Heating control system and method based on solar energy-heat pump-building collaborative optimization

Abstract

The invention discloses a heating control system based on solar energy-heat pump-building collaborative optimization and a construction method, and belongs to the technical field of renewable energy source heating. Comprising a parameter acquisition module, a dynamic prediction module and an optimization control module. The method comprises the following steps: constructing a building heat transfer dynamic prediction model, a solar energy performance prediction physical model, constructing a ground source heat pump performance prediction model and a solar energy-ground source heat pump system collaborative optimization control model, and predicting and optimizing system operation parameters in the next day. Through cooperative work of the parameter acquisition module, the dynamic prediction module and the optimization control module, intelligentization and high efficiency of building heating are achieved, solar energy and the heat pump efficiency can be utilized to the maximum extent, the ground source heat pump efficiency is improved, and energy consumption is reduced while the indoor comfort degree is guaranteed. In addition, a time-of-use electricity price mechanism is utilized to further optimize system operation parameters, the system operation cost is reduced, and the energy efficiency and the response capability of the system are improved.

Description

Technical Field

[0001] The invention relates to a heating control system and a construction method based on solar energy-heat pump-building collaborative optimization, belonging to the technical field of renewable energy heating. Background Art

[0002] Solar-heat pump heating system is widely used due to its energy-saving and environmental protection characteristics. The system integrates two heat sources, solar energy and heat pump. Through dual heat sources, it not only promotes the effective use of renewable energy, but also effectively guarantees the thermal comfort environment indoors. However, the use of multiple heat sources, the uncertainty of renewable energy, and the time-of-use electricity price policy of the new power system increase the complexity of the operation of solar-heat pump heating system. How to maximize the use of solar energy, improve the operating efficiency of heat pumps and the demand response capability of heating systems, and ensure environmental thermal comfort is of great significance to its potential for energy conservation and emission reduction.

[0003] Most existing solar-heat pump heating systems use rule-based control methods, that is, the control parameters of solar thermal collection / heating and heat pumps are fixed and lack the ability to dynamically adjust according to changes in operating conditions and time-of-use electricity price policies. Although this method is simple and easy to implement, it cannot fully tap the energy-saving and emission-reduction potential of the system.

[0004] Existing research has explored certain optimization controls for solar-heat pump systems. For example, the Chinese invention patent with publication number CN118836585 A, entitled “A solar-coupled dual-source heat pump heating and cooling system and control method thereof”, discloses combining solar heat storage and air energy heat supplementation to intelligently optimize the heating and cooling of buildings. However, it mainly provides improvements in the mutual backup mechanism of air source and water source heat pumps, and lacks research on detailed control of the system in different time periods. The Chinese invention patent with publication number CN 118816407 A, entitled “Solar and air energy transcritical carbon dioxide heat pump heating system and control method”, discloses intelligent switching between solar energy and air energy, and can automatically select the best heating method when demand is high, but still needs to be further optimized in terms of improving solar energy utilization and heat pump efficiency. The Chinese invention patent with publication number CN 118657310 A, entitled “Taking into account The Chinese invention patent of "Optimal Scheduling Method for Low-Carbon Heating System in Park with High Efficiency and Economy" discloses the use of multi-objective optimization to achieve low-carbon heating in the park, but it mainly focuses on overall scheduling and lacks detailed control of the combined system of solar energy and heat pumps in different time periods.

[0005] In summary, the existing control methods of solar-heat pump heating systems are difficult to dynamically adjust according to the external environment, building demand and power response requirements. Therefore, there is an urgent need for an optimization control method based on demand response that considers solar-heat pump-building synergy. By combining weather forecasts, electricity price information and building demand, the next day's operating parameters can be optimized to improve solar energy utilization, heat pump efficiency and building thermal comfort. Summary of the invention

[0006] In order to solve the problems existing in the background technology, the present invention provides a heating control system and a construction method based on solar energy-heat pump-building collaborative optimization.

[0007] To achieve the above purpose, the present invention adopts the following technical scheme: a heating control system based on solar energy-heat pump-building collaborative optimization, comprising

[0008] Parameter acquisition module, used to obtain various input information required by the system;

[0009] Dynamic prediction module, including multiple prediction models, used to dynamically predict the operating status and energy consumption of each part of the system;

[0010] The optimization control module uses an optimization algorithm to dynamically optimize the hourly optimal settings of system operating parameters by calling a dynamic prediction model and combining it with computer calculations, so as to achieve efficient operation of the heating system and minimize operating costs.

[0011] The present invention provides a method for constructing a heating control system based on solar energy-heat pump-building collaborative optimization, the method comprising the following steps:

[0012] S1: Construct a dynamic prediction model for building heat transfer;

[0013] S101: Based on the thermal resistance and heat capacity analogy principle and the heat transfer performance of radiant floors, a dynamic prediction model for building heat transfer is constructed:

[0014]

[0015] In formula (1):

[0016] C ai is the indoor air heat capacity;

[0017] T ai is the predicted value of indoor air temperature;

[0018] t is time;

[0019] i is the i-th non-transparent enclosure structure;

[0020] n is the total number of non-transparent enclosure structures;

[0021] Tw_in,i is the inner surface temperature of the i-th non-transparent enclosure structure;

[0022] R w_in,i is the heat exchange resistance between the inner surface of the i-th non-transparent enclosure structure and the indoor air;

[0023] T im is the internal heat storage body temperature;

[0024] R im It is the heat exchange resistance between the internal heat storage body and the indoor air;

[0025] T floor is the floor surface temperature;

[0026] R floor It is the thermal resistance of heat exchange between floor material and indoor air;

[0027] Q heat It is the heat transfer in the form of floor radiation terminal;

[0028] Q window Transfer heat to transparent envelope structures;

