A method for heating network regulation by establishing a nonlinear regression model

Abstract

The present invention proposes a method for heat network regulation by establishing a nonlinear regression model, which belongs to the field of heat network regulation technology. The method includes: taking points to calculate the temperature of the secondary network supply and return water based on the daily outdoor temperature and floor heating working conditions curves of the previous years provided by the heating company. 、 #imgabs1# 、 #imgabs2# 、 #imgabs3# 、 The #imgabs4# coefficient determines the heating company's exclusive nonlinear regression model. Establishing a nonlinear regression model allows for customized target temperature values ​​based on the actual needs of different regions and the conditions of different heat exchange stations, and compensates for changes in outdoor temperature, making regulation more responsive to actual needs and more accurate and effective. The addition of temperature correction values ​​accounts for regional uniqueness. Adding heat load as a factor in regulation allows heating companies to be more selective in their use of heat sources, better understand the relationship between heat source usage and regulation results, and enable adaptive regulation based on changes in the heat source.

Description

Technical Field

[0001] The present invention belongs to the technical field of heat network regulation, and in particular relates to a method for heat network regulation by establishing a nonlinear regression model. Background Art

[0002] At present, heat exchange stations generally have the problem of unclear heat source usage. Large heat source usage will lead to excessive emissions and rectification, and the heating cost is high and the heating pressure is great. Low heat source usage will lead to substandard heating, which makes it impossible for heat exchange stations to control accurately. There is no clear way to control how to meet heating needs without causing excessive use of heat sources. We can only rely on annual heating experience to control it slightly. Summary of the Invention

[0003] The present invention addresses the problems of the prior art and provides a method for regulating a heat network by establishing a nonlinear regression model, comprising:

[0004] Calculate the points based on the daily outdoor temperature and secondary network supply and return water temperature curves for radiators and floor heating in previous years provided by the heating company. 、 、 、 、 Coefficients, determine the nonlinear regression model specific to the heating company;

[0005] Get the daily average outdoor temperature in real time ,Will Substitute the formulas for two different working conditions to calculate the average temperature of the secondary network ;

[0006] Determine the temperature correction value of the heating company due to different energy-saving policies and different degrees of aging of insulation measures ,exist Add the temperature correction value to the basis of Final target temperature;

[0007] Get the water supply temperature of the heat exchange station in real time and return water temperature , calculate the current actual average temperature ;

[0008] Calculate the heating load according to the heat load formula Whether it meets the requirements for the current heating system operation;

[0009] If the calculated heating load is not within the heat source tolerance range of the heating company, the actual heat generated can be inferred according to the heat load formula. ;

[0010] Inferred from actual heat Re-introduce the nonlinear regression model and calculate the corresponding Carry out regulation to avoid energy waste and imbalance in the heat network.

[0011] Furthermore, according to the daily outdoor temperature and secondary network supply and return water temperature curves of radiator and floor heating conditions in previous years provided by the heating company, the calculation is carried out. 、 、 、 、 Coefficients, determine the nonlinear regression model exclusive to the heating company:

[0012]

[0013] is a constant term, which indicates the reference value of the secondary network temperature when the outdoor temperature is zero, corresponding to the initial set temperature under the design conditions, or the basic temperature when there is no external temperature influence;

[0014] is the linear term coefficient, which reflects the linear response of the secondary network temperature to the change of outdoor temperature and reflects the basic energy-saving regulation strategy;

[0015] is the quadratic term coefficient, which describes the nonlinear effect and reflects the acceleration of temperature change. It means that at extreme temperatures, the system response is more significant, reflecting the nonlinear characteristics of the heat load.

[0016] is the cubic term coefficient, capturing dynamic changes;

[0017] is the quartic term coefficient, which is used for high-order fitting to further optimize the model accuracy.

