical energy consumption data as target characteristics.
Setting a threshold for energy consumption departure warning: obtaining a standard deviation sigma according to a historical deviation (=an actual energy consumption value-an energy consumption predicted value), setting a threshold initial value as sigma, and gradually increasing the threshold to 3 sigma after the system tends to be stable; the set function is that the current air conditioner energy consumption actual value is larger than the energy consumption predicted value output by the air conditioner system energy consumption deviation recognition model and exceeds the set threshold value, the system initiates an alarm and carries out deviation correction calculation.
Adopting an improved simulated annealing algorithm as shown in fig. 2 to obtain a control parameter value of the water chilling unit when an energy consumption correction model of the air conditioning system outputs an energy consumption minimum value under the conditions of outdoor temperature, outdoor humidity and air conditioning refrigerating capacity determination; the improved simulated annealing algorithm for solving the minimum value of the air conditioner energy consumption specifically comprises the following substeps:
(1) Setting an initial annealing temperature T Initial =100, termination annealing temperature T Final =20, markov chain length, i.e. inner loop run number m=100, search step S 0 In the experiment, four chiller control parameters of a chilled water outlet temperature set value, a least adverse point differential pressure set value, a cooling water temperature difference set value and a wet bulb temperature approach set value are taken as independent variable states, and the lower limit of a set state search space is [2.5, 7, 20, 1]The upper limit is [5.5, 12, 40, 3.5]；
(2) Initialization state X 0 The minimum value of the search space range is taken, and the current state X is given n =X 0 =[2.5, 7, 20, 1]And inputting the energy consumption model of the air conditioning system, and predicting to obtain the initial electricity consumption f (X) of the air conditioning system n )；
(3) The outer layer (annealing temperature) controls the change of the neighborhood, and the inner layer (Markov chain length) perturbs in the neighborhood, thereby randomly generating a new state X at each iteration n+1 The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the disturbance formula of inlayer is:
X=X 0 +S 0 * (Max-Min) random formula (1)
S=c*S 0 Formula (2)
Where rand is a neighborhood function, and a random gaussian distribution probability density function is used in the experiment. The method greatly improves the searching efficiency, and judges which areas are more likely to have global optimal values by utilizing the information of the searched areas; c is a reduction factor of 0.99, the step length is reduced in each iteration, and the search area is gradually reduced, so that the search precision is improved;
(4) Calculate the difference Δd=f (X n+1 )-f(X n ) Accepting X when ΔD is less than 0 n+1 Updated to the current state X n And record the optimal state X best And an optimal value f (X best ) The method comprises the steps of carrying out a first treatment on the surface of the If DeltaD is greater than 0, X is accepted with a certain probability P n+1 Updated to the current state X n The method comprises the steps of carrying out a first treatment on the surface of the Wherein the probability P is formulated according to the MCMC principle:
p=min (1, e (- ΔD/T)) equation (3)
If P > Random (), accepting the inferior state, otherwise rejecting the inferior state; random () represents a Random function between 0 and 1, which can effectively avoid trapping local minima;
(5) After the inner layer iterates M times, the optimal state X is updated best And an optimal value f (X best )；
(6) Updating the temperature T if T>= T Final Continuously executing the steps (3) - (5), otherwise, stopping the circulation, and outputting the optimal state X best And an optimal value f (X best ) The program ends, wherein the formula for updating the temperature is:
T=T Initial * e (a) K (1/n)) formula (4)
Where a=0.05 is the attenuation coefficient, K is the number of iterations, and n=4 is the number of state parameters; according to the physical principle, the form of the exponential function accords with the essence of cooling, and the method improves the space traversing capacity and improves the algorithm operation efficiency.
In order to further verify the effect of the improved algorithm, a general simulated annealing algorithm and an improved simulated annealing algorithm are adopted to respectively solve, and for comparison convenience, the parameter settings of the two algorithms are the same, and the positions of the initial states are the same. The experimental results are shown in table 1 below:
the time of two algorithms is recorded, the SA algorithm calculates about 160s, while the modified SA algorithm calculates about 90s only; and the SA algorithm is continuously accepted by the optimal solution when the iteration is close to 100 times, and the improved SA algorithm is iterated 80 times to find the optimal solution, and then a new solution is not accepted. The improved SA algorithm can find global optimum only by iteration for fewer times, has higher precision and can obviously improve the calculation efficiency.
The above is merely an example of the present invention, but the present invention is not limited to the above specific embodiments, and it will be apparent to those skilled in the art that modifications and equivalents may be made to the solution described in the foregoing examples, or some of the features may be substituted. Any modification, improvement, etc. made without causing any conflict with the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The method for identifying and rectifying the energy consumption deviation of the cold water air conditioning system is characterized by comprising the following steps of: step S1, collecting historical energy consumption data, environment data and chiller control parameter data of a building air conditioning system; s2, cleaning the collected data; step S3, modeling by using a random forest regression algorithm to obtain an air conditioning system energy consumption deviation recognition model and an air conditioning system energy consumption deviation correction model; step S4, setting a threshold value of the energy consumption deviation warning: obtaining a standard deviation sigma according to a historical deviation (=an actual energy consumption value-an energy consumption predicted value), setting a threshold initial value as sigma, and gradually increasing the threshold to 3 sigma after the system tends to be stable; and S5, adopting an improved simulated annealing algorithm to obtain a water chilling unit control parameter value when the energy consumption correction model of the air conditioning system outputs an energy consumption minimum value under the conditions of outdoor temperature, outdoor humidity and refrigeration capacity determination.