[0029] Q rad It is the radiant heat transfer at the end of the floor radiation;

[0030] Q conv It is the convection heat transfer between the floor radiation terminal and the indoor air;

[0031] ε floor is the emissivity of the floor;

[0032] A floor is the effective heating area of ​​the floor;

[0033] σ is the Stefan-Boltzmann constant;

[0034] h floor is the convective heat transfer coefficient between the floor and the indoor air;

[0035] T floor,in is the inner surface temperature of the floor;

[0036] S102: Based on actual operation data, a genetic algorithm is used to optimize and identify the dynamic prediction model of building heat transfer;

[0037] S10201: Real-time data collection: The parameter acquisition module continuously collects outdoor environmental data and indoor temperature measurement data;

[0038] S10202: rolling optimization process: within a set time interval, the parameters of the building heat transfer dynamic prediction model are optimized based on the genetic algorithm to select the optimal parameter combination;

[0039] S10203: Parameter update: After each optimization, the optimization control module updates the optimal parameter combination to the building heat transfer dynamic prediction model, so that the building heat transfer dynamic prediction model can continue to maintain a high-precision prediction of the current working conditions.

[0040] S103: Input the weather forecast parameters for the next 24 hours into the dynamic prediction model for building heat transfer, and predict the hourly indoor temperature and building heating load for the next day.

[0041] S10301: Based on the dynamic prediction model of building heat transfer, the indoor air temperature is predicted to be T ai As the central node, based on formula (1), formula (2) is constructed to calculate the indoor temperature change and building heat load:

[0042]

[0043] S10302: Calculate the heat transfer of the room through the non-transparent enclosure structure in the first term on the right side of formula (2):

[0044]

[0045] In formula (3):

[0046] C floor is the heat capacity of the floor;

[0047] T floor,out is the outer surface temperature of the floor;

[0048] Q floor,solar The heat received by the floor surface from solar radiation;

[0049] C w,i is the heat capacity of the i-th non-transparent enclosure structure;

[0050] T w_out,i is the outer surface temperature of the i-th non-transparent enclosure structure;

[0051] R w,i is the heat transfer resistance of the i-th non-transparent enclosure structure;

[0052] R w_out,i is the heat exchange resistance between the outer surface of the i-th non-transparent enclosure structure and the outdoor air;

[0053] T ao is the outdoor air temperature;

[0054] Q solar_w,i is the solar radiation heat gain on the surface of the i-th non-transparent enclosure structure;

[0055] S10303: Calculate the heat storage and release characteristics of thermal storage bodies inside buildings:

[0056]

[0057] In formula (4):

[0058] C im is the heat capacity of the internal heat storage body;

[0059] Q solar,im The heat gained by solar radiation through windows and heat exchange with internal heat storage bodies;

[0060] Q floor,rad Radiant heat transfer to internal heat storage body for floor radiant heating;

[0061] S10304: Calculate the heat gain from solar radiation:

[0062] Q solar =p×I (5)

[0063] In formula (5):

[0064] Q solar Heat is gained by solar radiation;

[0065] p is the solar radiation absorption coefficient;

[0066] I is the predicted value of solar radiation intensity;

[0067] S10305: Calculation of heat transfer Q of transparent enclosure structures window :

[0068]

[0069] In formula (6):

[0070] j is the jth transparent enclosure structure;

[0071] z is the total number of transparent enclosure structures;

[0072] R window,j is the heat transfer resistance of the jth transparent enclosure structure;

[0073] S10306: The genetic algorithm inputs the model prediction value and the actual measurement value, and takes the minimum deviation between the model predicted indoor temperature and the actual indoor temperature as the goal, and identifies each parameter value by rolling optimization. The specific objective function of the model identification is as follows:

[0074]

[0075] In formula (7):

[0076] F is the objective function value of the optimization problem;

[0077] t1 and t2 are the start and end time of identification and optimization respectively;

[0078] T ai,test is the indoor air temperature monitoring value;

[0079] S10307: Calculate the heating water temperature and heating load of the heating system:

[0080]

[0081] In formula (8):

[0082] c is the specific heat capacity of water;

[0083] m is the water flow rate of the heating system;

[0084] T ws The water supply temperature at the end of the heating system;

[0085] T wr It is the return water temperature at the end of the heating system.

[0086] S2: Construct a physical model for solar energy performance prediction to predict the heat output of the solar energy system and the energy consumption of the water pump;

[0087] S201: Use simulation modeling software to build and verify a solar energy performance prediction physical model based on actual cases, generate a callable FMU model, and set parameters in the solar energy system for subsequent heat output prediction and control optimization;

[0088] S202: Use Python language to call the FMU model, and predict the solar energy collection, heating and water pump operation energy consumption of the solar energy system on an hourly basis the next day based on the future 24h weather forecast parameters and the building return water temperature.

[0089] S3: Construct a performance prediction model for ground-source heat pumps to predict the energy consumption and operating costs of ground-source heat pumps;

[0090] 301: Calculate the heating capacity of ground source heat pump:

[0091] Q GSHP (T wr ,T es )=cap(T wr ,T es )×Q nom (9)

[0092] In formula (9):

[0093] Q GSHP (T wr ,T es ) is the predicted value of the heating capacity of the ground source heat pump;

[0094] is the correction coefficient of the return water temperature on the building side and the supply water temperature on the ground source heat pump side for the heat supply of the ground source heat pump, which is determined by the multivariate linear regression method. α1, α2, α3, α4, α5 and α6 are all fitting coefficients obtained by fitting the experimental data;

[0095] Q nom The heating capacity of the ground source heat pump unit under nominal operating conditions;

[0096] T wr It is the return water temperature at the end of the heating system;

[0097] T es The water supply temperature on the ground source heat pump side;

[0098] 302: Calculate the energy consumption input ratio of the ground source heat pump:

[0099] EIR GSHP (T wr ,T es )=EIR(T wr ,T es ) / COP nom (10)

[0100] In formula (10):

[0101] EIR GSHP (T wr ,T es ) is the predicted value of the energy consumption input ratio of the ground source heat pump;

[0102] is the correction coefficient of the system water temperature to energy consumption input ratio, which is determined by the multivariate linear regression method. b1, b2, b3, b4, b5 and b6 are all fitting coefficients obtained by fitting the experimental data;

[0103] COP nom is the coefficient of performance of the ground source heat pump unit under rated conditions;

[0104] 303: Predicting the energy consumption of ground source heat pumps based on the ground source heat pump heating capacity and energy consumption input ratio:

[0105] W GSHP (T wr ,T es )=Q GSHP (T wr ,T es )×EIR GSHP (T wr ,T es ) (11)

[0106] 304: Based on the predicted energy consumption of the ground source heat pump, the predicted energy consumption of the solar side water pump and the time-of-use electricity price policy, the operation cost prediction model of the solar-heat pump heating system is constructed as follows to estimate the hourly operation cost of the heating system the next day:

[0107]

[0108] In formula (12):

[0109] C system Cost of running a geothermal heat pump heating system;

[0110] P is the time-of-use electricity price;

[0111] W sys,pump Energy consumption of circulating water pump for heating system;

[0112] W solar,pump is the energy consumption of the water pump on the solar side;

[0113] W sc,pump is the energy consumption of the hot water pump on the solar side;

[0114] W sh,pump Energy consumption of circulating pumps for solar heating.

[0115] S4: Construct a collaborative optimization control model for the solar-ground source heat pump system to predict and optimize the system operating parameters for the next day.

[0116] S401: Construct an objective function with the goal of minimizing the heating operation cost and the indoor temperature and its set deviation:

[0117]

[0118] In formula (13):

[0119] The J value represents the effect of operating costs and indoor temperature deviation;

[0120] T wr,set,k The return water temperature setting value of the ground source heat pump at different time k the next day;

[0121] ΔT sc,set,k is the solar heat collection temperature difference setting value at different time k of the next day;

[0122] ΔT sh,set,k is the set value of the solar heating temperature difference at different times k the next day;

[0123] ε is the operating cost weight factor;

[0124] t3 and t4 are the start and end time of room use respectively;

[0125] T ai,setSet the room temperature for different times k the next day;

[0126] S402: Setting upper and lower limits of operating parameters:

[0127] T wr,set,min ≤T wr,set ≤T wr,set,max (14)

[0128] ΔT sc,set,min ≤ΔT sc,set ≤ΔT sc,set,max (15)

[0129] ΔT sh,set,min ≤ΔT sh,set ≤ΔT sh,set,max (16)

[0130] In formulas (14)-(16):

[0131] T wr,set,min and T wr,set,max They are the minimum water temperature setting value and the maximum water temperature setting value during the operation of the ground source heat pump respectively;

[0132] ΔT sc,set,min and ΔT sc,set,max They are the minimum water temperature setting value and the maximum water temperature setting value of the solar heat collection temperature difference respectively;

[0133] ΔT sh,set,min and ΔT sh,set,max They are the minimum water temperature setting value and the maximum water temperature setting value of the solar heating temperature difference respectively;

[0134] S403: Setting heating water temperature increase and decrease constraints:

[0135] T ws (t)-T ws (t+1)≤-ΔT up ,T ws (t)<T ws (t+1) (17)

[0136] T wr (t)-T wr (t+1)≤ΔT down ,T wr (t)>T wr (t+1) (18)

[0137] In formula (17) and (18):

[0138] ΔT up and ΔT down are the maximum temperature differences during the rising and falling periods of the heating water temperature, respectively;

[0139] S404: Set indoor temperature design constraints:

[0140] T ai,min ≤T ai ≤T ai,max (19)

[0141] In formula (15):

[0142] T ai,min and T ai,max They are the upper and lower limits of the indoor temperature design range during heating period.

[0143] Compared with the prior art, the present invention has the following beneficial effects:

[0144] The present invention realizes intelligent and efficient building heating through the coordinated work of three modules: parameter acquisition, dynamic prediction, and optimization control. It can maximize the use of solar energy and heat pump efficiency, improve the efficiency of ground source heat pumps, and reduce energy consumption while ensuring indoor comfort. In addition, the time-of-use electricity price mechanism is used to further optimize the system operating parameters, reduce the system operating costs, and improve the system's energy efficiency and responsiveness, providing reliable theoretical support and technical guarantees for the efficient operation of solar-heat pump heating systems in complex environments, promoting the consumption of renewable energy electricity, and reducing users' heating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0145] Figure 1 is a flow chart of a method for constructing a heating control system of the present invention;

[0146] Figure 2 It is a schematic diagram of the principle and monitoring points of the solar-heat pump heating system;

[0147] Figure 3 Schematic diagram of the heating control system of the present invention. DETAILED DESCRIPTION

[0148] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0149] A heating control system based on solar energy-heat pump-building collaborative optimization, comprising

[0150] The parameter acquisition module is used to obtain various input information required by the system, such as obtaining weather forecast information for the next 24 hours (including outdoor air temperature and solar radiation intensity data) through the API interface, and obtaining the operating data of the heating system (including the operating status of the ground source heat pump system, the operating status of the solar energy system, and the system water temperature information) through the FMI interface protocol, providing the necessary basic input for subsequent prediction and optimization;

[0151] The dynamic prediction module includes a variety of prediction models, such as the building heat transfer dynamic prediction model for indoor temperature prediction, the solar energy performance prediction physical model and the ground source heat pump performance prediction model, which are used to dynamically predict the operating status and energy consumption of each part of the system; by obtaining basic parameters and operating data, the prediction model is used to accurately predict the indoor temperature, heating load and system operating costs for the next 24 hours, ensuring reliable input for the optimization control module.