[0018] Furthermore, the temperature correction value of the heating company due to different energy-saving policies and different degrees of aging of insulation measures is determined. ,exist Add the temperature correction value to the basis of Final target temperature:

[0019]

[0020] Get the water supply temperature of the heat exchange station in real time and return water temperature , calculate the current actual average temperature :

[0021]

[0022] The actual average temperature and Compare and control, change the opening of the electric regulating valve Since it takes a certain amount of time for the return water temperature of the secondary network to change, different adjustment intervals need to be determined according to the different heating areas of different heat exchange stations. and The difference is considered balanced within the temperature dormant zone, and stable heating does not participate in further regulation; the temperature dormant zone is set according to the accuracy of the collected supply and return water temperature sensors.

[0023] Compared with the prior art, the advantages and positive effects of the present invention are:

[0024] The present invention uses the average supply and return water temperature of the secondary network in the heat exchange station as the control target. If only the supply water temperature is controlled, it is easy to cause overcooling of remote residents. If only the return water temperature is controlled, it is easy to cause overheating of nearby residents. Therefore, using the average supply and return water temperature of the secondary network as the control target is the most accurate and direct control among all the parameters of the heat exchange station. By establishing a nonlinear regression model, the target temperature value can be customized according to the actual needs of different regions and the conditions of different heat exchange stations, and a compensation response is performed as the outdoor temperature changes, making its control more in line with actual needs and more accurate and effective. The increase in the temperature correction value takes into account the existence of regional uniqueness, such as some hospitals, nursing homes, and school areas that require special care, making the control more humane. Adding heat load as a consideration parameter in the control makes the heating company more selective in the use of its own heat source, and better understands the corresponding relationship between the use of the heat source and the control results. At the same time, it can also perform adaptive control according to the changes in the heat source. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0026] Figure 1 This is a schematic structural diagram of the heat network control system of the present invention;

[0027] Figure 2 This is a flow chart of the heat network control system of the present invention. DETAILED DESCRIPTION

[0028] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that the embodiments of the present application and the features therein can be combined with each other without conflict.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0030] Example 1, as Figures 1-2 As shown, this application relates to a method for regulating the heat network of a heat exchange station. This method regulates the heat network according to target demand, solves the problems of inaccurate heat network regulation and unclear heat source usage, prevents excessive emissions from heating boilers, ensures that while meeting target heating demand, excessive energy waste is reduced, increases energy utilization, and achieves the goal of energy conservation and consumption reduction. The purpose is to solve the problems of inaccurate heat network regulation and unclear heat source usage, so that heating companies can have a clear parameter consideration between heat source usage and heating results, and reduce heat source usage while meeting heating demand. At the same time, it can establish a regulation strategy that suits local conditions for different regions and environments, achieving precise regulation.

[0031] like Figure 1 As shown in the structural diagram, it includes a network electric regulating valve, a network water supply temperature collector, and a network return water temperature collector. Figure 2 The flow chart is provided by the heating company, with daily outdoor temperatures and average temperature curves of the secondary network for both radiator and floor heating conditions in previous years. The temperature values ​​that need to be corrected after comprehensive consideration of factors, as well as basic parameters such as comprehensive thermal indicators, indoor temperature target values, and heating area, are included. The secondary network supply and return water temperature collector can collect the supply water temperature and return water temperature in real time, calculate the average temperature of the secondary network, and compare it with the target average temperature of the secondary network. If it is too high, close the valve, and if it is too low, open the valve until it is consistent with the target value and stops regulation. Calculate whether the heating load under current conditions exceeds the heat source tolerance range of the heating company. If it exceeds the limit, reverse the calculation based on the actual heat source heat to obtain a new target temperature value for the secondary network. , and then adjust according to the new second network target temperature value.

[0032] A method for regulating a heat network by establishing a nonlinear regression model is a method that can adaptively adjust the balance of heat exchange stations based on changes in weather and heat source supply.

[0033] The actual control steps are as follows:

[0034] Step 1: Calculate the points based on the daily outdoor temperature and secondary network supply and return water temperature curves for radiators and floor heating in previous years provided by the heating company. 、 、 、 、 Coefficients are used to determine the nonlinear regression model specific to the heating company.

[0035]

[0036] It is a constant term, which represents the reference value of the secondary network temperature when the outdoor temperature is zero. This usually corresponds to the initial set temperature under the design conditions, or the base temperature when there is no external temperature influence.

