Heat exchange station optimization control method based on building room temperature soft measurement

Abstract

The invention provides a heat exchange station optimization control method based on building room temperature soft measurement. The heat exchange station optimization control method comprises the steps that related input parameters of heat exchange station operation and building heat supply are collected; selecting the room temperature of a reference household in the building as a reference room temperature; calculating a comprehensive indoor temperature value of the whole building based on the built building room temperature soft measurement model; predicting the load of the heat exchange station by using a Prohet model to obtain a preliminary load prediction result; correcting the preliminary load prediction result according to the set indoor target temperature value and the current comprehensive indoor temperature value; based on the corrected load prediction result, optimizing the operation strategy of the heat exchange station; and outputting the optimized operation parameters and related heat supply indexes of the heat exchange station. According to the method, the load value predicted by the Prohet model is corrected by using the comprehensive indoor temperature value calculated by using the room temperature soft measurement method, and the operation strategy of the heat exchange station is optimized by using the operation mode of mass flow regulation, so that the energy efficiency of the heat exchange station is improved.

Description

Technical Field

[0001] This invention belongs to the field of heat exchange station optimization control, and in particular relates to a heat exchange station optimization control method based on soft measurement of building room temperature. Background Technology

[0002] With increasing global attention to climate change and environmental pollution, improving the energy efficiency of heating systems is a future development trend. This can be achieved by adopting high-efficiency heat exchange equipment, optimizing the design and operation of heating networks, and improving the control strategies of heating systems, thereby reducing energy consumption, minimizing energy waste, and increasing the energy utilization efficiency of heating systems.

[0003] Heat exchange stations are a crucial component of heating systems, transferring and distributing heat energy through heat exchangers. Optimizing the design and operation of heat exchange stations is of great significance for improving the energy efficiency of heating systems, reducing energy consumption, and minimizing environmental pollution.

[0004] During the operation of a heat exchange station, optimizing its operation strategy can achieve balanced heat distribution and optimized control. This strategy can include optimizing heating load forecasting, heat exchange station scheduling, and control strategies. However, heat exchange stations currently still have the following shortcomings: 1. Inappropriate control strategy: If the control strategy of the heat exchange station is inappropriate, such as inaccurate temperature control or unstable water flow control, it may lead to energy waste and a decrease in heating effect. 2. Untimely maintenance: If the equipment of the heat exchange station is not maintained in a timely manner, such as the regular maintenance of pumps and the cleaning of heat exchangers, it may lead to a decline in equipment performance, an increase in energy consumption, or even equipment failure. 3. Insufficient indoor temperature monitoring: There are not enough indoor temperature monitoring points in buildings or building complexes, making it difficult to measure the heating effect and affecting the operation strategy of heat exchange stations. 4. Lack of energy monitoring and management: The heat exchange station lacks effective energy monitoring and management methods, making it impossible to accurately understand the operation and energy consumption of the heating system, and also impossible to discover and solve problems in a timely manner. Summary of the Invention

[0005] In view of this, the present invention aims to propose an optimized control method for heat exchange stations based on soft measurement of building room temperature, so as to at least solve one of the problems in the background art.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows: Optimization control methods for heat exchange stations based on soft measurement of building room temperature include: Collect relevant input parameters for heat exchange station operation and building heating; The room temperature of a benchmark household in the building is selected as the benchmark room temperature. Combined with other input parameters, the target room temperature of unmonitored users is calculated, and a soft measurement model of room temperature is constructed. Based on the constructed soft-sensor model of building room temperature, the comprehensive indoor temperature value of the entire building is calculated; The load of the heat exchange station was predicted using the Prohet model, and preliminary load prediction results were obtained. The preliminary load forecast results are revised based on the set indoor target temperature value and the current comprehensive indoor temperature value; Based on the revised load forecast results, optimize the operation strategy of the heat exchange station; Output optimized operating parameters and related heating indicators for the heat exchange station.

[0007] Furthermore, the relevant input parameters for the operation of the heat exchange station and building heating include: Heating status of building users, indoor temperature monitoring sequence of building monitoring users, return water temperature monitoring sequence of building users, weather condition monitoring sequence, trained heat exchange station load prediction model, and heat exchange station design parameters.

