Ground source heat pump system operation simulation prediction and actual measurement data feedback fusion control method

Abstract

The invention relates to the technical field of ground source heat pump system operation control, and provides a ground source heat pump system operation simulation prediction and actual measurement data feedback fusion control method, which comprises the following steps: S1, constructing a ground source heat pump system model, and inputting model parameters and initial boundary conditions; s2, selecting boundary conditions for long-period operation scheduling in the initial boundary conditions, and calculating to obtain a current long-period operation scheduling scheme; s3, selecting boundary conditions for short-period control in the initial boundary conditions, and calculating and issuing a short-period control strategy scheme; and S4, after the control of one short period is finished, acquiring field measured data, and circularly executing the steps S2-S4 until the operation of the current long period is finished. The operation energy efficiency of the combined type ground source heat pump system can be improved, the load prediction and scheduling strategy of the system is optimized, seamless connection between a long period and a short period is achieved, the intelligence and automation level of the system is improved, and the stability and safety of the ground source heat pump system are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of ground source heat pump system operation control, and in particular to a ground source heat pump system operation simulation prediction and measured data feedback fusion control method. Background Art

[0002] As a typical environmental protection and energy-saving technology, the ground source heat pump system has been widely used in the field of building heating and cooling in recent years. The system uses groundwater or soil as a heat source or cold source, and realizes the exchange of heat energy through a heat pump device, thereby providing cooling or heating functions. Compared with traditional air conditioning and heating systems, the energy efficiency of the ground source heat pump system is significantly improved, especially suitable for regional environments with small temperature differences, because the temperature difference required during its operation is relatively small, and it can provide a stable heat exchange effect under the condition of low energy consumption.

[0003] The composite ground source heat pump system combines the advantages of traditional ground source heat pump technology and peak load regulation system, and can maintain efficient operation of the system in a more complex load fluctuation environment. However, how to improve the operation energy efficiency of the composite ground source heat pump system and how to optimize the system's load forecasting and scheduling strategy are not effectively involved in traditional solutions, so the composite ground source heat pump system still has problems of high energy consumption, poor stability and safety during operation. Summary of the invention

[0004] The purpose of the present invention is to solve at least one technical problem in the background technology and to provide a ground source heat pump system operation simulation prediction and measured data feedback fusion control method.

[0005] To achieve the above object, the present invention provides a method for fusion control of ground source heat pump system operation simulation prediction and measured data feedback, comprising: S1. Construct a geothermal heat pump system model, input model parameters and initial boundary conditions, and perform long-term operation scheduling of the geothermal heat pump system and operation simulation of multiple short-term controls under long-term operation based on the geothermal heat pump system model; S2. Selecting the boundary conditions for the long-term operation scheduling of the ground source heat pump system model in the initial boundary conditions, and calculating the current long-term operation scheduling scheme of the ground source heat pump system model; S3. Based on the current long-cycle operation scheduling scheme, select the boundary conditions for short-cycle control of the ground source heat pump system model in the initial boundary conditions, calculate and issue the short-cycle control strategy scheme under the current long-cycle operation scheduling scheme of the ground source heat pump system model; S4. After a short-cycle control of the geothermal heat pump system model is completed, the field measured data is obtained, and the boundary conditions of the long-cycle operation scheduling and the boundary conditions of the short-cycle control are updated based on the field measured data, and the steps S2 to S4 are executed cyclically based on the updated boundary conditions until the current long-cycle operation of the geothermal heat pump system model is completed; The field measured data is used to update the corresponding data in the boundary conditions of step S2.

[0006] According to one aspect of the present invention, the geothermal heat pump system model includes: a geothermal side hydraulic model, a geothermal heat pump system model, a peak-shaving cooling system model, a peak-shaving heating system model and a composite geothermal heat pump system model; The initial boundary conditions include: operation mode, long-cycle control interval hours, current short-cycle control hours, long-cycle predicted load vector, short-cycle predicted load vector, shallow soil source cumulative heat extraction, operation scheduling plan for the last hour of the previous short cycle, and the optimal operation scheduling plan under the corresponding short cycle.

[0007] According to one aspect of the present invention, the step of selecting the boundary conditions for the long-term operation scheduling of the geothermal heat pump system model in the initial boundary conditions and calculating the current long-term operation scheduling scheme of the geothermal heat pump system model includes: (1) Construct and solve the long-term operation scheduling optimization objective function of the composite ground-source heat pump system, including: 1) Determine the long-term operation scheduling optimization solution target: The long-term operation scheduling optimization solution target is set as the underground pipe partition operation variable and the peak load system operation variable, which is recorded as: ; ; Where: Provide scheduling solutions for long-term operations; For the The control interval runs the scheduling scheme, ; For the The control interval ground source side operation scheduling plan, ; For the The peak load side operation scheduling plan of the control interval is ; For the Control interval operation mode, cooling season , the heating season is , ; For the Control interval The start and stop of the ground source side partition is turned on , closed to 0, ; For the Control interval Start and stop of each chiller, ; For the Control interval Start and stop of hot water boilers, ; is the number of long-cycle control intervals; ng is the number of ground-source heat pump units; The number of peak-shaving refrigeration units; The number of heating units for peak load regulation; The operating energy consumption of the ground source heat pump system and the peak load regulation system is expressed as a function that is only related to the ground source side operation scheduling scheme and the peak load regulation side operation scheduling scheme: ; ; ; ; Where: For the The average daily operating power of the ground source heat pump system in each control interval is For the The daily average operating power of the peak-shaving system with a control interval is ; For the Average cooling tower water consumption of each control interval peak load regulation system, ; For the Average gas operation volume of the peak-shaving system in each control interval, ; 2) Construct and solve the long-term operation scheduling optimization objective function: The goal of long-term scheduling optimization is to reduce the operating energy consumption of the composite ground source heat pump system as much as possible while ensuring the balance of cooling and heating on the ground source side of the ground source heat pump system. The optimization objective function is: ; Where: is the optimization function; For the Number of operating hours within a control interval; is the unit electricity price; is the unit water price; is the unit gas price; To consider the penalty factor of heat and cold imbalance on the ground source side; For the The average heat taken from the source side in each control interval is ; The cumulative heat taken by the shallow soil source from the beginning of this year's operating season to the optimization solution calculation of this long-term operation scheduling; The particle swarm algorithm is used to solve the long-term operation scheduling optimization objective function: the optimization period is from the beginning of the cooling season of the current year to the beginning of the cooling season of the next year or from the beginning of the heating season of the current year to the end of the heating season of the next year, including the cooling season, transition season, heating season, and transition season, a total of 365 / 366 days; the decision variable is the partition operation variable on the ground source side , Peak load system operation variables ; Solve to obtain the long-term optimal operation scheduling solution ; (2) Solving the long-term operation scheduling plan: The long-term optimal operation scheduling solution calculated by the long-term operation scheduling optimization objective function Decomposed into variables representing underground pipe partition operation and peak load system operation: ; The start and stop information of key equipment of the composite ground source heat pump system is obtained by the interlocking control of the underground pipe partition on the ground source side, the circulating water pump on the ground source side and the ground source heat pump unit, the interlocking control of the peak-shaving cooling tower, the cooling tower water pump and the peak-shaving chiller unit: For the Control interval The start and stop status of the underground pipes on the ground source side. ; For the Control interval The start and stop conditions of the peak-shaving chillers. ; For the Control interval The start and stop conditions of each hot water boiler, ; For the The number of ground source heat pump units in operation per control interval; For the Number of circulating water pumps in operation on the source side of each control interval; For the The number of operating units of the control interval peak-shaving chillers; For the Number of cooling towers in operation per control interval; For the The number of running cooling tower circulating water pumps per control interval; For the The number of operating hot water boilers for peak heating with control intervals; For the The number of control hours for each control interval.

