Stadium energy-saving regulation and control system and method based on multi-source waste heat collaboration and AI prediction

Abstract

The invention belongs to the technical field of green buildings, and particularly relates to a stadium energy-saving regulation and control system and method based on multi-source waste heat collaboration and AI prediction. According to the scheme, waste heat such as condensation heat of a central air conditioner of a stadium and heat dissipation of lighting equipment is subjected to collaborative recovery; according to the scheme, changes of heat source intensities and terminal requirements can be sensed in real time, the pipeline connection mode is automatically reconfigured, the working medium flow is distributed, and the heat exchange power is adjusted, so that severe fluctuation of the heat source intensities of air conditioners, lamplight and the like in the venue is effectively dealt with, the method adapts to differences of different terminals in heat using time sequences and temperature grades, and the service life of the terminal is prolonged. And waste heat is timely conveyed to the corresponding terminal at proper temperature, so that the influence of excessive heat on heat dissipation of equipment is avoided, the reduction of user experience caused by insufficient heat is also prevented, and the bottleneck of dynamic mismatching caused by original fixed connection is broken through from the hardware level.

Description

Technical Field

[0001] This invention belongs to the field of green building technology, specifically relating to a venue energy-saving control system and method based on multi-source waste heat synergy and AI prediction. Background Technology

[0002] With the increasing national requirements for energy conservation and emission reduction in public buildings, energy management of high-energy-consuming public buildings such as large stadiums and cultural centers faces severe challenges. These venues are usually equipped with high-power central air conditioning systems and high-intensity lighting equipment, which generate a large amount of low-grade waste heat during operation. Traditionally, the waste heat is directly discharged into the atmosphere through cooling towers or air cooling, resulting in huge energy waste and environmental thermal pollution. At the same time, the operation of the venues often requires a large amount of energy to produce domestic hot water, maintain the constant temperature of the swimming pool, and provide floor heating in winter, resulting in high energy costs.

[0003] In existing technologies, some studies have attempted to recover the condensation heat of air conditioners for preheating domestic hot water, or to utilize waste heat from stationary heat sources such as data centers. However, the inventors have found that these solutions generally suffer from the following problems: (1) Existing solutions usually adopt fixed pipe connections and heat exchange methods, which cannot flexibly cope with the dynamic characteristics of the intensity of heat sources such as air conditioning and lighting in the venue fluctuating drastically over time, as well as the mismatch between the time sequence and temperature quality of the terminal demand such as domestic hot water, swimming pool, and floor heating, resulting in low recovery efficiency, and may even affect the normal operation of the main equipment due to excess or insufficient heat.

[0004] (2) Existing control strategies are usually based on simple threshold control or static scheduling, lacking collaborative prediction and dynamic optimization of the supply and demand behavior of multi-source and multi-load systems in the future. They cannot realize on-demand, precise and cascaded transmission of waste heat among multiple terminals, resulting in poor energy-saving benefits.

[0005] (3) Existing solutions often neglect transmission energy consumption. The energy consumption of water pumps required to regulate flow and pressure in complex pipe networks is considerable. Traditional solutions lack global optimization aimed at minimizing the energy consumption of the transmission and distribution system itself, and are prone to falling into the dilemma of "saving heat but not electricity". Summary of the Invention

[0006] This invention addresses the problems existing in the prior art by providing a venue energy-saving control system and method based on multi-source waste heat synergy and AI prediction, effectively solving the problems existing in the prior art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A venue energy-saving control system based on multi-source waste heat synergy and AI prediction includes: Plate heat exchangers, dynamic distribution networks, and a central controller are installed on the heat source side and the heat-consuming terminal side; a first plate heat exchanger is installed in parallel on the coolant outlet pipe of the central air conditioning condenser on the heat source side; a plate heat exchanger is integrated into the heat dissipation module of the lighting equipment on the heat source side; a plate heat exchanger is installed on the water supply or circulation pipe of the domestic hot water storage tank on the heat-consuming terminal side; a plate heat exchanger is installed at the front end of the manifold of the swimming pool water circulation system and the floor heating system on the heat-consuming terminal side; the secondary side pipes of the plate heat exchangers on the heat source side and the heat-consuming terminal side form a waste heat adaptive regulation circulation system under the control of the central controller through the dynamic distribution network; The central controller specifically performs the following processing steps: periodically acquiring monitoring data from the heat source side, the heat-consuming terminal side, the environment and context, and the dynamic transmission and distribution network; based on the acquired monitoring data, calculating the current instantaneous waste heat generation of each heat source according to the principle of heat balance; based on the acquired instantaneous waste heat generation of each heat source and its corresponding historical time series, obtaining the waste heat generation prediction sequence for several future control cycles of each heat source through a pre-constructed deep learning-based waste heat generation prediction model; based on the waste heat generation prediction sequence for several future control cycles of each heat source and the heat demand prediction sequence from the heat-consuming terminal side, optimizing and solving a pre-constructed objective function aimed at minimizing the total net energy consumption cost by dynamically coordinating the flow distribution and temperature setting of each cooling loop, to obtain the optimal control parameters.

[0008] Furthermore, the dynamic distribution network includes an inlet header and a return header. The inlet header is connected to the outlet of the secondary side pipeline of each plate heat exchanger on the heat source side and to the inlet of the secondary side pipeline of each plate heat exchanger on the heat-using terminal side. The return header is connected to the outlet of the secondary side pipeline of each plate heat exchanger on the heat-using terminal side and to the inlet of the secondary side pipeline of each plate heat exchanger on the heat source side. An electronic three-way valve is installed between the inlet header and the inlet of the secondary side pipeline of each plate heat exchanger on the heat-using terminal side.