[0152] The optimization control module uses optimization algorithms such as genetic algorithms to dynamically optimize the hourly optimal settings of system operating parameters (including the ground source heat pump return water temperature setting value, solar energy collection temperature difference setting value, and solar energy heating temperature difference setting value) by calling dynamic prediction models and combining computer calculations to achieve efficient operation of the heating system and minimize operating costs.

[0153] The goal of the optimization control module is to ensure thermal comfort in the building while minimizing the energy consumption and operating costs of the system. The optimization results are directly applied to the heating system equipment through the controller, and the operating status of the heating system is adjusted in real time to achieve efficient and energy-saving heating control.

[0154] The present invention provides a method for constructing a heating control system based on solar energy-heat pump-building collaborative optimization, the method comprising the following steps:

[0155] S1: Construct a dynamic prediction model for building heat transfer;

[0156] S101: Considering the outdoor air temperature, solar radiation intensity, return water temperature of the ground source heat pump system, floor radiation terminal heat exchange, and thermal inertia caused by heat storage bodies such as building envelopes, walls and pipe networks, a dynamic prediction model of building heat transfer is constructed based on the thermal resistance and heat capacity analogy principle and the heat transfer performance of the radiation floor:

[0157]

[0158] In formula (1):

[0159] C ai is the indoor air heat capacity, in J / (m 2 ℃);

[0160] T aiis the predicted value of indoor air temperature, in °C;

[0161] t is time, unit is h;

[0162] i is the i-th non-transparent enclosure structure, such as the exterior wall or roof;

[0163] n is the total number of non-transparent enclosure structures;

[0164] T w_in,i is the inner surface temperature of the i-th non-transparent enclosure structure, in °C;

[0165] R w_in,i is the heat transfer resistance between the inner surface of the i-th non-transparent enclosure structure and the indoor air, in m 2 ℃ / W;

[0166] T im is the internal heat storage body temperature, in °C;

[0167] R im is the heat exchange resistance between the internal heat storage body and the indoor air, in m 2 ℃ / W;

[0168] T floor is the floor surface temperature, in °C;

[0169] R floor is the heat transfer resistance between floor material and indoor air, in m 2 ℃ / W;

[0170] Q heat The heat transfer in the form of floor radiation terminal, unit is W / m 2 ;

[0171] Q window The heat transfer of the transparent enclosure structure, in W / m 2 ;

[0172] Q rad is the radiation heat transfer at the end of the floor radiation, in W / m 2 ;

[0173] Q conv is the convection heat transfer between the floor radiation terminal and the indoor air, in W / m 2 ;

[0174] ε floor is the emissivity of the floor;

[0175] A floor is the effective heating area of ​​the floor, in m 2 ;

[0176] σ is the Stefan-Boltzmann constant, which is approximately 5.67×10 -8 W / m 2 ·K 4 ;

[0177] h floor is the convection heat transfer coefficient between the floor and the indoor air, in W / m 2 K;

[0178] T floor,in is the inner surface temperature of the floor, in °C;

[0179] S102: Based on actual operation data, a genetic algorithm is used to optimize and identify the dynamic prediction model of building heat transfer;

[0180] Since some parameters in the dynamic prediction model of building heat transfer are affected by changes in the outdoor environment, such as increased wind speed leading to reduced thermal resistance of the outer surface, etc. Therefore, in order to obtain accurate values ​​of thermal resistance, heat capacity and solar radiation absorption coefficient, a genetic algorithm is used for rolling optimization identification. The identification process of each parameter is based on real-time input data, so S102 includes the following steps:

[0181] S10201: Real-time data collection: The parameter acquisition module continuously collects outdoor environmental data (such as wind speed, temperature, radiation intensity) and indoor temperature measurement data to reflect the impact of external conditions on the thermal characteristics of the building;

[0182] S10202: Rolling optimization process: within a set time interval (e.g. 1 hour), the parameters of the building heat transfer dynamic prediction model are optimized based on the genetic algorithm to select the optimal parameter combination. The rolling frequency and time interval are dynamically adjusted according to the actual working conditions;

[0183] S10203: Parameter update: After each optimization, the optimization control module updates the optimal parameter combination to the building heat transfer dynamic prediction model, so that the building heat transfer dynamic prediction model can continue to maintain a high-precision prediction of the current working conditions.

[0184] S103: Input the weather forecast parameters for the next 24 hours into the dynamic prediction model for building heat transfer, and predict the hourly indoor temperature and building heating load for the next day.

[0185] S10301: Based on the dynamic prediction model of building heat transfer, the indoor air temperature is predicted to be T ai As the central node, based on formula (1), formula (2) is constructed to calculate the indoor temperature change and building heat load:

[0186]

[0187] When calculating indoor temperature changes, input the corresponding outdoor temperature, solar radiation and floor radiation heating, and combine all the building heat transfer dynamic prediction model formulas to calculate the indoor temperature changes.

[0188] When calculating the building heat load, it can be assumed that the indoor air temperature does not fluctuate over time, that is, the left side of formula (2) is 0. By combining all the building heat transfer dynamic prediction model formulas, the floor radiation heat supply Q at this time can be obtained: rad +Q conv , that is, the building heat load.