[0008] According to one aspect of the present invention, the method of updating the boundary conditions of the short-cycle control strategy optimization method of the ground source heat pump system model based on the current long-cycle operation scheduling scheme, and calculating and issuing the short-cycle control strategy scheme under the current long-cycle operation scheduling scheme of the ground source heat pump system model, includes: (1) Short-cycle load distribution: The number of local heat pump units and peak load units that can be operated in the short period is determined by the long-term optimal operation scheduling plan: ; Where: The number of local source heat pump units that can be operated in the current short period; The number of peak-shaving chillers or peak-shaving boilers that can be operated in the current short period; It is the operating variable of the peak-shaving cooling unit in the current short cycle; It is the operating variable of the peak load boiler in the current short period; The load vector is predicted by the short-term Determine the operating load of the ground source heat pump system and the peak load regulation system: ; Where: is the operating load of the ground source heat pump system in the hth hour, ; is the peak load of the system in the hth hour, ; Forecast load vectors for short periods; (2) The optimization method of the short-cycle control strategy of the composite ground-source heat pump unit is calculated, including: 1) Determine the relationship between the load rate ground source heat pump unit and the peak load regulation system: The load rate of any unit or hot water boiler is calculated as follows: ; Where: To distribute the load in reality; is the rated load; For ground source heat pump units, the load rate satisfies the relationship: ; Where: Operating load for the ground source heat pump system; For the The operating load rate of the ground source heat pump unit is ; For the Rated operating load of ground source heat pump unit: ; For peak-shaving chillers or peak-shaving boilers, the load factor satisfies the relationship: ; Where: To peak the chiller or boiler operating load; For the The operating load rate of the peak-shaving chiller or peak-shaving boiler, ; For the The rated operating load of a peak-shaving chiller or peak-shaving boiler, ; 2) Optimization of load rate of ground source heat pump units: The objective function for optimizing the load rate of ground source heat pump units in the cooling season is constructed as follows: ; in: ; Where: For the The ground source heat pump unit can be operated, Hourly unit load factor; For the The ground source heat pump unit can be operated, Hourly unit factor; For the The ground source heat pump unit can be operated, Hourly unit factor; For the The minimum unit load rate of each operable ground source heat pump unit; For the The maximum unit load rate of the ground source heat pump units that can be operated; For the The ground source heat pump unit can be operated, Hourly unit factor; is the chilled water outlet temperature of the ground source heat pump unit; The chilled water supply temperature of the ground source heat pump unit; Analogous to the load rate optimization objective function of the ground source heat pump unit in the cooling season, the load rate optimization objective function of the ground source heat pump unit in the heating season is as follows: ; in: ; Where: For the The ground source heat pump unit can be operated, Hourly unit factor; For the The ground source heat pump unit can be operated, Hourly unit factor; For the The ground source heat pump unit can be operated, Hourly unit factor; The hot water outlet temperature of the ground source heat pump unit; is the hot water inlet temperature of the ground source heat pump unit; The load rate of ground source heat pump units in the cooling and heating seasons is solved by the interior point penalty function method, and the operating load rate of ground source heat pump units in each region is determined ; According to the unit operating load rate The calculation formula for the outlet water temperature of each local heat pump unit in the cooling season and heating season is as follows: ; Where: For the cooling season, Hour, Chilled water outlet temperature of ground source heat pump unit; For the heating season, Hour, Hot water outlet temperature of ground source heat pump unit; 3) Peak load rate optimization of the peak load regulation system: In the peak-shaving cooling system, the fan operating frequency of the cooling tower is controlled by open-loop approximate optimal control, and the cooling water circulation pump is controlled by variable frequency control to ensure the minimum pressure requirement of the cooling tower water mist nozzle. The load rate optimization objective function of the peak-shaving chiller is constructed as follows: ; in: ; Where: For the The peak-shaving chiller can be operated Hourly unit load factor; For the The peak-shaving chiller can be operated Hourly unit factor; For the The peak-shaving chiller can be operated Hourly unit factor; For the The peak-shaving chiller can be operated Hourly unit factor; The outlet temperature of chilled water for peak-shaving chillers; To adjust the chilled water supply temperature of the peak-shaving chiller; By establishing a mathematical model of the hot water boiler, the load rate optimization objective function of the peak-shaving chiller is constructed as follows: ; in: ; Where: For the The hot water boiler can be operated Hourly load factor; For the The hot water boiler can be operated The coefficient of hours; For the The hot water boiler can be operated The coefficient of hours; For the The hot water boiler can be operated The coefficient of hours; is the outlet water temperature of the hot water boiler; is the return water temperature of the hot water boiler; The load rate of the peak-shaving chiller and hot water boiler is solved by the interior point penalty function method to determine the operating load rate of the unit and boiler ; According to the unit operating load rate , the calculation formula for the outlet water temperature of each unit and boiler in the cooling season and heating season is as follows: ; Where: For the cooling season, Hour, The outlet water temperature of the peak-shaving chiller; For the heating season, Hour, Water outlet temperature of hot water boiler; (3) Obtain short-term control strategies, including: Cooling season daily control strategy sequence: For the Hour Load rate of ground source heat pump units, ; For the Hourly composite ground source heat pump system user side chilled water outlet temperature, ; For the cooling season, Hour Chilled water outlet temperature of ground source heat pump unit, ; For the Hourly interval chilled water circulation flow in cooling season, ; For the Hourly interval of the chilled water return temperature of the composite ground source heat pump system, ; For the Number of circulating water pumps running per hour on the user side, ; For the Hourly user-side circulating water pump group operating frequency, ; For the cooling season, Hourly peak load system chiller chilled water outlet temperature, ; For the cooling season, Hourly peak load regulation system chiller load rate, ; Heating season daily control strategy sequence: For the Hour Load rate of ground source heat pump units, ; For the Hourly hot water outlet temperature of the user side of the composite ground source heat pump system, ; For the heating season, Hour The hot water outlet temperature of the ground source heat pump unit is ; For the Hourly hot water circulation flow in heating season, ; For the Hourly composite ground source heat pump system chilled water return temperature, ; For the Number of circulating water pumps running per hour on the user side, ; For the Hourly user-side circulating water pump group operating frequency, ; For the heating season, The hot water outlet temperature of the hot water boiler in the hourly peak load regulation system, ; For the Hourly peak load regulation system hot water boiler load rate, .

