Ground source heat pump and gas boiler operation simulation system in severe cold areas

Abstract

The present invention discloses a ground source heat pump and gas boiler operation simulation system in severely cold regions, including a heat supply correction model construction, ground source / gas heating estimation, heat supply scheduling and allocation, and correction model optimization module. Through the historical operation data of the ground source heat pump subsystem, heating correction models related to soil geology and environmental climate are respectively constructed based on the single variable principle, the estimated heat supply value of the ground source heat pump subsystem is dynamically corrected, and the heat supply of the gas boiler subsystem and the required heat load of the building complex are estimated. The heating output of the two subsystems is intelligently allocated, and the heat supply correction model is optimized through the actual heat supply deviation. The present invention can effectively overcome the problem of ground source heat pump heating supply deviation caused by soil thermal imbalance and climate fluctuations in severely cold regions, thereby improving the accuracy of energy efficiency estimation of the heating system, facilitating the optimization of the scheduling strategy, and having dynamic compensation and model self-evolution capabilities, and can adapt to complex environmental changes.

Description

Technical Field

[0001] The present invention relates to the field of energy dispatching and distribution of heating systems, and particularly to an operation simulation system of a ground source heat pump and a gas boiler in severely cold regions. Background Art

[0002] In severely cold regions, a stable and reliable heating system is a key infrastructure to ensure residents' lives and industrial production. At present, the combined heating system constructed by combining the ground source heat pump subsystem and the gas boiler subsystem has become the preferred solution to meet the complex heating needs in severely cold regions, relying on the efficient renewable energy utilization characteristics of the ground source heat pump and the flexible emergency adjustment capabilities of the gas boiler. The two subsystems can give full play to their respective advantages through coordinated operation and effectively ensure a stable supply of heat. However, the rationality of the scheduling and accuracy of the operation control of the ground source heat pump and the gas boiler in this combined heating system directly affect the overall heating efficiency and energy consumption level of the system. How to achieve scientific scheduling and coordinated optimization of the two has become a core issue in improving the energy efficiency of the heating system, reducing operating costs, and promoting energy conservation and emission reduction.

[0003] The existing scheduling and planning methods of heating systems still have some limitations and shortcomings in practical applications.

[0004] For example, the existing Chinese patent publication number CN118780176A discloses a method and device for planning the installed capacity of heat source equipment. The method includes: determining the annual cost value, carbon emissions, and operating energy consumption of the heating system during the heating period under different installed capacities; establishing a multi-objective optimization model for the installed capacity of the heat source equipment of the heating system based on the annual cost value, carbon emissions, and operating energy consumption; inputting the weights of the economic indicators, carbon emissions indicators, and energy consumption indicators preset by the user into the multi-objective optimization model, and using a particle swarm algorithm to solve and obtain the installed capacity of the air source heat pump and gas boiler of the heating system that meets the user's preset weights. This invention can quickly and accurately calculate the installed capacity of the air source heat pump and gas boiler that meets the user's preferences, balancing good ecological and economic benefits while ensuring the residents' heat needs.

[0005] For another example, the existing Chinese patent with publication number CN115455709A discloses a simulation and configuration method for a low-carbon integrated energy system taking into account the installation of carbon capture equipment. The method constructs a coupling model of an integrated energy system taking into account the installation of carbon capture equipment, which includes a carbon capture power plant model, a P2G equipment model, a photovoltaic model, a gas turbine model, a waste heat boiler model, a gas boiler model, an electric boiler model, a ground source heat pump model, an energy storage device model, a power network model, and a thermal network model, as well as an economic dispatch model of an urban area integrated energy system taking into account the installation of carbon capture equipment. Then, a regional electric-thermal integrated energy system consisting of a modified IEEE33-node distribution network and a six-node thermal network is selected. The system is simulated and solved using the YALMIP and GUROBI solvers on the MATLAB platform. Finally, the configuration stage of the integrated energy system is analyzed based on the solution results and the optimal configuration scheme is obtained, thereby optimizing the operation and configuration of the electric-thermal multi-energy complementary integrated energy system.