[0189] S10302: Calculate the heat transfer of the room through the non-transparent enclosure structure in the first term on the right side of formula (2):

[0190]

[0191] In formula (3):

[0192] C floor is the heat capacity of the floor, in J / (m 2 ℃);

[0193] T floor,out is the outer surface temperature of the floor, in °C;

[0194] Q floor,solar The solar radiation heat received by the floor surface, in W / m 2 ;

[0195] C w,i is the heat capacity of the i-th non-transparent enclosure structure, in J / (m 2 ℃);

[0196] T w_out,i is the outer surface temperature of the i-th non-transparent enclosure structure, in °C;

[0197] R w,i is the heat transfer resistance of the i-th non-transparent enclosure structure, in m 2 ℃ / W;

[0198] R w_out,i is the heat transfer resistance between the outer surface of the i-th non-transparent enclosure structure and the outdoor air, in m 2 ℃ / W;

[0199] T ao is the outdoor air temperature in °C, obtained from the meteorological website;

[0200] Q solar_w,i is the solar radiation heat gain of the i-th non-transparent enclosure surface, in W / m 2 .

[0201] The internal heat storage body of the building composed of interior walls, furniture, etc., increases its temperature continuously through convection heat exchange or solar radiation through windows. When the surface temperature of the internal heat storage body is higher than the indoor air temperature, the stored heat is gradually released into the indoor air in the form of convection to ensure indoor comfort.

[0202] S10303: Calculate the heat storage and release characteristics of thermal storage bodies inside buildings:

[0203]

[0204] In formula (4):

[0205] C im is the heat capacity of the internal heat storage body, in J / (m 2 ℃);

[0206] Q solar,im The solar radiation heat gain through the window and the internal heat storage body, the unit is W / m 2 ;

[0207] Q floor,rad The radiation heat exchange of floor radiant heating to the internal heat storage body, the unit is W / m 2 ;

[0208] S10304: Calculate the heat gain from solar radiation:

[0209] Q solar =p×I (5)

[0210] In formula (5):

[0211] Q solar The solar radiation heat gain, unit is W / m 2 ;

[0212] p is the solar radiation absorption coefficient, which is affected by the absorption rate and orientation, and is identified by the genetic algorithm;

[0213] I is the predicted value of solar radiation intensity, in W / m 2 , obtained from the meteorological website;

[0214] S10305: Calculation of heat transfer Q of transparent enclosure structures window :

[0215]

[0216] In formula (6):

[0217] j is the jth transparent enclosure structure;

[0218] z is the total number of transparent enclosure structures;

[0219] R window,j is the heat transfer resistance of the jth transparent enclosure structure, in m 2 ℃ / W;

[0220] S10306: The genetic algorithm inputs the indoor air temperature model prediction value and actual measurement value for 5 consecutive days at the beginning of the heating season with an interval of 1 hour. With the goal of minimizing the deviation between the model predicted indoor temperature and the actual indoor temperature, the rolling optimization is used to identify the values ​​of each parameter. The specific objective function of the model identification is as follows:

[0221]

[0222] In formula (7):

[0223] F is the objective function value of the optimization problem. By minimizing F, the combination of input parameters can be optimized so that the fitting error between the model predicted temperature and the actual measured temperature is minimized;

[0224] t1 and t2 are the start and end time of identification and optimization, respectively, with an interval of 120 hours;

[0225] T ai,test is the indoor air temperature monitoring value, in °C;

[0226] S10307: After the dynamic prediction model of building heat transfer is constructed and parameter identification and optimization are completed, the outdoor air temperature, solar radiation and heat pump heating water temperature in the next 24 hours are input into the above-mentioned dynamic prediction model of building heat transfer to predict the indoor air temperature in the next 24 hours.

[0227] According to the energy conservation principle, the sum of the floor radiation heat dissipation and convection heat dissipation is equal to the heat dissipated by the hot water in the pipe network through the terminal. Therefore, the heating water temperature and heating load of the heating system can be calculated according to formula (23):

[0228]

[0229] In formula (8):

[0230] c is the specific heat capacity of water, in J / (kg·℃);

[0231] m is the water flow rate of the heating system, in kg / s;

[0232] T ws It is the water supply temperature at the end of the heating system, in °C.

[0233] T wr It is the return water temperature at the end of the heating system, in °C.

[0234] S2: Construct a physical model for solar energy performance prediction to predict the heat output of the solar energy system and the energy consumption of the water pump;

[0235] S201: Use simulation modeling software such as Dymola and TRNSYS to build a physical model for solar energy performance prediction based on actual cases (build a simulation model based on the composition of the solar energy system. The main modeling components include sub-models such as solar collectors, plate heat exchangers, hot water storage tanks, and circulating water pumps. The model takes into account physical properties such as collector area, thermal efficiency, material, and heat loss coefficient. At the same time, environmental parameters such as outdoor air temperature and solar radiation intensity in the next 24 hours are introduced to improve the accuracy of the prediction) and verify (verify the accuracy of the model by comparing the simulation model output with historical operation data. The verification process includes verification of key indicators such as heat output, energy consumption, and system response time to ensure that the simulation model can truly reflect the dynamic behavior of the system) to generate a callable FMU (Functional Mock-up) The solar energy system FMU model is exported through Dymola, which can be seamlessly integrated with the optimization control module under the standardized framework, and the parameters such as the collector type, collector area, hot water tank volume and water pump in the solar energy system can be set for subsequent heat output prediction and control optimization. The advantage of choosing Dymola as a modeling tool is that it supports multi-physics system modeling and can output standardized FMU files, which is easy to integrate with tools such as Python.

[0236] S202: Use Python language to call the FMU model, and predict the solar energy collection, heating and water pump operation energy consumption of the solar energy system on an hourly basis the next day based on the future 24h weather forecast parameters and the building return water temperature.

[0237] Different from the traditional multivariate regression analysis method, this method is based on physical modeling FMU simulation, which can more accurately capture the dynamic behavior of the solar energy system. At the same time, the flexible adjustment of model parameters makes it more reliable and adaptable, providing important technical support for improving solar energy utilization efficiency and reducing operating costs.