[0009] According to one aspect of the present invention, after a short-cycle control of the geothermal heat pump system model ends, field measured data is obtained, boundary conditions for long-cycle operation scheduling and short-cycle control are updated based on the field measured data, and steps S2 to S4 are cyclically executed on the basis of the updated boundary conditions until the current long-cycle operation of the geothermal heat pump system model ends: After the short-cycle control is completed, the measured ground source heat pump system operation data is uploaded to extract the operation scheduling plan for the last hour of the previous short cycle. And calculate the cumulative heat taken by the shallow soil source , delete the long-term load forecast long-term forecast load vector The data is updated, the short-cycle forecast load vector is updated, and the control hours of the current short cycle are updated; the long-cycle operation scheduling plan and the short-cycle control strategy plan are rolled out to obtain the equipment start-stop operation scheduling plan and specific operation control strategy for subsequent short cycles in the current long-cycle operation scheduling plan until the current long-cycle operation of the ground source heat pump system model ends.

[0010] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a ground source heat pump system operation simulation prediction and measured data feedback fusion control system, comprising: Model building module, builds the ground source heat pump system model, inputs model parameters and initial boundary conditions, and performs long-term operation scheduling of the ground source heat pump system and operation simulation of multiple short-term controls under long-term based on the ground source heat pump system model; The long-term operation scheduling scheme calculation module selects the boundary conditions for the long-term operation scheduling of the ground source heat pump system model in the initial boundary conditions, and calculates the current long-term operation scheduling scheme of the ground source heat pump system model; The short-cycle control strategy calculation module selects the boundary conditions for the short-cycle control of the ground source heat pump system model in the initial boundary conditions based on the current long-cycle operation scheduling plan, calculates and issues the short-cycle control strategy under the current long-cycle operation scheduling plan of the ground source heat pump system model; The rolling operation module obtains the field measured data after a short-cycle control of the ground source heat pump system model ends, updates the boundary conditions of the long-cycle operation scheduling and the boundary conditions of the short-cycle control based on the field measured data, and cyclically executes the long-cycle operation scheduling scheme calculation module-rolling operation module on the basis of the updated boundary conditions until the current long-cycle operation of the ground source heat pump system model ends; Among them, the field measured data is used to update the corresponding data in the boundary conditions of the long-term operation scheduling scheme calculation module.

[0011] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the ground source heat pump system operation simulation prediction and measured data feedback fusion control method as described above is implemented.

[0012] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the ground source heat pump system operation simulation prediction and measured data feedback fusion control method as described above is implemented.

[0013] According to the scheme of the present invention, the present invention realizes the high-efficiency operation of the ground source heat pump system and its peak-shaving equipment through the combination of long-cycle and short-cycle optimization scheduling. The long-cycle optimization method can accurately predict the load demand and reasonably arrange the operation and rest time of the equipment, while the short-cycle optimization method dynamically adjusts the load rate of the unit according to the real-time load to ensure that the equipment is always in the best working state, thereby significantly improving the energy efficiency of the system.

[0014] The present invention achieves efficient use of energy by optimizing the consumption of various resources such as electricity, water, and gas. The optimization objective function not only takes into account the energy efficiency of system operation, but also the operating cost, including factors such as unit electricity price, unit water price, and gas price, thereby minimizing the overall operating cost while ensuring efficient operation of the system.

[0015] The present invention effectively ensures the balance of cooling and heating loads of the ground source heat pump system through accurate load prediction and scheduling strategies. Whether in the cooling season or the heating season, the system can dynamically adjust the operating status of the equipment to avoid the imbalance of cooling and heating, thereby improving the stability and reliability of the system.

[0016] The present invention introduces an intelligent dispatching system that can collect field data in real time and combine it with the optimization algorithm to automatically adjust the start and stop and operation mode of the equipment. Through adaptive control strategies, the system can quickly respond and optimize operation under complex conditions such as various load changes and seasonal changes. Intelligent dispatching control not only reduces manual intervention, but also improves the flexibility and response speed of the system.

[0017] The present invention successfully achieves the seamless connection between long-term scheduling and short-term control strategies through the rolling time domain optimization method. The matching of long-term predicted load and short-term actual load ensures the stability of the system in long-term operation, while making the load distribution in the short term more refined and accurate, thereby improving the continuity and efficiency of system operation.

[0018] The present invention takes full account of the load distribution and resource scheduling of equipment on the basis of optimized scheduling. Through intelligent algorithms and interlocking control, the system can ensure that each key equipment starts and stops at the appropriate time, avoiding the waste of excessive operation or idle operation, thereby further optimizing resource allocation and reducing unnecessary energy waste.

[0019] The optimization control method of the present invention not only improves the efficiency of the ground source heat pump system, but also improves the overall efficiency of building energy management. The combination of intelligent scheduling and optimization algorithm makes the internal energy use of the building more reasonable, thereby providing technical support for building energy conservation, environmental protection and sustainable development.

[0020] The present invention reduces energy consumption and greenhouse gas emissions by improving the operating efficiency of the ground source heat pump system, which helps promote the construction of green buildings. The technology can be widely used in various construction fields to promote the improvement of building energy efficiency and promote the goals of environmental protection and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart schematically showing a method for fusion control of ground source heat pump system operation simulation prediction and measured data feedback according to an embodiment of the present invention; Figure 2 The operating principle diagram of the ground source heat pump system operation simulation prediction and measured data feedback fusion control method according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0022] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only to enable those skilled in the art to better understand and thus implement the present invention, rather than implying any limitation on the scope of the present invention.

[0023] As used herein, the term “including” and variations thereof are to be interpreted as open-ended terms meaning “including, but not limited to.” The term “based on” is to be interpreted as “based, at least in part, on.” The terms “one embodiment” and “an embodiment” are to be interpreted as “at least one embodiment.”