[0006] The above patents conduct calculation analysis or simulation solutions based on indicators or requirements to seek the optimal configuration and scheduling of each device or subsystem in the integrated energy system to achieve efficient operation of the integrated energy system. This is under the condition that the energy supply of each device or subsystem is known, but does not take into account whether the energy output estimate of the device or subsystem itself is accurate. The estimated results of energy output will directly affect the scheduling and configuration decisions of the integrated energy system.

[0007] Especially for renewable energy equipment or systems such as ground-source heat pumps, their energy output is affected by many factors including the environment, and requires dynamic and in-depth analysis. The existing conventional or idealized calculations will cause a large deviation between the theoretical output and the actual output and are not accurate enough. If this deviation is too large, the configuration result will not be optimal, which will affect the accuracy and rationality of the scheduling decision. Improper scheduling will not only cause energy waste and increase operating costs, but may also accelerate soil thermal imbalance and affect the long-term performance of the heat pump. Summary of the Invention

[0008] In response to the above problems, the present invention proposes a ground source heat pump and gas boiler operation simulation system in severe cold areas to realize the function of energy scheduling and allocation of the heating system.

[0009] The technical solution adopted by the present invention to solve its technical problems is: the present invention provides a ground source heat pump and gas boiler operation simulation system in severe cold areas, including: a heating correction model building module: based on the heating deviation of the historical operation of the ground source heat pump subsystem and the corresponding soil geological parameters and environmental climate parameters, combined with the single variable principle and through machine learning, a heating correction model related to soil geology and environmental climate is constructed.

[0010] Ground source heating estimation module: obtains soil geological parameters and environmental climate parameters within the prediction period, substitutes them into the heating correction model to analyze and obtain the correction amount of the ground source heat pump subsystem's heating supply, and estimates the heating supply of the ground source heat pump subsystem based on its theoretical heating supply.

[0011] Gas heating estimation module: estimates the heating capacity of the gas boiler subsystem within the forecast period based on the calculation formula of the gas boiler heating capacity.

[0012] Heating scheduling and allocation module: Based on the historical data of the building complex's heat load, the required heat load of the building complex within the forecast period is estimated. According to the estimated heat supply results of the two subsystems and the required heat load of the building complex, the heating output of the two subsystems is dynamically allocated.

[0013] Correction model optimization module: evaluates the accuracy of the heating correction model and optimizes it based on the difference between the actual value of the heating supply of the ground source heat pump subsystem and the estimated result.

[0014] Compared with the existing technology, the cold region ground source heat pump and gas boiler operation simulation system described in the present invention has the following beneficial effects: 1. Improving the accuracy of ground source heat pump heating estimation: The present invention quantifies the impact of natural factors on the heating supply of the ground source heat pump subsystem through a two-dimensional correction model of soil geology and environmental climate, solving the defect of traditional theoretical calculations ignoring environmental variables, and the heating supply correction amount is signed to realize dynamic compensation of positive and negative deviations.

[0015] 2. Optimize dual-system heating scheduling: This invention dynamically allocates the output of the two subsystems based on the heat load forecast of the building complex and the estimated results of the heat supply of the ground source heat pump subsystem and the gas boiler subsystem, giving priority to the use of low-carbon ground source heat pumps, and supplementing them with gas when they are insufficient, thereby improving the energy efficiency of the heating system and reducing carbon emissions.

[0016] 3. Support continuous model optimization: The present invention evaluates the accuracy of the heat supply correction model of the ground source heat pump subsystem by measuring the deviation between the actual heat supply of the ground source heat pump subsystem and the estimated value, and realizes the adaptive evolution of the model by dynamically expanding the training data set, thereby adapting to long-term changes in geology and climate.

[0017] 4. Enhance system robustness: This invention distinguishes the independent influences of soil geology and environmental climate factors, avoids overfitting of the heating correction model caused by multivariable coupling, and improves the adaptability of the ground-source heat pump subsystem to the complex environment of severe cold regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is a system module connection diagram of the present invention.

[0020] Figure 2 Schematic diagram of the workflow of the present invention.

[0021] Figure 3 This is a structural diagram of the severe cold area heating system of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] See also Figure 1 、 Figure 2 and Figure 3 As shown, the present invention provides a ground source heat pump and gas boiler operation simulation system in severe cold areas, including a heating correction model building module, a ground source heating estimation module, a gas heating estimation module, a heating scheduling and allocation module, and a correction model optimization module.