[0008] Furthermore, the indoor temperature of a reference unit in the selected building is used as the reference indoor temperature. This is then corrected using other input parameters to estimate the indoor temperature value of the unmonitored unit within the target indoor temperature range. : ; in, —Base household indoor temperature, °C; —Weight; —Indoor temperature correction function considering longitudinal temperature gradient; —Base user floor height, in meters; —Target user floor height, in meters; —Indoor temperature correction function considering the influence of solar radiation; —The layout of the standard unit; —The apartment type of the target households; —Time type, divided into morning, noon, and afternoon; —Indoor temperature correction function considering the influence of apartment layout; —Indoor temperature correction function considering the impact of the target household's power outage and the number of surrounding households experiencing power outages; —Whether the benchmark household has stopped receiving electricity. If the benchmark household has stopped receiving electricity, the value of this variable is 1; if the benchmark household has not stopped receiving electricity, the value of this variable is 0. —Number of households around the benchmark household whose electricity supply has been suspended; the number of households around the benchmark household whose electricity supply has been suspended is ; —Whether to suspend power supply to the target households; —Number of households around the target household whose power supply has been suspended; —Indoor temperature correction function considering the influence of return water temperature; —Custom correction value.

[0009] Furthermore, based on the constructed soft-sensor model of building room temperature, the comprehensive indoor temperature value of the entire building is calculated, including: Indoor temperature values ​​for each unmonitored user were calculated based on the constructed soft-sensor model of building room temperature. ; Monitor users' indoor temperature value series Calculated indoor temperature value sequence for unmonitored users Merged into a single temperature value sequence ; Calculate the comprehensive indoor temperature value and the temperature value sequence. The average indoor temperature of heating users in China is used as the comprehensive indoor temperature value. .

[0010] Furthermore, the Prohet model is used to predict the load of the heat exchange station, treating the load as a combination of trend, date, and holiday factors for modeling and analysis. ; in: — This represents the trend term, used to fit the non-periodic term in the load sequence of the heat exchange station; — Represents the periodic term, used to fit the periodic term in the load sequence of the heat exchange station; — This represents the holiday term, used to fit the impact of holidays in the load sequence of the heat exchange station; — Represents error factors that were not taken into account by the model.

[0011] Furthermore, the step of correcting the preliminary load forecast results based on the set indoor target temperature value and the current comprehensive indoor temperature value includes: Based on indoor temperature setting value Current comprehensive indoor temperature value The formula for correcting the load forecast value of the heat exchange station is shown below: ; in: — Corrected load forecast for heat exchange stations, in kW; —Predicted load values ​​for the heat exchange station based on the Prohet model, in kW; —Linear correction term constant; —Indoor temperature setting; —Comprehensive indoor temperature value.

[0012] Furthermore, based on the corrected load forecast results, the heat exchange station operation strategy is optimized, and the heat exchange station adopts a mass flow rate regulation operation mode. The decision variable is: the secondary side supply water temperature of the heat exchange station. and secondary flow rate of heat exchange station After determining the secondary side water supply temperature and flow rate of the heat exchange station, the pump frequency and primary side water supply valve opening are controlled according to the local PID control.

[0013] Furthermore, this solution discloses an electronic device, including a processor and a memory communicatively connected to the processor and used to store executable instructions of the processor, wherein the processor is used to execute a heat exchange station optimization control method based on soft measurement of building room temperature.

[0014] Furthermore, this solution discloses a server, including at least one processor and a memory communicatively connected to the processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the processor to cause the at least one processor to execute a heat exchange station optimization control method based on soft measurement of building room temperature.

[0015] Furthermore, this solution discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements an optimized control method for heat exchange stations based on soft measurement of building room temperature.

[0016] Compared with existing technologies, the heat exchange station optimization control method based on soft measurement of building room temperature described in this invention has the following advantages: The heat exchange station optimization control method based on soft measurement of building room temperature described in this invention uses the comprehensive indoor temperature value calculated by the soft measurement method to correct the load value predicted by the Prohet model, and uses the mass flow rate regulation operation mode to optimize the operation strategy of the heat exchange station, thereby improving the energy efficiency of the heat exchange station. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the heat exchange station optimization control method based on soft measurement of building room temperature as described in an embodiment of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0019] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] This invention relates to an optimized control method for heat exchange stations based on soft measurement of building room temperature. The method uses the comprehensive indoor temperature value calculated by the soft measurement method to correct the load value predicted by the Prohet model, and optimizes the operation strategy of the heat exchange station under the mass-flow regulation operation mode.

[0023] The specific implementation steps of this invention are as follows: Step 1: Input parameters; Input parameters: including building user heating information (user height) Apartment type Whether each user has stopped receiving power. Number of households with outages in the vicinity of each user ), Building monitoring household indoor temperature monitoring sequence Building user return water temperature monitoring sequence Weather (Sunny / Cloudy) Monitoring Sequence The trained Prohet model for the heat exchange station and the design load of the heat exchange station. The design water supply temperature of the heat exchange station The design return water temperature of the heat exchange station .