[0024] Figure 1 The flowchart schematically shows a method for fusion control of ground source heat pump system operation simulation prediction and measured data feedback according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the ground source heat pump system operation simulation prediction and measured data feedback fusion control method includes: S1. Construct a geothermal heat pump system model, input model parameters and initial boundary conditions, and perform long-term operation scheduling of the geothermal heat pump system and operation simulation of multiple short-term controls under long-term operation based on the geothermal heat pump system model; S2. Selecting the boundary conditions for the long-term operation scheduling of the ground source heat pump system model in the initial boundary conditions, and calculating the current long-term operation scheduling scheme of the ground source heat pump system model; S3. Based on the current long-cycle operation scheduling scheme, select the boundary conditions for short-cycle control of the ground source heat pump system model in the initial boundary conditions, calculate and issue the short-cycle control strategy scheme under the current long-cycle operation scheduling scheme of the ground source heat pump system model; S4. After a short-cycle control of the geothermal heat pump system model is completed, the field measured data is obtained, and the boundary conditions of the long-cycle operation scheduling and the boundary conditions of the short-cycle control are updated based on the field measured data, and the steps S2 to S4 are executed cyclically based on the updated boundary conditions until the current long-cycle operation of the geothermal heat pump system model is completed; The field measured data is used to update the corresponding data in the boundary conditions of step S2.

[0025] Further, according to an embodiment of the present invention, the geothermal heat pump system model includes: a geothermal side hydraulic model, a geothermal heat pump system model, a peak-shaving cooling system model, a peak-shaving heating system model and a composite geothermal heat pump system model; The initial boundary conditions include: the operating mode vector , long-term control interval hours, current short-term control hours, long-term forecast load vector , short-term forecast load vector , Cumulative heat removal from shallow soil sources , the operation scheduling plan for the last hour of the previous short cycle , the optimal operation scheduling plan corresponding to the short cycle.

[0026] Further, according to an embodiment of the present invention, a boundary condition for long-term operation scheduling of the ground source heat pump system model is selected in the initial boundary condition, and a current long-term operation scheduling scheme of the ground source heat pump system model is calculated, including: (1) Select the following boundary conditions: Operation mode vector ; Outdoor wet bulb temperature vector ; Long-term forecast load vector ; Long cycle control interval hours ; Cumulative heat removal from shallow soil sources ; Operation scheduling plan for the last hour of the previous short cycle ; (2) Construct and solve the long-term operation scheduling optimization objective function of the composite ground-source heat pump system, including: 1) Determine the long-term operation scheduling optimization solution target: The long-term operation scheduling optimization solution target is set as the underground pipe partition operation variable and the peak load system operation variable, which is recorded as: ; ; Where: Provide scheduling solutions for long-term operations; For the The control interval runs the scheduling scheme, ; For the The control interval ground source side operation scheduling plan, ; For the The peak load side operation scheduling plan of the control interval is ; For the Control interval operation mode, cooling season , the heating season is , ; For the Control interval The start and stop of the ground source side partition is turned on , closed to 0, ; For the Control interval Start and stop of each chiller, ; For the Control interval Start and stop of hot water boilers, ; is the number of long-cycle control intervals; ng is the number of ground-source heat pump units; The number of peak-shaving refrigeration units; The number of heating units for peak load regulation; The operating energy consumption of the ground source heat pump system and the peak load regulation system is expressed as a function that is only related to the ground source side operation scheduling scheme and the peak load regulation side operation scheduling scheme: ; ; ; ; Where: For the The average daily operating power of the ground source heat pump system in each control interval is For the The daily average operating power of the peak-shaving system with a control interval is ; For the Average cooling tower water consumption of each control interval peak load regulation system, ; For the Average gas operation volume of the peak-shaving system in each control interval, ; 2) Construct and solve the long-term operation scheduling optimization objective function: The goal of long-term scheduling optimization is to reduce the operating energy consumption of the composite ground source heat pump system as much as possible while ensuring the balance of cooling and heating on the ground source side of the ground source heat pump system. The optimization objective function is: ; Where: is the optimization function; For the Number of operating hours within a control interval; is the unit electricity price; is the unit water price; is the unit gas price; To consider the penalty factor of heat and cold imbalance on the ground source side; For the The average heat taken from the source side in each control interval is ; The cumulative heat taken by the shallow soil source from the beginning of this year's operating season to the optimization solution calculation of this long-term operation scheduling; The particle swarm algorithm is used to solve the long-term operation scheduling optimization objective function: the optimization period is from the beginning of the cooling season of the current year to the beginning of the cooling season of the next year or from the beginning of the heating season of the current year to the end of the heating season of the next year, including the cooling season, transition season, heating season, and transition season, a total of 365 / 366 days; the decision variable is the partition operation variable on the ground source side , Peak load system operation variables ; Solve to obtain the long-term optimal operation scheduling solution ; (3) Solving the long-term operation scheduling plan: The long-term optimal operation scheduling solution calculated by the long-term operation scheduling optimization objective function Decomposed into variables representing underground pipe partition operation and peak load system operation: ; The start and stop information of key equipment of the composite ground source heat pump system is obtained by the interlocking control of the underground pipe partition on the ground source side, the circulating water pump on the ground source side and the ground source heat pump unit, the interlocking control of the peak-shaving cooling tower, the cooling tower water pump and the peak-shaving chiller unit: For the Control interval The start and stop status of the underground pipes on the ground source side. ; For the Control interval The start and stop conditions of the peak-shaving chillers. ; For the Control interval The start and stop conditions of each hot water boiler, ; For the The number of ground source heat pump units in operation per control interval; For the Number of circulating water pumps in operation on the source side of each control interval; For the The number of operating units of the control interval peak-shaving chillers; For the Number of cooling towers in operation per control interval; For the The number of running cooling tower circulating water pumps per control interval; For the The number of operating hot water boilers for peak heating with control intervals; For the The number of control hours for each control interval.

[0027] In this embodiment, the long-term operation scheduling scheme calculated by the long-term optimization scheduling algorithm is extracted. The first vector in is , as the optimal operation scheduling scheme for a short period.