[0024] The ground source heating estimation module is connected to the heating correction model building module and the gas heating estimation module respectively, and the heating scheduling and allocation module is connected to the gas heating estimation module and the correction model optimization module respectively.

[0025] It should be noted that the heating system in extremely cold areas includes a ground-source heat pump subsystem that extracts soil heat through a buried pipe heat exchanger, a gas boiler subsystem that supplements heating by burning natural gas, and a hybrid system coupling interface. One end of the hybrid system coupling interface is connected in parallel to the ground-source heat pump subsystem and the gas boiler subsystem, and the other end is connected to the building complex.

[0026] It's important to note that in heating systems in severely cold regions, the geothermal heat pump subsystem, relying on a heat exchange medium from the natural environment, is more sensitive to factors such as geography, geology, and climate. In contrast, the gas boiler subsystem generates heat directly through fuel combustion and is less susceptible to external environmental interference. The geothermal heat pump subsystem relies on underground soil for heat exchange. Its core principle is to directly capture heat or cold energy from the natural environment through buried pipes. Therefore, geography and geology directly determine the availability and efficiency of the heat exchange medium, while climate and ambient temperature affect system performance by altering the thermal state of the medium itself. Furthermore, long-term, seasonal, unidirectional heat migration can cause imbalances in the underground thermal field, further weakening system efficiency. These dynamic changes in natural factors make the geothermal heat pump subsystem highly sensitive to environmental conditions. Therefore, the geothermal heat pump subsystem is more susceptible to external or environmental factors. Analyzing the correlation between these influencing factors and the geothermal heat pump subsystem's heat supply and improving the accuracy of heat supply estimates are crucial for the rational scheduling and allocation of energy in the heating system.

[0027] The heating correction model building module is based on the heating deviation of the historical operation of the ground source heat pump subsystem and the corresponding soil geological parameters and environmental climate parameters, combined with the single variable principle and through machine learning to build a heating correction model related to soil geology and environmental climate.

[0028] Furthermore, the specific working process of the heating correction model building module includes: S1: extracting the massive historical operation data of the ground source heat pump subsystem in the cold region stored in the database, obtaining the heating supply of each historical operation of the ground source heat pump subsystem and comparing it with the theoretical heating supply obtained based on the theoretical heating supply calculation formula to obtain the heating supply deviation of each historical operation.

[0029] It should be noted that the heat supply deviation refers to the difference between the actual heat supply in historical operation and the theoretical heat supply, and the heat supply deviation can be positive, negative, or zero.

[0030] S2: and obtain soil geological parameters and environmental climate parameters corresponding to each historical operation of the ground source heat pump subsystem, wherein the soil geological parameters include soil temperature, soil moisture content and groundwater flow velocity, and the environmental climate parameters include outdoor temperature, day and night temperature difference and wind speed.

[0031] S3: Extract the range of soil geological parameters and the range of environmental climate parameters suitable for the theoretical heat supply calculation of the ground source heat pump subsystem stored in the database, and record it as the ideal range of soil geological parameters and environmental climate parameters.

[0032] It should be noted that the soil geological parameter range and environmental climate parameter range suitable for the theoretical heating supply calculation of the geothermal heat pump subsystem are obtained based on historical experience. This means that if the soil geological parameters and environmental climate parameters are both within their corresponding ranges, the deviation between the theoretical heating supply calculated by the geothermal heat pump subsystem and the actual heating supply is less than the set deviation threshold and can be ignored. That is, the soil geological parameters and environmental climate parameters at this time do not affect the heating supply of the geothermal heat pump subsystem.

[0033] S4: If the soil geological parameters corresponding to a certain historical operation do not fall within the ideal range but the environmental climate parameters fall within the ideal range, the data of this historical operation will be included in the data set used to train the soil geological correlation heating correction model.

[0034] If the environmental climate parameters corresponding to a certain historical operation do not fall within the ideal range but the soil geological parameters fall within the ideal range, the data of this historical operation will be included in the data set used to train the environmental climate-related heating correction model.

[0035] It should be noted that not belonging to the ideal range means that any one of the soil geological parameters or environmental climate parameters does not belong to the corresponding ideal range, and belonging to the ideal range means that all items of the soil geological parameters or environmental climate parameters belong to the corresponding ideal range.