[0024] Step 2: Construct a soft measurement model of building room temperature; Select any household as the baseline household, and record the room temperature of this baseline household. As a reference room temperature, and at this reference room temperature The indoor temperature value of the target household (households without room temperature monitoring) was obtained after correction. ,in: ; In the formula: —Base household indoor temperature, °C; —Weight; —Indoor temperature correction function considering longitudinal temperature gradient; —Base user floor height, in meters; —Target user floor height, in meters; —Indoor temperature correction function considering the influence of solar radiation; —The layout of the standard unit; —The apartment type of the target households; —Time type, divided into morning, noon, and afternoon; —Indoor temperature correction function considering the influence of apartment layout (window-to-wall ratio); —Indoor temperature correction function considering the impact of the target household's power outage and the number of surrounding households experiencing power outages; —Whether the benchmark household has stopped receiving electricity. If the benchmark household has stopped receiving electricity, the value of this variable is 1; if the benchmark household has not stopped receiving electricity, the value of this variable is 0. —Number of households around the benchmark household whose electricity supply has been suspended; the number of households around the benchmark household whose electricity supply has been suspended is ; —Whether to suspend power supply to the target households; —Number of households around the target household whose power supply has been suspended; —Indoor temperature correction function considering the influence of return water temperature; —Custom correction value; 2.1 Includes an indoor temperature correction function that considers the longitudinal temperature gradient: 1. Indoor temperature monitoring sequence based on monitored households Regression longitudinal temperature gradient function

[0025] a. First, the building is divided into reasonable zones based on the number of floors, namely high zone, middle zone, and low zone. The number of building zones should be reasonably divided according to the actual situation of the building. b. Calculate the average indoor temperature of each monitored household on each floor of each time zone. ; c. Calculate the longitudinal temperature gradient of each zone. ;in Represents the lower zone. Representing the Central District, Represents the High District.

[0026] d. Obtain the longitudinal temperature gradient function that considers temperature differences along the floor height. (Piecewise linear function).

[0027] ; In the formula: —Longitudinal temperature gradient function; —Temperature gradient function in the lower region; —Temperature gradient function in the middle zone; —Temperature gradient function in the high region; — Height, in meters; —Lower zone height, m; —Central zone height, m; — Height of the high-altitude area, in meters; 2. Indoor temperature correction function considering longitudinal temperature gradient ; 2.2 Includes an indoor temperature correction function that considers the influence of apartment layout (window-to-wall ratio). Different apartment layouts have different exterior wall and window areas (window-to-wall ratios), resulting in variations in indoor temperature under the same outdoor meteorological parameters. Therefore, each apartment within the building is categorized according to its layout, and its indoor temperature is monitored using historical data. Indoor temperature values ​​were selected from those monitored on cloudy days. Calculate the average indoor temperature for each apartment type. Calculate the difference between the average indoor temperatures of various apartment types and create the following matrix (3x3): ; That is, based on the indoor temperature correction matrix that takes into account the influence of apartment layout. Find the indoor temperature correction values ​​for different apartment types: ; 2.3 Includes an indoor temperature correction function that takes into account the effects of solar radiation: Because different apartment layouts have different exterior wall orientations and window areas, the indoor temperature of different apartment layouts can vary at the same time due to solar radiation. Each apartment in the building is categorized according to its layout, and historical indoor temperature monitoring data is used to determine the appropriate temperature. Indoor temperature values ​​monitored on sunny days were selected. Calculate the average indoor temperature of each apartment type at different times. Calculate the difference between the average indoor temperatures for each time period (morning, noon, and afternoon) and for each apartment type, and create the following matrix (3x3): ; The indoor temperature difference matrix for each time period is shown below: ; ; ; The formulas for calculating the indoor temperature correction matrix considering the influence of solar radiation at different times are shown below: ; ; ; In the formula: —Indoor temperature difference matrix during the morning period; —Indoor temperature difference matrix during midday; —Indoor temperature difference matrix during the afternoon period; —Indoor temperature correction matrix for the morning period affected by solar radiation; —Indoor temperature correction matrix based on solar radiation during midday; —Indoor temperature correction matrix for the afternoon period affected by solar radiation; That is, the correction value of indoor temperature is obtained based on the indoor temperature correction matrix affected by solar radiation in each time period.