[0037] It is the linear term coefficient, reflecting the linear response of the secondary network temperature to the change of outdoor temperature, and embodies the basic energy-saving regulation strategy.

[0038] It is the quadratic term coefficient, which describes the nonlinear effect and reflects the acceleration of temperature change. It means that under extreme temperatures (extreme cold or hot), the system response is more significant (such as the accelerated increase or decrease of the secondary network temperature), reflecting the nonlinear characteristics of the heat load.

[0039] is the cubic term coefficient, capturing more complex dynamic changes.

[0040] is the quartic term coefficient, which is used for high-order fitting to further optimize the model accuracy.

[0041] Based on the data on radiator and floor heating working conditions provided by the heating company in previous years, five sets of data are taken and entered into the model to obtain a set of linear equations with five variables, which can be calculated by the elimination method.

[0042] The following is an example of Gaussian elimination:

[0043] Assume that the system of equations is:

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] First write the augmented matrix:

[0050]

[0051] This matrix is ​​then converted to row echelon form so that the first column is zero except for the first row.

[0052] Subtract the first row from the second row, and the new second row becomes

[0053] Subtract the first row from the third row, and the new third row becomes

[0054] Subtract the first row from the fourth row, and the new fourth row becomes

[0055] Subtract the first row from the fifth row, and the new fifth row becomes

[0056] So now the matrix becomes:

[0057]

[0058] From the last line we can find =5, we can find from the fourth row =4, we can find from the third row =3, we can find it from the second row =2, which can be obtained by substituting it into the first row. = 1. This principle is written into the program code, and the direct calculation can be processed using the program code of Gaussian elimination method.

[0059] For example, the model calculated by a heating company in Luoyang ,

[0060] ,

[0061] ,

[0062] ,

[0063] .

[0064] In order to ensure the accuracy of the control, the program retains 12 decimal places.

[0065] Step 2: Get the average outdoor temperature in real time ,Will Substitute the formulas for two different working conditions to calculate the average temperature of the secondary network .

[0066] Step 3: Determine the temperature correction value for different heating companies due to different energy-saving policies and different degrees of insulation aging. ,exist Add the temperature correction value to the basis of Final target temperature. The temperature correction value is determined by the heating company and usually does not require correction. However, some areas with special requirements may require correction, such as areas with certain restrictions on carbon emissions. If the correction is too much or too little, and the heating effect after the adjustment is completed and the balance effect is not ideal, you can repeat the second adjustment from step 3 until the heating effect reaches the ideal value.

[0067] Step 4: Obtain the water supply temperature of the heat exchange station in real time and return water temperature , calculate the current actual average temperature .

[0068]

[0069] The actual average temperature and Compare and control, change the opening of the electric regulating valve Since it takes a certain amount of time for the return water temperature of the secondary network to change, different adjustment intervals need to be determined according to the different heating areas of different heat exchange stations. and If the difference is within the temperature dormancy zone, it is considered balanced and stable heating is not involved in further regulation. The temperature dormancy zone is generally set based on the accuracy of the collected supply and return water temperature sensors. For example, if the sensor accuracy is ±0.5°C, the temperature dormancy zone is set to ±1°C.

[0070] Step 5: Calculate the heating load according to the heat load formula ( ) Whether it meets the requirements of the current heating system operation. The local radiator design outdoor temperature Generally designed according to the "Design Code for Heating, Ventilation and Air Conditioning of Civil Buildings" and local specifications, which can be searched and obtained (for example, the design outdoor temperature of Yantai radiator is -5.8℃); comprehensive thermal index It is generally provided by the heating company, that is, the heating intensity of the heating company.

[0071]

[0072] Step 6: If the calculated heating load is not within the heat source tolerance of the heating company, the actual heat generated can be used to infer the heat load formula. .

[0073] By reversing the heat load formula in step 5, we can get the formula:

[0074]

[0075] Step 7: Infer from actual heat Re-enter the nonlinear regression model and repeat steps 2-3 to calculate the corresponding Carry out regulation to avoid energy waste and imbalance in the heat network.