[0238] S3: Construct a performance prediction model for ground-source heat pumps to predict the energy consumption and operating costs of ground-source heat pumps;

[0239] 301: Calculate the heating capacity of ground source heat pump:

[0240] Q GSHP (T wr ,T es )=cap(T wr ,T es )×Q nom (9)

[0241] In formula (9):

[0242] Q GSHP (Twr ,T es ) is the predicted value of the heating capacity of the ground source heat pump;

[0243] is the correction coefficient of the return water temperature on the building side and the supply water temperature on the ground source heat pump side for the heat supply of the ground source heat pump, which is determined by the multivariate linear regression method. α1, α2, α3, α4, α5 and α6 are all fitting coefficients obtained by fitting the experimental data;

[0244] Q nom The nominal heating capacity of the ground source heat pump unit, in kW, is obtained from the unit nameplate;

[0245] T wr is the return water temperature at the end of the heating system, in °C, obtained through regression fitting using the Scikit-learn library in Python based on the actual operation data of the ground-source heat pump;

[0246] T es is the water supply temperature on the ground source heat pump side, in °C;

[0247] 302: Calculate the energy consumption input ratio of the ground source heat pump (the ratio of the operating energy consumption of the ground source heat pump unit to the heating capacity):

[0248] EIR GSHP (T wr ,T es )=EIR(T wr ,T es ) / COP nom (10)

[0249] In formula (10):

[0250] EIR GSHP (T wr ,T es ) is the predicted value of the energy consumption input ratio of the ground source heat pump;

[0251] is the correction coefficient of the system water temperature to energy consumption input ratio, which is determined by the multivariate linear regression method. b1, b2, b3, b4, b5 and b6 are all fitting coefficients obtained by fitting the experimental data;

[0252] COP nom is the coefficient of performance of the ground source heat pump unit under rated conditions, obtained from the unit nameplate;

[0253] 303: Predicting the energy consumption of ground source heat pumps based on the ground source heat pump heating capacity and energy consumption input ratio:

[0254] W GSHP (T wr ,T es )=QGSHP (T wr ,T es )×EIR GSHP (T wr ,T es ) (11)

[0255] 304: Based on the predicted energy consumption of the ground source heat pump, the predicted energy consumption of the solar side water pump and the time-of-use electricity price policy, the operation cost prediction model of the solar-heat pump heating system is constructed as follows to estimate the hourly operation cost of the heating system the next day:

[0256]

[0257] In formula (12):

[0258] C system is the operating cost of the ground source heat pump heating system, in yuan;

[0259] P is the time-of-use electricity price, in yuan / kWh;

[0260] W sys,pump The energy consumption of the circulating water pump of the heating system, in kWh;

[0261] W solar,pump is the energy consumption of the water pump on the solar side, in kWh.

[0262] W sc,pump is the energy consumption of the hot water pump on the solar side, in kWh;

[0263] W sh,pump The energy consumption of the solar heating circulation pump is in kWh.

[0264] S4: Construct a collaborative optimization control model for the solar-ground source heat pump system to predict and optimize the system operating parameters for the next day.

[0265] S401: Construct an objective function with the goal of minimizing the heating operation cost and the indoor temperature and its set deviation:

[0266]

[0267] In formula (13):

[0268] The J value represents the effect of operating costs and indoor temperature deviation, regardless of the unit;

[0269] T wr,set,k The return water temperature setting value of the ground source heat pump at different time k the next day, in °C;

[0270] ΔT sc,set,k is the solar heat collection temperature difference setting value at different time k of the next day, in ℃;

[0271] ΔT sh,set,k is the set value of solar heating temperature difference at different time k of the next day, in °C;

[0272] ε is the operating cost weight factor, which is set according to the user's demand for reduced operating costs;

[0273] t3 and t4 are the start time and end time of room use, such as 8:00-20:00;

[0274] T ai,set Set the room temperature at different time k the next day in °C.

[0275] ε×C system In order to respond to the total operating cost of the solar-heat pump heating system during the time-of-use electricity price, the responsiveness of the time-of-use electricity price is guaranteed by adding a cost weight factor;

[0276] Represents the deviation of indoor air temperature from its set value during the response time electricity price period.

[0277] S402: Setting upper and lower limits of operating parameters:

[0278] T wr,set,min ≤T wr,set ≤T wr,set,max (14)

[0279] ΔT sc,set,min ≤ΔT sc,set ≤ΔT sc,set,max (15)

[0280] ΔT sh,set,min ≤ΔT sh,set ≤ΔT sh,set,max (16)

[0281] In formulas (14)-(16):

[0282] T wr,set,min and T wr,set,max They are the lowest and highest water temperature setting values ​​during the operation of the ground source heat pump, in °C;

[0283] ΔT sc,set,min and ΔT sc,set,max They are the minimum and maximum water temperature setting values ​​of the solar heat collection temperature difference, in °C;

[0284] ΔT sh,set,min and ΔT sh,set,max They are the minimum and maximum water temperature setting values ​​of the solar heating temperature difference, in °C;

[0285] S403: Setting heating water temperature increase and decrease constraints:

[0286] T ws (t)-T ws (t+1)≤-ΔT up ,T ws (t)<T ws (t+1) (17)

[0287] T wr (t)-T wr (t+1)≤ΔT down ,T wr (t)>T wr (t+1) (18)

[0288] In formula (17) and (18):

[0289] ΔT up and ΔT down are the maximum temperature differences during the rising and falling periods of the heating water temperature, ℃ respectively.

[0290] S404: Set indoor temperature design constraints:

[0291] T ai,min ≤T ai ≤T ai,max (19)

[0292] In formula (15):

[0293] T ai,min and T ai,max are the upper and lower limits of the indoor temperature design range during heating, ℃.