[0028] Further, according to an embodiment of the present invention, based on the current long-cycle operation scheduling scheme, the boundary conditions for the short-cycle control of the ground source heat pump system model in the initial boundary conditions are selected, and the short-cycle control strategy scheme under the current long-cycle operation scheduling scheme of the ground source heat pump system model is calculated and issued, including: (1) Select the following boundary conditions: Operation mode vector ; Optimal operation scheduling scheme corresponding to short cycle ; Current short cycle control hours ; Outdoor wet bulb temperature vector ; Short-term forecast load vector ; (2) Short-cycle load distribution: The number of local heat pump units and peak load units that can be operated in the short period is determined by the long-term optimal operation scheduling plan: ; Where: The number of local source heat pump units that can be operated in the current short period; The number of peak-shaving chillers or peak-shaving boilers that can be operated in the current short period; It is the operating variable of the peak-shaving cooling unit in the current short cycle; It is the operating variable of the peak load boiler in the current short period; The load vector is predicted by the short-term Determine the operating load of the ground source heat pump system and the peak load regulation system: ; Where: is the operating load of the ground source heat pump system in the hth hour, ; is the peak load of the system in the hth hour, ; Forecast load vectors for short periods; (3) The short-cycle control strategy optimization method of the composite ground source heat pump unit is calculated, including: 1) Determine the relationship between the load rate ground source heat pump unit and the peak load regulation system: The load rate of any unit or hot water boiler is calculated as follows: ; Where: To distribute the load in reality; is the rated load; For ground source heat pump units, the load rate satisfies the relationship: ; Where: Operating load for the ground source heat pump system; For the The operating load rate of the ground source heat pump unit is ; For the Rated operating load of ground source heat pump unit: ; For peak-shaving chillers or peak-shaving boilers, the load factor satisfies the relationship: ; Where: To peak the chiller or boiler operating load; For the The operating load rate of the peak-shaving chiller or peak-shaving boiler, ; For the The rated operating load of a peak-shaving chiller or peak-shaving boiler, ; 2) Optimization of load rate of ground source heat pump units: The objective function for optimizing the load rate of ground source heat pump units in the cooling season is constructed as follows: ; in: ; Where: For the The ground source heat pump unit can be operated, Hourly unit load factor; For the The ground source heat pump unit can be operated, Hourly unit factor; For the The ground source heat pump unit can be operated, Hourly unit factor; For the The minimum unit load rate of each operable ground source heat pump unit; For the The maximum unit load rate of the ground source heat pump units that can be operated; For the The ground source heat pump unit can be operated, Hourly unit factor; The outlet temperature of chilled water of the ground source heat pump unit; The chilled water supply temperature of the ground source heat pump unit; Analogous to the load rate optimization objective function of the ground source heat pump unit in the cooling season, the load rate optimization objective function of the ground source heat pump unit in the heating season is as follows: ; in: ; Where: For the The ground source heat pump unit can be operated, Hourly unit factor; For the The ground source heat pump unit can be operated, Hourly unit factor; For the The ground source heat pump unit can be operated, Hourly unit factor; The hot water outlet temperature of the ground source heat pump unit; is the hot water inlet temperature of the ground source heat pump unit; The load rate of ground source heat pump units in the cooling and heating seasons is solved by the interior point penalty function method, and the operating load rate of ground source heat pump units in each region is determined ; According to the unit operating load rate The calculation formula for the outlet water temperature of each local heat pump unit in the cooling season and heating season is as follows: ; Where: For the cooling season, Hour, Chilled water outlet temperature of ground source heat pump unit; For the heating season, Hour, Hot water outlet temperature of ground source heat pump unit; 3) Peak load rate optimization of the peak load regulation system: In the peak-shaving cooling system, the fan operating frequency of the cooling tower is controlled by open-loop approximate optimal control, and the cooling water circulation pump is controlled by variable frequency control to ensure the minimum pressure requirement of the cooling tower water mist nozzle. The load rate optimization objective function of the peak-shaving chiller is constructed as follows: ; in: ; Where: For the The peak-shaving chiller can be operated Hourly unit load factor; For the The peak-shaving chiller can be operated Hourly unit factor; For the The peak-shaving chiller can be operated Hourly unit factor; For the The peak-shaving chiller can be operated Hourly unit factor; The outlet temperature of chilled water for peak-shaving chillers; To adjust the chilled water supply temperature of the peak-shaving chiller; By establishing a mathematical model of the hot water boiler, the load rate optimization objective function of the peak-shaving chiller is constructed as follows: ; in: ; Where: For the The hot water boiler can be operated Hourly load factor; For the The hot water boiler can be operated The coefficient of hours; For the The hot water boiler can be operated The coefficient of hours; For the The hot water boiler can be operated The coefficient of hours; is the outlet water temperature of the hot water boiler; is the return water temperature of the hot water boiler; The load rate of the peak-shaving chiller and hot water boiler is solved by the interior point penalty function method to determine the operating load rate of the unit and boiler ; According to the unit operating load rate , the calculation formula for the outlet water temperature of each unit and boiler in the cooling season and heating season is as follows: ; Where: For the cooling season, Hour, The outlet water temperature of the peak-shaving chiller; For the heating season, Hour, Water outlet temperature of hot water boiler; In this embodiment, the load rate calculation formula of any unit or hot water boiler is: ; Where: is the actual distributed load, kW; is the rated load, kW; For ground source heat pump units, the load rate satisfies the relationship: ; Where: is the operating load of the ground source heat pump system, kW; For the The operating load rate of the ground source heat pump unit is ; For the Rated operating load of ground source heat pump unit: , kW; For peak-shaving chillers or peak-shaving boilers, the load factor satisfies the relationship: ; Where: The operating load of the peak-shaving chiller or peak-shaving boiler, kW; For the The operating load rate of the peak-shaving chiller or peak-shaving boiler, ; For the The rated operating load of a peak-shaving chiller or peak-shaving boiler, , kW; (4) Obtain short-term control strategies, including: Cooling season daily control strategy sequence: For the Hour Load rate of ground source heat pump units, ; For the Hourly composite ground source heat pump system user side chilled water outlet temperature, ; For the cooling season, Hour Chilled water outlet temperature of ground source heat pump unit, ; For the Hourly interval chilled water circulation flow in cooling season, ; For the Hourly interval of the chilled water return temperature of the composite ground source heat pump system, ; For the Number of circulating water pumps running per hour on the user side, ; For the Hourly user-side circulating water pump group operating frequency, ; For the cooling season, Hourly peak load system chiller chilled water outlet temperature, ; For the cooling season, Hourly peak load regulation system chiller load rate, ; Heating season daily control strategy sequence: For the Hour Load rate of ground source heat pump units, ; For the Hourly hot water outlet temperature of the user side of the composite ground source heat pump system, ; For the heating season, Hour The hot water outlet temperature of the ground source heat pump unit is ; For the Hourly hot water circulation flow in heating season, ; For the Hourly composite ground source heat pump system chilled water return temperature, ; For the Number of circulating water pumps running per hour on the user side, ; For the Hourly user-side circulating water pump group operating frequency, ; For the heating season, The hot water outlet temperature of the hot water boiler in the hourly peak load regulation system, ; For the Hourly peak load regulation system hot water boiler load rate, .

[0029] Further, in this embodiment, if Figure 2As shown, the present invention is based on the above-mentioned current long-cycle operation scheduling scheme and short-cycle control strategy scheme, combined with field measured data, to construct a long-short cycle coupled rolling time domain operation optimization method for a ground source heat pump system, and the operation steps are as follows: In the current long-term operation scheduling scheme, the long-term forecast load vector As the main input control boundary condition of this method, the operation scheduling scheme for each control interval in the current long period is calculated by the above method. , take the operation scheduling plan of the first control interval as the start-stop operation scheduling plan for each key equipment in the next short control cycle.