[0036] It should be noted that if the soil geological parameters and environmental climate parameters corresponding to a certain historical operation are within their ideal ranges, the data of this historical operation will not be used to train the heating correction model.

[0037] Furthermore, the theoretical heat supply calculation formula of the ground source heat pump subsystem in step S1 is: , where The theoretical heat supply, Indicates the heating energy efficiency ratio of the set ground source heat pump subsystem, Represents the compressor power of the ground source heat pump subsystem stored in the database, Indicates the heat loss rate of the set ground source heat pump subsystem, Indicates the runtime.

[0038] It should be noted that the heating energy efficiency ratio of the geothermal heat pump subsystem is related to the underground heat source temperature and the temperature of the heating side. The set value of the geothermal heat pump subsystem heating energy efficiency ratio refers to the heating energy efficiency ratio corresponding to the ideal underground heat source temperature and the heating side temperature within their respective ranges. In one specific embodiment, the heating energy efficiency ratio of the geothermal heat pump subsystem is 2.5-4.5.

[0039] It should be noted that the heat loss rate of the ground source heat pump subsystem is evaluated based on the buried pipe design and soil conditions. In a specific embodiment, the heat loss rate of the ground source heat pump subsystem is 5%-15%.

[0040] Furthermore, the specific method for obtaining the soil temperature of the ground source heat pump subsystem in step S2 is: demarcating the area where the heat source of the ground source heat pump subsystem is located, and evenly distributing a number of soil measurement points in the area where the heat source is located.

[0041] The soil temperature of each depth layer at each soil measuring point is collected by temperature sensors.

[0042] The soil temperature at the soil measuring point is obtained by weighted fusion calculation based on the soil temperature of each depth layer at the soil measuring point and the set weights of each depth layer.

[0043] The soil temperature at each soil measuring point is counted and the mean is calculated to obtain the soil temperature of the ground source heat pump subsystem.

[0044] It should be noted that the weight of each depth layer is set based on the degree of influence of the soil temperature at each depth layer on the heating supply of the ground-source heat pump subsystem, and the cumulative sum is 1. The greater the influence of the soil temperature at a certain depth layer on the heating supply of the ground-source heat pump subsystem, the greater the weight of that depth layer.

[0045] Furthermore, the specific working process of the heating correction model building module also includes: based on the heating supply deviation of each historical operation of the ground source heat pump subsystem in the training data set and the corresponding soil temperature, soil moisture content, and groundwater flow velocity, a model with soil temperature, soil moisture content, and groundwater flow velocity as input and heating supply deviation as output is constructed through a machine learning algorithm, and it is recorded as a soil geology-related heating correction model.

[0046] It should be noted that the above training data set refers to the data set for training the soil geology-related heating correction model.

[0047] It should be noted that ground-source heat pumps rely on underground soil for heat exchange. Soil temperature directly determines the heat exchange efficiency. Soil moisture content and groundwater flow velocity affect heat conductivity and heat migration. These three are the core geological factors that affect the heating capacity of ground-source heat pumps and can be quantitatively monitored through sensors. Therefore, soil temperature, soil moisture content, and groundwater flow velocity are selected to construct a soil-geology-related heating correction model. Doing so can directly link soil thermal characteristics, quantify the impact of geological parameters on heating supply deviations, and solve the problem of theoretical heating supply and actual deviations caused by changes in geological conditions. It also filters data through the single variable principle, eliminates climate interference, and ensures that the model focuses on the mapping relationship between geological factors and heating supply deviations.

[0048] Furthermore, the specific working process of the heating correction model building module also includes: constructing each group of historical operation data based on the heating deviation of each historical operation of the ground source heat pump subsystem in the training data set and the corresponding outdoor temperature, day and night temperature difference and wind speed.

[0049] Traverse each set of historical operation data. If the outdoor temperature of two sets of historical operation data is different but the day and night temperature difference and wind speed are the same, pair the two sets of historical operation data, and make the difference between the outdoor temperature and heating supply deviation of the two sets of historical operation data to obtain a set of outdoor temperature difference and heating supply deviation difference.

[0050] It should be noted that if the day and night temperature difference and wind speed of two sets of historical operation data are the same, it means that the values ​​of the day and night temperature difference and wind speed of the two sets of historical operation data are completely consistent or the value deviation is less than the set deviation threshold.