[0294] Embodiment 1:

[0295] S401: Construct an objective function with the goal of minimizing the heating operation cost and the indoor temperature and its set deviation:

[0296]

[0297] S402: Setting upper and lower limits of operating parameters:

[0298] 20≤T wr,set ≤45 (21)

[0299] 10≤ΔT sc,set ≤25 (22)

[0300] 2≤ΔT sh,set ≤10 (23)

[0301] S403: Setting heating water temperature increase and decrease constraints:

[0302] T ws (t)-T ws (t+1)≤-10,T ws (t)<T ws (t+1) (24)

[0303] T wr (t)-T wr (t+1)≤5,T wr (t)>T wr (t+1) (25)

[0304] S404: Set indoor air temperature constraints:

[0305] 22≤T ai ≤26 (26)

[0306] According to the results of dynamic planning of heating temperature, the system operating parameters are modified in real time through the controller, while the heating system operating data is monitored, the heating system operating data set is continuously updated, and the prediction model is rolled out for optimization to improve the accuracy of dynamic temperature planning.

[0307] After completing dynamic planning and adjusting various operating parameters in real time through the controller, the entire system process continues to move forward to ensure that the optimization results can be effectively applied and feedback optimized in actual operation.

[0308] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and range of equivalents of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0309] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A heating control system based on solar energy-heat pump-building collaborative optimization, characterized by: include: Parameter acquisition module, used to obtain various input information required by the system; Dynamic prediction module, including multiple prediction models, used to dynamically predict the operating status and energy consumption of each part of the system; The optimization control module uses an optimization algorithm to dynamically optimize the hourly optimal settings of system operating parameters by calling a dynamic prediction model and combining it with computer calculations, so as to achieve efficient operation of the heating system and minimize operating costs.

2. A method for constructing a heating control system based on solar energy-heat pump-building collaborative optimization according to claim 1, characterized in that: The method comprises the following steps: S1: Construct a dynamic prediction model for building heat transfer; S2: Construct a physical model for solar energy performance prediction to predict the heat output of the solar energy system and the energy consumption of the water pump; S3: Construct a performance prediction model for ground-source heat pumps to predict the energy consumption and operating costs of ground-source heat pumps; S4: Construct a collaborative optimization control model for the solar-ground source heat pump system to predict and optimize the system operating parameters for the next day.

3. The method according to claim 2, characterized in that: The S1 comprises the following steps: S101: Based on the thermal resistance and heat capacity analogy principle and the heat transfer performance of radiant floors, a dynamic prediction model for building heat transfer is constructed: In formula (1): C ai is the indoor air heat capacity; T ai is the predicted value of indoor air temperature; t is time; i is the i-th non-transparent enclosure structure; n is the total number of non-transparent enclosure structures; T w_in,i is the inner surface temperature of the i-th non-transparent enclosure structure; R w_in,i is the heat exchange resistance between the inner surface of the i-th non-transparent enclosure structure and the indoor air; T im is the internal heat storage body temperature; R im It is the heat exchange resistance between the internal heat storage body and the indoor air; T floor is the floor surface temperature; R floor It is the thermal resistance of heat exchange between floor material and indoor air; Q heat It is the heat transfer in the form of floor radiation terminal; Q window Transfer heat to transparent envelope; Q rad It is the radiant heat transfer at the end of the floor radiation; Q conv It is the convection heat transfer between the floor radiation terminal and the indoor air; ε floor is the emissivity of the floor; A floor is the effective heating area of ​​the floor; σ is the Stefan-Boltzmann constant; h floor is the convective heat transfer coefficient between the floor and the indoor air; T floor,in is the inner surface temperature of the floor; S102: Based on actual operation data, a genetic algorithm is used to optimize and identify the dynamic prediction model of building heat transfer; S103: Input the weather forecast parameters for the next 24 hours into the dynamic prediction model for building heat transfer, and predict the hourly indoor temperature and building heating load for the next day.

4. The method according to claim 3, characterized in that: The S102 comprises the following steps: S10201: Real-time data collection: The parameter acquisition module continuously collects outdoor environmental data and indoor temperature measurement data; S10202: rolling optimization process: within a set time interval, the parameters of the building heat transfer dynamic prediction model are optimized based on the genetic algorithm to select the optimal parameter combination; S10203: Parameter update: After each optimization, the optimization control module updates the optimal parameter combination to the building heat transfer dynamic prediction model, so that the building heat transfer dynamic prediction model can continue to maintain a high-precision prediction of the current working conditions.

5. The method according to claim 4, characterized in that: The S103 comprises the following steps: S10301: Based on the dynamic prediction model of building heat transfer, the indoor air temperature is predicted to be T ai As the central node, based on formula (1), formula (2) is constructed to calculate the indoor temperature change and building heat load: S10302: Calculate the heat transfer of the room through the non-transparent enclosure structure in the first term on the right side of formula (2): In formula (3): C floor is the heat capacity of the floor; T floor,out is the outer surface temperature of the floor; Q floor,solar The heat received by the floor surface from solar radiation; C w,i is the heat capacity of the i-th non-transparent enclosure structure; T w_out,i is the outer surface temperature of the i-th non-transparent enclosure structure; R w,i is the heat transfer resistance of the i-th non-transparent enclosure structure; R w_out,i is the heat exchange resistance between the outer surface of the i-th non-transparent enclosure structure and the outdoor air; T ao is the outdoor air temperature; Q solar_w,i is the solar radiation heat gain on the surface of the i-th non-transparent enclosure structure; S10303: Calculate the heat storage and release characteristics of thermal storage bodies inside buildings: In formula (4): C im is the heat capacity of the internal heat storage body; Q solar,im The heat gained by solar radiation through windows and heat exchange with internal heat storage bodies; Q floor,rad Radiant heat transfer to internal heat storage body for floor radiant heating; S10304: Calculate the heat gain from solar radiation: Q solar =p×I (5) In formula (5): Q solar Heat is gained by solar radiation; p is the solar radiation absorption coefficient; I is the predicted value of solar radiation intensity; S10305: Calculation of heat transfer Q of transparent enclosure structures window : In formula (6): j is the jth transparent enclosure structure; z is the total number of transparent enclosure structures; R window,j is the heat transfer resistance of the jth transparent enclosure structure; S10306: The genetic algorithm inputs the model prediction value and the actual measurement value, and takes the minimum deviation between the model predicted indoor temperature and the actual indoor temperature as the goal, and identifies each parameter value by rolling optimization. The specific objective function of the model identification is as follows: In formula (7): F is the objective function value of the optimization problem; t1 and t2 are the start and end time of identification and optimization respectively; T ai,test is the indoor air temperature monitoring value; S10307: Calculate the heating water temperature and heating load of the heating system: In formula (8): c is the specific heat capacity of water; m is the water flow rate of the heating system; T ws The water supply temperature at the end of the heating system; T wr It is the return water temperature at the end of the heating system.