2. The method for identifying and correcting energy consumption deviation of a cold water air conditioning system according to claim 1, wherein the environmental data in step S1 includes: outdoor temperature, outdoor humidity, air conditioning refrigerating capacity, chilled water outlet temperature, chilled water return temperature, cooling water outlet temperature, cooling water return temperature and the like; the control parameters of the water chilling unit comprise a chilled water outlet temperature set value, a most unfavorable point pressure difference set value (chilled water circulation), a cooling water temperature difference set value, a wet bulb temperature approach set value (cooling tower), a switch unit number and the like.
3. The method for identifying and correcting energy consumption deviation of a cold water air conditioning system according to claim 1, wherein step S2 cleans the collected data, removes data of a cold water unit in a period of abnormal start-up, and eliminates abnormal data caused by communication faults and the like.
4. The method for identifying and correcting energy consumption deviation of a cold water air conditioning system according to claim 1, wherein in step S3, an air conditioning system energy consumption deviation identification model is constructed by taking environmental data as a feature vector and historical energy consumption data as a target feature; the energy consumption correction model of the air conditioning system is constructed by taking the outdoor temperature, the outdoor humidity, the air conditioning refrigerating capacity and the control parameters of the water chilling unit as characteristic vectors and the historical energy consumption data as target characteristics.
5. The method for identifying and correcting the deviation of energy consumption of a cold water air conditioning system according to claim 1, wherein the step S4 is a threshold setting of the deviation of energy consumption warning, which acts on the alarm and the correction calculation if the energy consumption of the air conditioning system is actually larger than the prediction exceeding the set threshold when the system runs the deviation of energy consumption of the air conditioning system identification program.
6. The method for identifying and correcting energy consumption deviation of a cold water air conditioning system according to claim 1, wherein the step S5 of adopting an improved simulated annealing algorithm to calculate the minimum value of the energy consumption of the air conditioning specifically comprises the following substeps:
s5.1, setting an initial annealing temperature T Initial Termination annealing temperature T Final The Markov chain length is the internal circulation running times M and the searching step length S 0 Setting up upper and lower limits of state search space Min and Max]；
S5.2, initialization State X 0 The minimum value of the search space range is taken, and the current state X is given n =X 0 And inputting the energy consumption model of the air conditioning system, and predicting to obtain the initial electricity consumption f (X) of the air conditioning system n )；
S5.3, the outer layer (annealing temperature) controls the change of the neighborhood, and the inner layer (Markov chain length) is disturbed in the neighborhood, so that a new state X is randomly generated n+1 ；
S5.4, calculating the difference Δd=f (X n+1 )-f(X n ) Accepting X when ΔD is less than 0 n+1 Updated to the current state X n And record the optimal state X best And an optimal value f (X best ) The method comprises the steps of carrying out a first treatment on the surface of the If DeltaD is greater than 0, X is accepted with a certain probability P n+1 Updated to the current state X n The method comprises the steps of carrying out a first treatment on the surface of the The method can effectively avoid sinking into local minima;
s5.5, after the inner layer iterates M times, updating the optimal state X best And an optimal value f (X best )；
S5.6, updating the temperature T, if T>= T Final Continuously executing the steps (3) - (5), otherwise, stopping the circulation, and outputting the optimal state X best And an optimal value f (X best ) The routine ends.
7. The method for identifying and correcting energy consumption deviation of a cold water air conditioning system according to claim 5, wherein the disturbance formula of the inner layer in step S5.3 is:
X=X 0 +S 0 * (Max-Min) random formula (1)
Wherein rand is a neighborhood function, and a random Gaussian distribution probability density function is used in the experiment;
the method greatly improves the searching efficiency, and judges which areas are more likely to have global optimal values by utilizing the information of the searched areas.
8. The method for identifying and correcting energy consumption deviation of a cold water air conditioning system according to claim 6, wherein the updating formula of the searching step length in the inner layer iteration process is as follows:
S=c*S 0 formula (2)
In the formula, c is a reduction factor, and the step length is reduced in each iteration, so that the search area is gradually reduced, and the search precision is improved.
9. The method for identifying and correcting energy consumption deviation of cold water air conditioning system according to claim 5, wherein if Δd is greater than 0 in step S5.4, X is accepted with a certain probability P n+1 Updated to the current state X n Wherein the probability P is formulated according to the MCMC principle,
p=min (1, e (- ΔD/T)) equation (3)
If P > Random (), then the bad state is accepted, whereas the bad state is rejected, random () represents a Random function between 0 and 1.
10. The method for identifying and correcting energy consumption deviation of a cold water air conditioning system according to claim 5, wherein the formula for updating the temperature in step S5.6 is:
T=T Initial * e (a) K (1/n)) formula (4)
Wherein a is an attenuation coefficient, K is iteration times, and n is the number of state parameters; according to the physical principle, the form of the exponential function accords with the essence of cooling, and the method improves the space traversing capacity and improves the algorithm operation efficiency.
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| CN117190407B (en) * | 2023-11-02 | 2024-02-09 | 深圳市美兆环境股份有限公司 | Energy-saving control method and system for central air conditioner |
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