[0051] The outdoor temperature difference and heating supply deviation difference of each group are counted and classified into the training sub-dataset based on the single variable of outdoor temperature.

[0052] According to the training sub-dataset based on the single variable of outdoor temperature, the outdoor temperature difference is taken as the independent variable and the heating supply deviation difference is taken as the dependent variable. The quantitative mapping relationship between outdoor temperature and heating supply deviation is analyzed through machine learning algorithm.

[0053] Similarly, the quantitative mapping relationship between the day-night temperature difference and wind speed and the heating amount deviation is analyzed respectively.

[0054] According to the quantitative mapping relationship between outdoor temperature, day-night temperature difference and wind speed and heating supply deviation, a heating correction model associated with environmental climate is constructed.

[0055] It should be noted that the above training data set refers to the data set for training the environment-climate-related heating correction model.

[0056] It should be noted that outdoor temperature affects the thermal state of the soil and the energy efficiency of heat pumps, while the diurnal temperature difference and wind speed indirectly affect the heating supply by changing the heat loss rate. These three are the main climate variables in severely cold regions and can be obtained and quantified through meteorological data. Therefore, outdoor temperature, diurnal temperature difference, and wind speed are selected to construct a heating correction model associated with environmental climate. This can be done through a single variable method to remove the interference of multiple coupling factors, accurately establish a quantitative relationship between each climate parameter and the heating supply deviation, and cover the diurnal and seasonal variation characteristics of severely cold regions, improve the model's generalization ability for different climate conditions, and ensure the real-time and accuracy of heating supply estimation.

[0057] In this embodiment, the present invention distinguishes the independent influences of soil geology and environmental climate factors, avoids overfitting of the heating correction model caused by multivariable coupling, and improves the adaptability of the ground source heat pump subsystem to the complex environment of severe cold regions.

[0058] The ground source heating estimation module obtains soil geological parameters and environmental climate parameters within the prediction period, substitutes them into the heating correction model for analysis to obtain the correction amount of the ground source heat pump subsystem's heating supply, and estimates the heating supply of the ground source heat pump subsystem based on its theoretical heating supply.

[0059] Furthermore, the specific working process of the ground source heating estimation module is: obtaining soil geological parameters of the severe cold area within the prediction period, substituting them into the heating correction model associated with the soil geology to obtain the heating deviation based on the soil geology.

[0060] The environmental climate parameters of the severe cold area within the forecast period are obtained, and substituted into the heating correction model associated with the environmental climate to obtain the heating supply deviation based on the outdoor temperature, day and night temperature difference and wind speed, and then accumulated to obtain the heating supply deviation based on the environmental climate.

[0061] The heating supply deviations based on soil geology and environmental climate are accumulated to obtain the correction amount of heating supply of the local heat pump subsystem during the forecast period.

[0062] It should be noted that the correction value of the heat supply of the ground source heat pump subsystem contains a sign.

[0063] Based on the theoretical heat supply calculation formula of the ground source heat pump subsystem, the theoretical heat supply of the ground source heat pump subsystem within the prediction period is obtained.

[0064] The heat supply of the ground source heat pump subsystem is obtained by adding the correction amount of the heat supply of the ground source heat pump subsystem in the prediction period to the theoretical heat supply.

[0065] In this embodiment, the present invention quantifies the impact of natural factors on the heating supply of the ground source heat pump subsystem through a two-dimensional correction model of soil geology and environmental climate, thereby solving the defect of traditional theoretical calculations ignoring environmental variables, and the heating supply correction value is signed to realize dynamic compensation of positive and negative deviations.

[0066] The gas heating estimation module estimates the heating supply of the gas boiler subsystem within a prediction period based on a calculation formula for the heating supply of the gas boiler.

[0067] Furthermore, the specific working process of the gas heating estimation module is: calculating the theoretical heating capacity of the gas boiler subsystem within the prediction period according to the calculation formula of the theoretical heating capacity of the gas boiler subsystem stored in the database.