[0030] In the short-cycle control strategy scheme, the short-cycle operation scheduling scheme and the short-term forecast load vector As the main input control boundary conditions, the specific operation control strategy of each unit in the current short cycle is calculated by the above method.

[0031] Through the automatic control system, the short-term key equipment start-stop operation scheduling plan and specific operation control strategy are sent to each device on the actual site to control the operation of each device of the local source heat pump system in the current short period.

[0032] Further, according to an embodiment of the present invention, after a short-cycle control of the geothermal heat pump system model ends, field measured data is obtained, and the boundary conditions of the long-cycle operation scheduling and the boundary conditions of the short-cycle control are updated based on the field measured data, and steps S2 to S4 are cyclically executed on the basis of the updated boundary conditions until the current long-cycle operation of the geothermal heat pump system model ends as follows: After the short-cycle control is completed, the measured ground source heat pump system operation data is uploaded to extract the operation scheduling plan for the last hour of the previous short cycle. And calculate the cumulative heat taken by the shallow soil source , delete the long-term load forecast long-term forecast load vector Before Row data, call the relevant load forecasting algorithm to update the short-term forecast load vector , update the control hours of the current short cycle , rolling operation of the long-cycle operation scheduling plan and the short-cycle control strategy plan, to obtain the equipment start-stop operation scheduling plan and specific operation control strategy for subsequent short cycles in the current long-cycle operation scheduling plan, until the current long-cycle operation of the ground source heat pump system model ends.

[0033] According to the above scheme of the present invention, the present invention can improve the operating energy efficiency of the composite ground source heat pump system: by adopting an optimization scheduling method combining long cycles and short cycles, accurately predicting the system load and dynamically adjusting the operating state of the equipment, the coordinated and efficient operation of the ground source heat pump system and the peak load regulation system can be achieved. Under the premise of ensuring the cold and heat balance on the ground source side of the system, energy consumption can be reduced as much as possible and the energy configuration of the system can be optimized.

[0034] The present invention can optimize the system's load prediction and scheduling strategy: by using an improved genetic algorithm to optimize the long-cycle and short-cycle operation scheduling objective functions, it can solve the scheduling problem of the ground source heat pump system under variable load conditions, ensure the accurate distribution of loads in each cycle, and improve the system's load prediction accuracy and scheduling response speed.

[0035] The present invention can achieve seamless connection between long cycles and short cycles: the present invention establishes an effective feedback mechanism between long cycles and short cycles through a rolling time domain optimization method to ensure the continuity and consistency of the scheduling schemes of the two. By using real-time data feedback, the long-cycle boundary conditions and short-cycle control strategies are dynamically adjusted, thereby improving the adaptability and intelligence level of the system at different time scales.

[0036] The present invention can improve the intelligence and automation level of the system: the intelligent dispatching system designed by the present invention can monitor the operating status of the ground source heat pump system and peak load regulation equipment in real time, and automatically adjust the start and stop and operating mode of the equipment through adaptive optimization algorithms. This intelligent dispatching method greatly improves the operating efficiency and stability of the system and reduces manual intervention and management costs.

[0037] The present invention can ensure the stability and safety of the ground source heat pump system: through multi-level interlocking control and optimized scheduling strategy, the present invention ensures the coordinated operation of key equipment of the composite ground source heat pump system in each operation cycle, thereby ensuring the stability and safety of the system under load fluctuations, seasonal changes and complex environments.

[0038] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a ground source heat pump system operation simulation prediction and measured data feedback fusion control system, comprising: Model building module, builds the ground source heat pump system model, inputs model parameters and initial boundary conditions, and performs long-term operation scheduling of the ground source heat pump system and operation simulation of multiple short-term controls under long-term based on the ground source heat pump system model; The long-term operation scheduling scheme calculation module selects the boundary conditions for the long-term operation scheduling of the ground source heat pump system model in the initial boundary conditions, and calculates the current long-term operation scheduling scheme of the ground source heat pump system model; The short-cycle control strategy calculation module selects the boundary conditions for the short-cycle control of the ground source heat pump system model in the initial boundary conditions based on the current long-cycle operation scheduling plan, calculates and issues the short-cycle control strategy under the current long-cycle operation scheduling plan of the ground source heat pump system model; The rolling operation module obtains the field measured data after a short-cycle control of the ground source heat pump system model ends, updates the boundary conditions of the long-cycle operation scheduling and the boundary conditions of the short-cycle control based on the field measured data, and cyclically executes the long-cycle operation scheduling scheme calculation module-rolling operation module on the basis of the updated boundary conditions until the current long-cycle operation of the ground source heat pump system model ends; Among them, the field measured data is used to update the corresponding data in the boundary conditions of the long-term operation scheduling scheme calculation module.

[0039] The ground source heat pump system operation simulation prediction and measured data feedback fusion control system according to the present invention can realize the above-mentioned ground source heat pump system operation simulation prediction and measured data feedback fusion control method. The specific method steps are as described above and will not be repeated here.

[0040] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the ground source heat pump system operation simulation prediction and measured data feedback fusion control method as described above is implemented.

[0041] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the ground source heat pump system operation simulation prediction and measured data feedback fusion control method as described above is implemented.

[0042] Those skilled in the art will appreciate that the modules and algorithm steps 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 to be beyond the scope of the present invention.

[0043] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods, and will not be repeated here.

[0044] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0045] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0046] In addition, each functional module in the embodiment of the present invention 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.

[0047] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0048] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

[0049] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A ground source heat pump system operation simulation prediction and measured data feedback fusion control method, characterized in that: include: S1. Construct a geothermal heat pump system model, input model parameters and initial boundary conditions, and perform long-term operation scheduling of the geothermal heat pump system and operation simulation of multiple short-term controls under long-term operation based on the geothermal heat pump system model; S2. Selecting the boundary conditions for the long-term operation scheduling of the ground source heat pump system model in the initial boundary conditions, and calculating the current long-term operation scheduling scheme of the ground source heat pump system model; S3. Based on the current long-cycle operation scheduling scheme, select the boundary conditions for short-cycle control of the ground source heat pump system model in the initial boundary conditions, calculate and issue the short-cycle control strategy scheme under the current long-cycle operation scheduling scheme of the ground source heat pump system model; S4. After a short-cycle control of the geothermal heat pump system model is completed, the field measured data is obtained, and the boundary conditions of the long-cycle operation scheduling and the boundary conditions of the short-cycle control are updated based on the field measured data, and the steps S2 to S4 are executed cyclically based on the updated boundary conditions until the current long-cycle operation of the geothermal heat pump system model is completed; The field measured data is used to update the corresponding data in the boundary conditions of step S2.