[0028] 2.4 Includes an indoor temperature correction function that considers whether the power supply is interrupted and the impact of the number of surrounding users experiencing power outages: a. Based on whether the user has stopped supplying the service Number of households around the room whose electricity supply has been suspended Users are divided into several categories.

[0029] b. Calculate the average indoor temperature for each type of user. Fitting based on least squares algorithm , , A quadratic function in two variables: ; c. Calculate the indoor temperature correction function for the target household compared to the baseline household, taking into account whether the user has stopped supplying electricity and the number of surrounding users who have stopped supplying electricity: ; 2.5 Includes an indoor temperature correction function that considers the influence of return water temperature: a. Based on the historical indoor temperature sequence of monitored households Historical sequence of return water temperature for monitored households The least squares algorithm was used to regress the indoor temperature values ​​of monitored households. and return water temperature value Fitting function between ; b. Calculate the indoor temperature correction function for the target household, considering the influence of return water temperature, compared to the baseline household: ; Step 3: Calculate the overall indoor temperature value; The comprehensive indoor temperature value of the building is calculated based on the soft-sensor model of building temperature constructed in step 2, specifically including: a. Calculate the indoor temperature values ​​of each unmonitored user based on the constructed soft-sensor model of building room temperature. ; b. Monitor the indoor temperature value sequence of users Calculated indoor temperature value sequence for unmonitored users Merged into a single temperature value sequence ; c. Calculate the comprehensive indoor temperature value and sequence the temperature values. The average indoor temperature of central heating users (whose heating supply has not been suspended) is used as the comprehensive indoor temperature value. .

[0030] Step 4: Predict the load of the heat exchange station based on the Prohet model; The Prohet model views heat exchange station load forecasting as a combination of three parts: trend, date, and holiday. The Prohet model is a forecasting model for time series data. It is applicable to various types of time series data, including data with seasonal and non-linear trends, and can automatically detect and adapt to seasonal and non-linear patterns in the data.

[0031] The Prohet model trained in this invention treats heat exchanger load forecasting as a combination of three parts: trend, date, and holiday. The model is shown below: ; In the formula: — This represents the trend term, used to fit the non-periodic term in the load sequence of the heat exchange station; — Represents the periodic term, used to fit the periodic term in the load sequence of the heat exchange station (e.g., the daily heat exchange load periodic term); — This represents the holiday term, used to fit the impact of holidays (e.g., weekends, major holidays) in the load sequence of the heat exchange station. — Represents error factors that were not taken into account by the model.

[0032] a. The input parameters for training the heat exchange station load prediction model are: time series. Heat exchange station load sequence ; b. Training a predictive model for heat exchange station load forecasting; c. Perform load forecasting for the heat exchange station in the next control time interval. The load forecast value is... ; Step 5: Adjustment of heat exchange station load forecast; Based on indoor temperature setting value Current comprehensive indoor temperature value The formula for correcting the load forecast value of the heat exchange station is shown below: ; In the formula: — Corrected load forecast for heat exchange stations, in kW; —Predicted load values ​​for the heat exchange station based on the Prohet model, in kW; —Linear correction term constant; —Indoor temperature setting; —Comprehensive indoor temperature value; Step 6: Optimize the operation strategy of the heat exchange station; Optimizing the secondary flow of the heat exchange station based on the mass flow rate regulation operation mode and secondary water supply temperature value The optimization of the heat exchange station operation strategy involves adopting a mass flow rate regulation operation mode, with the decision variable being the secondary side water supply temperature of the heat exchange station. and secondary flow rate of heat exchange station After determining the secondary side water supply temperature and flow rate of the heat exchange station, the pump frequency and primary side water supply valve opening are controlled according to the local PID control.

[0033] Based on predicted load values ​​of heat exchange stations Heat exchange station design load Calculate the load ratio The calculation formula is as follows: ; Flow ratio of quality-flow regulation load ratio The formula for calculating the polynomial is as follows: ; In the formula: —Polynomial coefficients, taken as 0.459; —Polynomial coefficients, taken as 0.785; — Polynomial coefficients, taken as -0.244.