[0068] The historical operation data of the gas boiler subsystem in severe cold regions stored in the database are extracted to obtain the actual heating supply and the corresponding theoretical heating supply of each historical operation of the gas boiler subsystem. The average deviation between the actual heating supply and the theoretical heating supply of the gas boiler subsystem is calculated and recorded as the correction amount of the heating supply of the gas boiler subsystem.

[0069] The theoretical heat supply of the gas boiler subsystem within the prediction period is accumulated with the correction amount to obtain the heat supply of the gas boiler subsystem within the prediction period.

[0070] It should be noted that the calculation formula for the theoretical heat supply of the gas boiler subsystem is: , where The theoretical heat supply, Indicates gas consumption. Indicates the set lower calorific value of the gas. Indicates the set theoretical thermal efficiency, Indicates the runtime.

[0071] The heat supply scheduling and allocation module estimates the required heat load of the building group within the forecast period based on the historical data of the building group's heat load, and dynamically allocates the heat output of the two subsystems according to the heat supply estimation results of the two subsystems and the required heat load of the building group.

[0072] Furthermore, the specific working process of the heating scheduling and allocation module is: extracting the historical data of heat load of buildings in severe cold areas stored in the database, and obtaining the required heat load of the buildings in the forecast period based on year-on-year and month-on-month analysis.

[0073] It should be noted that the specific method for obtaining the demand heat load of the building complex within the forecast period is as follows: based on the historical data of heat load of building complexes in severe cold areas, the heat load demand curves of the building complexes in each historical year and the current year are obtained, the heat load of the forecast period in each historical year and the heat load of each historical period in the current year are intercepted, and the heat load of the building complex within the forecast period based on year-on-year and month-on-month estimates are analyzed respectively, and the weighted average is performed to obtain the demand heat load of the building complex within the forecast period.

[0074] If the required heat load of the building complex is less than or equal to the heating capacity of the ground source heat pump subsystem, the ground source heat pump subsystem will be started first for independent heating.

[0075] If the required heat load of the building complex is greater than the heat supply of the ground source heat pump subsystem, the gas boiler subsystem is triggered to supplement the heat supply, and the supplementary heat is the difference between the required heat load of the building complex and the heat supply of the ground source heat pump subsystem.

[0076] It should be noted that after obtaining the heating output ratio of the ground source heat pump subsystem and the gas boiler subsystem, the heating output of the two subsystems flowing into the building complex heating main pipeline is controlled through the hybrid system coupling interface installed in parallel at the heating outlets of the ground source heat pump subsystem and the gas boiler subsystem.

[0077] In this embodiment, the present invention dynamically allocates the output of the two subsystems based on the heat load prediction of the building complex and the estimated results of the heat supply of the ground source heat pump subsystem and the gas boiler subsystem, giving priority to the use of low-carbon ground source heat pumps, and supplementing them with gas when they are insufficient, thereby improving the energy efficiency of the heating system and reducing carbon emissions.

[0078] The correction model optimization module evaluates the accuracy of the heat supply correction model and optimizes it according to the difference between the actual value of the heat supply of the ground source heat pump subsystem and the estimated result.

[0079] Furthermore, the specific working process of the correction model optimization module is: the difference between the actual value and the estimated value of the heat supply of the ground source heat pump subsystem during the prediction period is recorded as the heat supply prediction deviation, and the heat supply correction model accuracy corresponding to the set heat supply prediction deviation interval is matched to obtain the heat supply correction model accuracy of the ground source heat pump subsystem.

[0080] If the accuracy of the heating correction model is less than the set accuracy threshold, the heating correction model is optimized.

[0081] It should be noted that the specific process of optimizing the heating correction model is: incorporating the operating data of the groundsource heat pump subsystem during the prediction period into the data set for training the heating correction model, expanding the data set to obtain a new data set, and reconstructing the heating correction model based on the new training set.

[0082] In this embodiment, the present invention evaluates the accuracy of the heat supply correction model of the ground source heat pump subsystem by the deviation between the actual heat supply of the ground source heat pump subsystem and the estimated value, and realizes the adaptive evolution of the model by dynamically expanding the training data set, thereby adapting to long-term changes in geology and climate.

[0083] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0084] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0085] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0086] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0087] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0088] Finally, 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 in the scope of protection of the present invention.