2. The method for fusion control of ground source heat pump system operation simulation prediction and measured data feedback according to claim 1 is characterized in that: The ground source heat pump system model includes: a ground source side hydraulic model, a ground source heat pump system model, a peak-shaving cooling system model, a peak-shaving heating system model and a composite ground source heat pump system model; The initial boundary conditions include: operation mode, long-cycle control interval hours, current short-cycle control hours, long-cycle predicted load vector, short-cycle predicted load vector, shallow soil source cumulative heat extraction, operation scheduling plan for the last hour of the previous short cycle, and the optimal operation scheduling plan under the corresponding short cycle.

3. The ground source heat pump system operation simulation prediction and measured data feedback fusion control method according to claim 2 is characterized in that: The step of selecting the boundary conditions for the long-term operation scheduling of the ground source heat pump system model in the initial boundary conditions and calculating the current long-term operation scheduling scheme of the ground source heat pump system model includes: (1) Construct and solve the long-term operation scheduling optimization objective function of the composite ground-source heat pump system, including: 1) Determine the long-term operation scheduling optimization solution target: The long-term operation scheduling optimization solution target is set as the underground pipe partition operation variable and the peak load system operation variable, which is recorded as: ； ； Where: Provide scheduling solutions for long-term operations; For the The control interval runs the scheduling scheme, ; For the The control interval ground source side operation scheduling plan, ; For the The peak load side operation scheduling plan of the control interval is ; For the Control interval operation mode, cooling season , the heating season is , ; For the Control interval The start and stop of the ground source side partition is turned on , closed to 0, ; For the Control interval Start and stop of each chiller, ; For the Control interval Start and stop of hot water boilers, ; is the number of long-cycle control intervals; ng is the number of ground-source heat pump units; The number of peak-shaving refrigeration units; The number of heating units for peak load regulation; The operating energy consumption of the ground source heat pump system and the peak load regulation system is expressed as a function that is only related to the ground source side operation scheduling scheme and the peak load regulation side operation scheduling scheme: ； ； ； ； Where: For the The average daily operating power of the ground source heat pump system in each control interval is For the The daily average operating power of the peak-shaving system with a control interval is ; For the Average cooling tower water consumption of each control interval peak load regulation system, ; For the Average gas operation volume of the peak-shaving system in each control interval, ; 2) Construct and solve the long-term operation scheduling optimization objective function: The goal of long-term scheduling optimization is to reduce the operating energy consumption of the composite ground source heat pump system as much as possible while ensuring the balance of cooling and heating on the ground source side of the ground source heat pump system. The optimization objective function is: ； Where: is the optimization function; For the Number of operating hours within a control interval; is the unit electricity price; is the unit water price; is the unit gas price; To consider the penalty factor of heat and cold imbalance on the ground source side; For the The average heat taken from the source side in each control interval, ; The cumulative heat taken by the shallow soil source from the beginning of this year's operating season to the optimization solution calculation of this long-term operation scheduling; The particle swarm algorithm is used to solve the long-term operation scheduling optimization objective function: the optimization period is from the beginning of the cooling season of the current year to the beginning of the cooling season of the next year or from the beginning of the heating season of the current year to the end of the heating season of the next year, including the cooling season, transition season, heating season, and transition season, a total of 365 / 366 days; the decision variable is the partition operation variable on the ground source side , Peak load system operation variables ; Solve to obtain the long-term optimal operation scheduling solution ; (2) Solving the long-term operation scheduling plan: The long-term optimal operation scheduling solution calculated by the long-term operation scheduling optimization objective function Decomposed into variables representing underground pipe partition operation and peak load system operation: ； The start and stop information of key equipment of the composite ground source heat pump system is obtained by the interlocking control of the underground pipe partition on the ground source side, the circulating water pump on the ground source side and the ground source heat pump unit, the interlocking control of the peak-shaving cooling tower, the cooling tower water pump and the peak-shaving chiller unit: For the Control interval The start and stop status of the underground pipes on the ground source side. ; For the Control interval The start and stop conditions of the peak-shaving chillers. ; For the Control interval The start and stop conditions of each hot water boiler, ; For the The number of ground source heat pump units in operation per control interval; For the Number of circulating water pumps in operation on the source side of each control interval; For the The number of operating units of the control interval peak-shaving chillers; For the Number of cooling towers in operation per control interval; For the The number of running cooling tower circulating water pumps per control interval; For the The number of operating hot water boilers for peak heating with control intervals; For the The number of control hours for each control interval.