6. The method according to claim 2, characterized in that: The S2 comprises the following steps: S201: Use simulation modeling software to build and verify the solar energy performance prediction physical model based on actual cases, generate a callable FMU model, and set the parameters in the solar energy system for subsequent heat output prediction and control optimization; S202: Use Python language to call the FMU model, and predict the solar energy collection, heating and water pump operation energy consumption of the solar energy system on an hourly basis the next day based on the future 24h weather forecast parameters and the building return water temperature.

7. The method according to claim 2, characterized in that: The S3 comprises the following steps: 301: Calculate the heating capacity of ground source heat pump: Q GSHP (T wr ,T es )=cap(T wr ,T es )×Q nom (9) In formula (9): Q GSHP (T wr ,T es ) is the predicted value of the heating capacity of the ground source heat pump; is the correction coefficient of the return water temperature on the building side and the supply water temperature on the ground source heat pump side for the heat supply of the ground source heat pump, which is determined by the multivariate linear regression method. α1, α2, α3, α4, α5 and α6 are all fitting coefficients obtained by fitting the experimental data; Q nom The nominal heating capacity of the ground source heat pump unit; T wr It is the return water temperature at the end of the heating system; T es The water supply temperature on the ground source heat pump side; 302: Calculate the energy consumption input ratio of the ground source heat pump: EIR GSHP (T wr ,T es )=EIR(T wr ,T es ) / COP nom (10) In formula (10): EIR GSHP (T wr ,T es ) is the predicted value of the energy consumption input ratio of the ground source heat pump; is the correction coefficient of the system water temperature to energy consumption input ratio, which is determined by the multivariate linear regression method. b1, b2, b3, b4, b5 and b6 are all fitting coefficients obtained by fitting the experimental data; COP nom is the coefficient of performance of the ground source heat pump unit under rated conditions; 303: Predicting the energy consumption of ground source heat pumps based on the ground source heat pump heating capacity and energy consumption input ratio: W GSHP (T wr ,T es )=Q GSHP (T wr ,T es )×EIR GSHP (T wr ,T es ) (11) 304: Based on the predicted energy consumption of the ground source heat pump, the predicted energy consumption of the solar side water pump and the time-of-use electricity price policy, the operation cost prediction model of the solar-heat pump heating system is constructed as follows to estimate the hourly operation cost of the heating system the next day: In formula (12): C system Cost of running a geothermal heat pump heating system; P is the time-of-use electricity price; W sys,pump Energy consumption of circulating water pump for heating system; W solar,pump is the energy consumption of the water pump on the solar side; W sc,pump is the energy consumption of the hot water pump on the solar side; W sh,pump Energy consumption of circulating pumps for solar heating.

8. The method according to claim 7, characterized in that: The S4 comprises the following steps: S401: Construct an objective function with the goal of minimizing the heating operation cost and the indoor temperature and its set deviation: In formula (13): The J value represents the effect of operating costs and indoor temperature deviation; T wr,set,k The return water temperature setting value of the ground source heat pump at different time k the next day; ΔT sc,set,k is the solar heat collection temperature difference setting value at different time k of the next day; ΔT sh,set,k is the set value of the solar heating temperature difference at different times k the next day; ε is the operating cost weight factor; t3 and t4 are the start and end time of room use respectively; T ai,set Set the room temperature for different times k the next day; S402: Setting upper and lower limits of operating parameters: T wr,set,min ≤T wr,set ≤T wr,set,max (14) ΔT sc,set,min ≤ΔT sc,set ≤ΔT sc,set,max (15) ΔT sh,set,min ≤ΔT sh,set ≤ΔT sh,set,max (16) In formulas (14)-(16): T wr,set,min and T wr,set,max They are the minimum water temperature setting value and the maximum water temperature setting value during the operation of the ground source heat pump respectively; ΔT sc,set,min and ΔT sc,set,max They are the minimum water temperature setting value and the maximum water temperature setting value of the solar heat collection temperature difference respectively; ΔT sh,set,min and ΔT sh,set,max They are the minimum water temperature setting value and the maximum water temperature setting value of the solar heating temperature difference respectively; S403: Setting heating water temperature increase and decrease constraints: T ws (t)-T ws (t+1)≤-ΔT up ,T ws (t)<T ws (t+1) (17) T wr (t)-T wr (t+1)≤ΔT down ,T wr (t)>T wr (t+1) (18) In formula (17) and (18): ΔT up and ΔT down are the maximum temperature differences during the rising and falling periods of the heating water temperature, respectively; S404: Set indoor temperature design constraints: T ai,min ≤T ai ≤T ai,max (19) In formula (15): T ai,min and T ai,max They are the upper and lower limits of the indoor temperature design range during heating period.

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