Claims

1. The ground source heat pump and gas boiler operation simulation system in severe cold areas is characterized by: include: Heating correction model building module: Based on the heating supply deviation of the ground source heat pump subsystem's historical operation and the corresponding soil geological parameters and environmental climate parameters, combined with the single variable principle and machine learning, a heating correction model related to soil geology and environmental climate is constructed; Ground source heating estimation module: obtains soil geological parameters and environmental climate parameters within the prediction period, substitutes them into the heating correction model to analyze and obtain the correction amount of the ground source heat pump subsystem's heating supply, and then estimates the heating supply of the ground source heat pump subsystem based on its theoretical heating supply; Gas heating estimation module: estimates the heating supply of the gas boiler subsystem within the forecast period based on the calculation formula of the heating supply of the gas boiler; Heating scheduling and allocation module: This module estimates the required heat load of the building complex within the forecast period based on the historical heat load data of the building complex. It dynamically allocates the heating output of the two subsystems based on the estimated heat supply of the two subsystems and the required heat load of the building complex. Correction model optimization module: evaluates the accuracy of the heating correction model and optimizes it based on the difference between the actual value of the heating supply of the ground source heat pump subsystem and the estimated result.

2. The cold region ground source heat pump and gas boiler operation simulation system according to claim 1 is characterized by: The specific working process of the heating correction model building module includes: S1: Extract the massive historical operation data of the ground source heat pump subsystem in the severe cold region stored in the database, obtain the heating value of each historical operation of the ground source heat pump subsystem, and compare it with the theoretical heating value calculated based on the theoretical heating value calculation formula to obtain the heating value deviation of each historical operation; S2: Obtain soil geological parameters and environmental climate parameters corresponding to each historical operation of the ground source heat pump subsystem, wherein the soil geological parameters include soil temperature, soil moisture content, and groundwater flow velocity, and the environmental climate parameters include outdoor temperature, day and night temperature difference, and wind speed; S3: extracting the soil geological parameter range and the environmental climate parameter range suitable for the theoretical heat supply calculation of the ground source heat pump subsystem stored in the database, and recording them as the ideal range of the soil geological parameter and the environmental climate parameter; S4: If the soil geological parameters corresponding to a certain historical operation do not fall within the ideal range but the environmental climate parameters do fall within the ideal range, the data of this historical operation will be included in the dataset used to train the soil geological correlation heating correction model; If the environmental climate parameters corresponding to a certain historical operation do not fall within the ideal range but the soil geological parameters fall within the ideal range, the data of this historical operation will be included in the data set used to train the environmental climate-related heating correction model.

3. The cold region ground source heat pump and gas boiler operation simulation system according to claim 2 is characterized by: The theoretical heat supply calculation formula of the ground source heat pump subsystem in step S1 is: , where The theoretical heat supply, Indicates the heating energy efficiency ratio of the set ground source heat pump subsystem, Represents the compressor power of the ground source heat pump subsystem stored in the database, Indicates the heat loss rate of the set ground source heat pump subsystem, Indicates the runtime.

4. The cold region ground source heat pump and gas boiler operation simulation system according to claim 2 is characterized by: The specific method for obtaining the soil temperature of the ground source heat pump subsystem in step S2 is: Delineate the heat source area of ​​the ground source heat pump subsystem and evenly distribute several soil measurement points within the heat source area; The soil temperature of each depth layer at each soil measuring point is collected by temperature sensors; The soil temperature at the soil measuring point is obtained by weighted fusion calculation based on the soil temperature of each depth layer at the soil measuring point and the weight of each depth layer set; The soil temperature at each soil measuring point is counted and the mean is calculated to obtain the soil temperature of the ground source heat pump subsystem.

5. The cold region ground source heat pump and gas boiler operation simulation system according to claim 2 is characterized by: The specific working process of the heating correction model building module also includes: Based on the heating supply deviations of the ground-source heat pump subsystem during each historical operation in the training dataset and the corresponding soil temperature, soil moisture content, and groundwater flow velocity, a model with soil temperature, soil moisture content, and groundwater flow velocity as input and heating supply deviation as output was constructed through a machine learning algorithm. This model was recorded as a soil geology-related heating correction model.