[0034] Secondary flow rate of heat exchange station The calculation formula is as follows: ; Based on load ratio Flow ratio Design water supply temperature Design return water temperature The formula for calculating the temperature difference between the secondary supply and return water is as follows: ; In the formula: —Secondary side supply and return water temperature difference, °C; —Secondary side design water supply temperature, °C; —Secondary side design return water temperature, °C; —Flow ratio; The formula for calculating the secondary return water temperature of mass-flow regulation is as follows: ; In the formula: —Secondary side supply and return water temperature difference, °C; The formula for calculating the secondary side supply water temperature of the mass-flow regulation is as follows: ; In the formula: —Secondary side supply and return water temperature difference, °C; Step 7: Output parameters; Output parameters: Predicted indoor temperature Comprehensive indoor temperature value Secondary water supply temperature of the heat exchange station Secondary flow rate of heat exchange station ; The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the control of heat exchange stations based on soft measurement of building room temperature, characterized in that, include: Collect relevant input parameters for heat exchange station operation and building heating; The room temperature of a benchmark household in the building is selected as the benchmark room temperature. Combined with other input parameters, the target room temperature of unmonitored users is calculated, and a soft measurement model of room temperature is constructed. Based on the constructed soft-sensor model of building room temperature, the comprehensive indoor temperature value of the entire building is calculated; The load of the heat exchange station was predicted using the Prohet model, and preliminary load prediction results were obtained. The preliminary load forecast results are revised based on the set indoor target temperature value and the current comprehensive indoor temperature value; Based on the revised load forecast results, optimize the operation strategy of the heat exchange station; Output optimized operating parameters and related heating indicators for the heat exchange station.

2. The optimized control method for heat exchange stations based on soft measurement of building room temperature as described in claim 1, characterized in that: The relevant input parameters for the operation of the heat exchange station and building heating include: Heating status of building users, indoor temperature monitoring sequence of building monitoring users, return water temperature monitoring sequence of building users, weather condition monitoring sequence, trained heat exchange station load prediction model, and heat exchange station design parameters.

3. The optimized control method for heat exchange stations based on soft measurement of building room temperature as described in claim 1, characterized in that, The indoor temperature of a benchmark household in the selected building is used as the benchmark temperature. This is then adjusted based on other input parameters to calculate the indoor temperature value of the unmonitored household within the target indoor temperature range. This includes: Indoor temperature monitoring sequence based on monitored households Regression longitudinal temperature gradient function Considering the indoor temperature correction function for the longitudinal temperature gradient.

4. The optimized control method for heat exchange stations based on soft measurement of building room temperature as described in claim 1, characterized in that, The building temperature soft-sensor model, based on the constructed building temperature, calculates the overall indoor temperature value of the entire building, including: Indoor temperature values ​​for each unmonitored user were calculated based on the constructed soft-sensor model of building room temperature. ; Monitor users' indoor temperature value series Calculated indoor temperature value sequence for unmonitored users Merged into a single temperature value sequence ; Calculate the comprehensive indoor temperature value and the temperature value sequence. The average indoor temperature of heating users in China is used as the comprehensive indoor temperature value. .

5. The optimized control method for heat exchange stations based on soft measurement of building room temperature according to claim 1, characterized in that, In the prediction of heat exchange station load using the Prohet model, the heat exchange station load is modeled and analyzed as a combination of three parts: trend, date, and holiday.

6. The optimized control method for heat exchange stations based on soft measurement of building room temperature as described in claim 1, characterized in that, The step of correcting the preliminary load forecast results based on the set indoor target temperature value and the current comprehensive indoor temperature value includes: Based on indoor temperature setting value Current comprehensive indoor temperature value The formula for correcting the load forecast value of the heat exchange station is shown below: ； in: — Corrected load forecast for heat exchange stations, in kW; —Predicted load values ​​for the heat exchange station based on the Prohet model, in kW; —Linear correction term constant; —Indoor temperature setting; —Comprehensive indoor temperature value.

7. The optimized control method for heat exchange stations based on soft measurement of building room temperature as described in claim 1, characterized in that, Based on the revised load forecast results, the operation strategy of the heat exchange station is optimized. The heat exchange station adopts a mass flow rate regulation operation mode, and the decision variable is: the secondary side supply water temperature of the heat exchange station. and secondary flow rate of heat exchange station After determining the secondary side water supply temperature and flow rate of the heat exchange station, the pump frequency and primary side water supply valve opening are controlled according to the local PID control.

8. An electronic device comprising a processor and a memory communicatively connected to the processor and used for storing processor-executable instructions, characterized in that: The processor is used to execute the heat exchange station optimization control method based on soft measurement of building room temperature as described in any one of claims 1-7.

9. A server, characterized in that: It includes at least one processor and a memory communicatively connected to the processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the processor to cause the at least one processor to perform the heat exchange station optimization control method based on building room temperature soft measurement as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the heat exchange station optimization control method based on soft measurement of building room temperature as described in any one of claims 1-7.

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