4. The method for fusion control of ground source heat pump system operation simulation prediction and measured data feedback according to claim 3 is characterized in that: The method comprises: selecting a boundary condition for short-cycle control of the ground source heat pump system model in the initial boundary condition based on the current long-cycle operation scheduling scheme, and calculating and issuing a short-cycle control strategy scheme under the current long-cycle operation scheduling scheme of the ground source heat pump system model. (1) Short-cycle load distribution: The number of local heat pump units and peak load units that can be operated in the short period is determined by the long-term optimal operation scheduling plan: ； Where: The number of local source heat pump units that can be operated in the current short period; The number of peak-shaving chillers or peak-shaving boilers that can be operated in the current short period; It is the operating variable of the peak-shaving cooling unit in the current short cycle; It is the operating variable of the peak load boiler in the current short period; The load vector is predicted by the short-term Determine the operating load of the ground source heat pump system and the peak load regulation system: ； Where: is the operating load of the ground source heat pump system in the hth hour, ; is the peak load of the system in the hth hour, ; Forecast load vectors for short periods; (2) The optimization method of the short-cycle control strategy of the composite ground-source heat pump unit is calculated, including: 1) Determine the relationship between the load rate ground source heat pump unit and the peak load regulation system: The load rate of any unit or hot water boiler is calculated as follows: ； Where: To distribute the load in reality; is the rated load; For ground source heat pump units, the load rate satisfies the relationship: ； Where: Operating load for the ground source heat pump system; For the The operating load rate of the ground source heat pump unit is ; For the Rated operating load of ground source heat pump unit: ; For peak-shaving chillers or peak-shaving boilers, the load factor satisfies the relationship: ； Where: To peak the chiller or boiler operating load; For the The operating load rate of the peak-shaving chiller or peak-shaving boiler, ; For the The rated operating load of a peak-shaving chiller or peak-shaving boiler, ; 2) Optimization of load rate of ground source heat pump units: The objective function for optimizing the load rate of ground source heat pump units in the cooling season is constructed as follows: ； in: ； Where: For the The ground source heat pump unit can be operated, Hourly unit load factor; For the The ground source heat pump unit can be operated, Hourly unit factor; For the The ground source heat pump unit can be operated, Hourly unit factor; For the The minimum unit load rate of each operable ground source heat pump unit; For the The maximum unit load rate of the ground source heat pump units that can be operated; For the The ground source heat pump unit can be operated, Hourly unit factor; is the chilled water outlet temperature of the ground source heat pump unit; The chilled water supply temperature of the ground source heat pump unit; Analogous to the load rate optimization objective function of the ground source heat pump unit in the cooling season, the load rate optimization objective function of the ground source heat pump unit in the heating season is as follows: ； in: ； Where: For the The ground source heat pump unit can be operated, Hourly unit factor; For the The ground source heat pump unit can be operated, Hourly unit factor; For the The ground source heat pump unit can be operated, Hourly unit factor; The hot water outlet temperature of the ground source heat pump unit; is the hot water inlet temperature of the ground source heat pump unit; The load rate of ground source heat pump units in the cooling and heating seasons is solved by the interior point penalty function method, and the operating load rate of ground source heat pump units in each region is determined ; According to the unit operating load rate The calculation formula for the outlet water temperature of each local heat pump unit in the cooling season and heating season is as follows: ； Where: For the cooling season, Hour, Chilled water outlet temperature of ground source heat pump unit; For the heating season, Hour, Hot water outlet temperature of ground source heat pump unit; 3) Peak load rate optimization of the peak load regulation system: In the peak-shaving cooling system, the fan operating frequency of the cooling tower is controlled by open-loop approximate optimal control, and the cooling water circulation pump is controlled by variable frequency control to ensure the minimum pressure requirement of the cooling tower water mist nozzle. The load rate optimization objective function of the peak-shaving chiller is constructed as follows: ； in: ； Where: For the The peak-shaving chiller can be operated Hourly unit load factor; For the The peak-shaving chiller can be operated Hourly unit factor; For the The peak-shaving chiller can be operated Hourly unit factor; For the The peak-shaving chiller can be operated Hourly unit factor; The outlet temperature of chilled water for peak-shaving chillers; To adjust the chilled water supply temperature of the peak-shaving chiller; By establishing a mathematical model of the hot water boiler, the load rate optimization objective function of the peak-shaving chiller is constructed as follows: ； in: ； Where: For the The hot water boiler can be operated Hourly load factor; For the The hot water boiler can be operated The coefficient of hours; For the The hot water boiler can be operated The coefficient of hours; For the The hot water boiler can be operated The coefficient of hours; is the outlet water temperature of the hot water boiler; is the return water temperature of the hot water boiler; The load rate of the peak-shaving chiller and hot water boiler is solved by the interior point penalty function method to determine the operating load rate of the unit and boiler ; According to the unit operating load rate , the calculation formula for the outlet water temperature of each unit and boiler in the cooling season and heating season is as follows: ； Where: For the cooling season, Hour, The outlet water temperature of the peak-shaving chiller; For the heating season, Hour, Water outlet temperature of hot water boiler; (3) Obtain short-cycle control strategies, including: Cooling season daily control strategy sequence: For the Hour Load rate of ground source heat pump units, ; For the Hourly composite ground source heat pump system user side chilled water outlet temperature, ; For the cooling season, Hour Chilled water outlet temperature of ground source heat pump unit, ; For the Hourly interval chilled water circulation flow in cooling season, ; For the Hourly interval of the chilled water return temperature of the composite ground source heat pump system, ; For the Number of circulating water pumps running per hour on the user side, ; For the Hourly user-side circulating water pump group operating frequency, ; For the cooling season, Hourly peak load system chiller chilled water outlet temperature, ; For the cooling season, Hourly peak load regulation system chiller load rate, ; Heating season daily control strategy sequence: For the Hour Load rate of ground source heat pump units, ; For the Hourly hot water outlet temperature of the user side of the composite ground source heat pump system, ; For the heating season, Hour The hot water outlet temperature of the ground source heat pump unit is ; For the Hourly hot water circulation flow in heating season, ; For the Hourly composite ground source heat pump system chilled water return temperature, ; For the Number of circulating water pumps running per hour on the user side, ; For the Hourly user-side circulating water pump group operating frequency, ; For the heating season, The hot water outlet temperature of the hot water boiler in the hourly peak load regulation system, ; For the Hourly peak load regulation system hot water boiler load rate, .

5. The method for fusion control of ground source heat pump system operation simulation prediction and measured data feedback according to claim 4 is characterized in that: After a short-cycle control of the geothermal heat pump system model is completed, the field measured data is obtained, the boundary conditions of the long-cycle operation scheduling and the boundary conditions of the short-cycle control are updated based on the field measured data, and the steps S2 to S4 are cyclically executed on the basis of the updated boundary conditions until the current long-cycle operation of the geothermal heat pump system model is completed: After the short-cycle control is completed, the measured ground source heat pump system operation data is uploaded to extract the operation scheduling plan for the last hour of the previous short cycle. And calculate the cumulative heat taken by the shallow soil source , delete the long-term load forecast long-term forecast load vector The data is updated, the short-cycle forecast load vector is updated, and the control hours of the current short cycle are updated; the long-cycle operation scheduling plan and the short-cycle control strategy plan are rolled out to obtain the equipment start-stop operation scheduling plan and specific operation control strategy for subsequent short cycles in the current long-cycle operation scheduling plan until the current long-cycle operation of the ground source heat pump system model ends.

6. A ground source heat pump system operation simulation prediction and measured data feedback fusion control system, characterized in that: include: Model building module, builds the ground source heat pump system model, inputs model parameters and initial boundary conditions, and performs long-term operation scheduling of the ground source heat pump system and operation simulation of multiple short-term controls under long-term based on the ground source heat pump system model; The long-term operation scheduling scheme calculation module selects the boundary conditions for the long-term operation scheduling of the ground source heat pump system model in the initial boundary conditions, and calculates the current long-term operation scheduling scheme of the ground source heat pump system model; The short-cycle control strategy calculation module selects the boundary conditions for the short-cycle control of the ground source heat pump system model in the initial boundary conditions based on the current long-cycle operation scheduling plan, calculates and issues the short-cycle control strategy under the current long-cycle operation scheduling plan of the ground source heat pump system model; The rolling operation module obtains the field measured data after a short-cycle control of the ground source heat pump system model ends, updates the boundary conditions of the long-cycle operation scheduling and the boundary conditions of the short-cycle control based on the field measured data, and cyclically executes the long-cycle operation scheduling scheme calculation module-rolling operation module on the basis of the updated boundary conditions until the current long-cycle operation of the ground source heat pump system model ends; Among them, the field measured data is used to update the corresponding data in the boundary conditions of the long-term operation scheduling scheme calculation module.

7. An electronic device, characterized in that It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the ground source heat pump system operation simulation prediction and measured data feedback fusion control method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for fusion control of ground source heat pump system operation simulation prediction and measured data feedback is implemented as described in any one of claims 1 to 5.

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