6. The cold region ground source heat pump and gas boiler operation simulation system according to claim 2 is characterized by: The specific working process of the heating correction model building module also includes: Each set of historical operation data is constructed based on the heating deviation of each historical operation of the ground source heat pump subsystem in the training data set and the corresponding outdoor temperature, day and night temperature difference and wind speed; Traverse each set of historical operation data. If the outdoor temperature of two sets of historical operation data is different but the day and night temperature difference and wind speed are the same, pair the two sets of historical operation data, and make the difference between the outdoor temperature and heat supply deviation of the two sets of historical operation data to obtain a set of outdoor temperature difference and heat supply deviation difference; The outdoor temperature difference and heat supply deviation difference of each group are counted and classified into the training sub-dataset based on the single variable of outdoor temperature; Based on a training sub-dataset based on a single variable of outdoor temperature, with outdoor temperature difference as the independent variable and heating supply deviation difference as the dependent variable, a machine learning algorithm is used to analyze the quantitative mapping relationship between outdoor temperature and heating supply deviation. Similarly, the quantitative mapping relationship between the day-night temperature difference and wind speed and the heat supply deviation is analyzed respectively; According to the quantitative mapping relationship between outdoor temperature, day-night temperature difference and wind speed and heating supply deviation, a heating correction model associated with environmental climate is constructed.

7. The cold region ground source heat pump and gas boiler operation simulation system according to claim 1 is characterized by: The specific working process of the ground source heating estimation module is as follows: Obtain soil geological parameters in severe cold regions within the forecast period and substitute them into the heating correction model associated with soil geology to obtain the heating deviation based on soil geology; Obtain the environmental climate parameters of the severe cold region within the forecast period, substitute them into the heating correction model associated with the environmental climate to obtain the heating supply deviation based on the outdoor temperature, day and night temperature difference and wind speed, and accumulate them to obtain the heating supply deviation based on the environmental climate; The heating supply deviations based on soil geology and environmental climate are accumulated to obtain the correction amount of heating supply of the local heat pump subsystem during the forecast period; Based on the theoretical heat supply calculation formula of the ground source heat pump subsystem, the theoretical heat supply of the ground source heat pump subsystem within the forecast period is obtained; The heat supply of the ground source heat pump subsystem is obtained by adding the correction amount of the heat supply of the ground source heat pump subsystem in the prediction period to the theoretical heat supply.

8. The cold region ground source heat pump and gas boiler operation simulation system according to claim 1 is characterized by: The specific working process of the gas heating estimation module is as follows: Calculate the theoretical heat supply of the gas boiler subsystem within the forecast period according to the calculation formula of the theoretical heat supply of the gas boiler subsystem stored in the database; Extract the historical operating data of the gas boiler subsystem in the severe cold region stored in the database, obtain the actual heating supply and the corresponding theoretical heating supply of each historical operation of the gas boiler subsystem, calculate the average deviation between the actual heating supply and the theoretical heating supply of the gas boiler subsystem, and record it as the correction value of the heating supply of the gas boiler subsystem; The theoretical heat supply of the gas boiler subsystem within the prediction period is accumulated with the correction amount to obtain the heat supply of the gas boiler subsystem within the prediction period.

9. The cold region ground source heat pump and gas boiler operation simulation system according to claim 1 is characterized by: The specific working process of the heat supply scheduling and allocation module is as follows: Extract the historical heat load data of buildings in cold regions stored in the database, and calculate the required heat load of the buildings in the forecast period based on year-on-year and month-on-month analysis; If the required heat load of the building complex is less than or equal to the heat supply of the ground source heat pump subsystem, the ground source heat pump subsystem will be activated first for independent heating; If the required heat load of the building complex is greater than the heat supply of the ground source heat pump subsystem, the gas boiler subsystem is triggered to supplement the heat supply, and the supplementary heat is the difference between the required heat load of the building complex and the heat supply of the ground source heat pump subsystem.

10. The cold region ground source heat pump and gas boiler operation simulation system according to claim 1 is characterized by: The specific working process of the correction model optimization module is as follows: The difference between the actual value and the estimated value of the heat supply of the ground source heat pump subsystem during the prediction period is recorded as the heat supply prediction deviation. The heat supply correction model accuracy of the ground source heat pump subsystem is matched according to the heat supply prediction deviation interval. If the accuracy of the heating correction model is less than the set accuracy threshold, the heating correction model is optimized.

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