[0076] The advantage of the present invention is that it takes the average supply and return water temperature of the secondary network in the heat exchange station as the control target. If the supply water temperature is controlled alone, it is easy to cause overcooling of remote residents. If the return water temperature is controlled alone, it is easy to cause overheating of nearby residents. Therefore, taking the average supply and return water temperature of the secondary network as the control target is the most accurate control among all the parameters of the heat exchange station, and it is also the most direct control. By establishing a nonlinear regression model, the target temperature value can be customized according to the actual needs of different regions and the conditions of different heat exchange stations, and a compensation response is performed as the outdoor temperature changes, so that its control is more in line with actual needs and more accurate and effective. The increase in the temperature correction value takes into account the existence of regional uniqueness, such as some hospitals, nursing homes, and school areas that require special care, making the control more humane. Adding heat load as a consideration parameter in the control makes the heating company more selective in the use of its own heat source, and better understands the correspondence between the use of the heat source and the control results. At the same time, it can also perform adaptive control according to the changes in the heat source.

[0077] The above description is merely a preferred embodiment of the present invention and does not limit the present invention in any other form. Any technician familiar with the present invention may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes for application in other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for regulating a heating network by establishing a nonlinear regression model, characterized in that: include: Calculate the points based on the daily outdoor temperature and secondary network supply and return water temperature curves for radiators and floor heating in previous years provided by the heating company. 、 、 、 、 Coefficients, determine the nonlinear regression model specific to the heating company; Get the daily average outdoor temperature in real time ,Will Substitute the formulas for two different working conditions to calculate the average temperature of the secondary network ; Determine the temperature correction value of the heating company due to different energy-saving policies and different degrees of aging of insulation measures ,exist Add the temperature correction value to the basis of Final target temperature; Get the water supply temperature of the heat exchange station in real time and return water temperature , calculate the current actual average temperature ; Calculate the heating load according to the heat load formula Whether it meets the requirements of the current heating system operation; If the calculated heating load is not within the heat source tolerance range of the heating company, the actual heat generated can be inferred according to the heat load formula. ; Inferred from actual heat Re-introduce the nonlinear regression model and calculate the corresponding To control, the current actual average temperature and Compare the pressure and close the valve if it is too high, and open the valve if it is too low, until it reaches the target value and stops regulating, thus avoiding energy waste and heat network imbalance. Calculate the points based on the daily outdoor temperature and secondary network supply and return water temperature curves for radiators and floor heating in previous years provided by the heating company. 、 、 、 、 Coefficients, determine the nonlinear regression model exclusive to the heating company: is a constant term, which indicates the reference value of the secondary network temperature when the outdoor temperature is zero, corresponding to the initial set temperature under the design conditions, or the basic temperature when there is no external temperature influence; is the linear term coefficient, which reflects the linear response of the secondary network temperature to the change of outdoor temperature and reflects the basic energy-saving regulation strategy; is the quadratic term coefficient, which describes the nonlinear effect and reflects the acceleration of temperature change. It means that at extreme temperatures, the system response is more significant, reflecting the nonlinear characteristics of the heat load. is the cubic term coefficient, capturing dynamic changes; is the quartic term coefficient, which is used for high-order fitting to further optimize the model accuracy.

2. The method for controlling a heating network by establishing a nonlinear regression model according to claim 1, wherein: Determine the temperature correction value of the heating company due to different energy-saving policies and different degrees of aging of insulation measures ,exist Add the temperature correction value to the basis of Final target temperature: Get the water supply temperature of the heat exchange station in real time and return water temperature , calculate the current actual average temperature : The actual average temperature and Compare and control, change the opening of the electric regulating valve Since it takes a certain amount of time for the return water temperature of the secondary network to change, different adjustment intervals need to be determined according to the different heating areas of different heat exchange stations. and The difference is considered balanced within the temperature dormant zone, and stable heating does not participate in further regulation; the temperature dormant zone is set according to the accuracy of the collected supply and return water temperature sensors.

3. The method for controlling a heating network by establishing a nonlinear regression model according to claim 1, wherein: Calculate the heating load according to the heat load formula Whether it meets the requirements of the current heating system operation: 。

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