[0009] Furthermore, the inlet of the electronic three-way valve is connected to the inlet header, the first outlet is connected to the inlet of the secondary side pipe of the plate heat exchanger, and the second outlet is connected to the return header.

[0010] Furthermore, the objective function that aims to minimize the total net energy cost aims to minimize the total operating energy consumption of the variable frequency pump over several future steps.

[0011] Furthermore, the objective function and its related constraints are specifically expressed as follows: in, , , , These are the power-frequency characteristic coefficients of the variable frequency pump. for The frequency of the variable frequency pump at any given time. These represent the opening degrees of the first through fourth electronic three-way valves, respectively. Its constraints include: Pump flow rate constraints: in, for The flow rate of the variable frequency pump at any given time. , , This refers to the flow-frequency characteristic coefficient of the variable frequency pump. for The frequency of the variable frequency pump at any given moment; Boundary condition constraints: in, for The opening ratio of the j-th electronic three-way valve at time j; Terminal heat equation constraints: in, For the density of water, The specific heat capacity of water, for The temperature of the mixed water in the inlet manifold at all times. for The predicted return water temperature is constantly measured using the heat terminal j. The pipeline transmission efficiency to the heat-using terminal j (between 0 and 1) represents the losses along the way from the inlet header to the inlet of the plate heat exchanger at the heat-using terminal. Let be the heat exchanger efficiency of the heat-using terminal j (between 0 and 1), which represents the heat exchange efficiency of the heat exchanger itself. Heat demand constraint: in, express The actual heat demand of terminal j at any given time. for The predicted heat demand at terminal j corresponds to the previously obtained ; Upper limit of energy for inlet header: in, To optimize the heat transfer efficiency from the central air conditioning heat source to the main water inlet pipe, pipe losses from the central air conditioning heat source outlet to the main pipe were taken into account. To optimize the heat transfer efficiency from the lighting heat source to the main water inlet pipe, pipe losses from the lighting heat source outlet to the main pipe were considered. and These represent the times of the central air conditioning and lighting heat sources, respectively. Predictable available heat; Bypass thermal shock constraint: in, Let be the bypass backflow influence coefficient for heat terminal j, which represents the degree of influence of the hot water flowing out of the j-th valve on the return water temperature of the heat source. This is the maximum allowable return water temperature rise for the heat source; Heat source return water temperature constraint: For the heat source AC, i.e., central air conditioning: For the heat source, light, i.e., illumination: in, and These are the proportional coefficients for the bypass flow from the heat terminal j to the heat source ac and light, respectively. and Other return flow factors affecting heat source AC and heat source Light, including system water replenishment and leakage, etc. and These are the secondary circulation pump flow rates for heat source AC and heat source Light, respectively. and These are the maximum allowable return water temperatures for heat source AC and heat source Light, respectively.

[0012] Furthermore, the monitoring data on the heat source side and the heat-using terminal side include: the condenser inlet water temperature, outlet water temperature, coolant flow rate and compressor power of the central air conditioning system; the power of each lighting circuit, the temperature of the plate heat exchanger and the coolant inlet water temperature; the return water temperature of the water storage tank, swimming pool and underfloor heating; historical data of domestic hot water flow rate; ambient temperature, tap water temperature, future activity schedule characteristics and predicted number of attendees.

[0013] The venue energy-saving control method based on multi-source waste heat synergy and AI prediction, which is based on the aforementioned venue energy-saving control system based on multi-source waste heat synergy and AI prediction, includes: Periodically acquire monitoring data from the heat source side, the heat consumption terminal side, the environment and context, and the dynamic transmission and distribution network; Based on the obtained monitoring data, the instantaneous waste heat generation of each heat source is calculated according to the principle of heat balance. Based on the instantaneous waste heat generation of each heat source and its corresponding historical time series, a waste heat generation prediction sequence for several future control cycles of each heat source is obtained through a pre-constructed waste heat generation prediction model based on deep learning. Based on the predicted waste heat generation sequence of each heat source for several future control cycles and the predicted heat demand sequence of the heat consumption terminal, the optimal control parameters are obtained by optimizing the pre-constructed objective function that aims to minimize the total net energy consumption cost by dynamically coordinating the flow distribution and temperature setting of each cooling loop.

[0014] An electronic device includes a memory, a processor, and a computer program stored in the memory and running on the memory. When the processor executes the program, it implements the above-mentioned venue energy-saving control method based on multi-source waste heat synergy and AI prediction.

[0015] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned venue energy-saving control method based on multi-source waste heat synergy and AI prediction.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: (1) The present invention provides a venue energy-saving control system and method based on multi-source waste heat synergy and AI prediction. The solution recovers waste heat such as condensation heat of central air conditioning and heat dissipation of lighting equipment in a coordinated manner. Through intelligently adjustable electronic three-way valves, variable frequency pumps and multi-channel heat exchanger groups, the solution can sense the changes in the intensity of each heat source and the demand of the terminal in real time, and automatically reconstruct the pipeline connection method, allocate the working fluid flow and adjust the heat exchange power, thereby effectively coping with the drastic fluctuations in the intensity of heat sources such as air conditioning and lighting in the venue, adapting to the differences in the heat use sequence and temperature quality of different terminals, ensuring that waste heat is delivered to the corresponding terminal in a timely and appropriate temperature, avoiding the impact of excessive heat on equipment heat dissipation, and preventing the decline in user experience due to insufficient heat. It breaks through the bottleneck of dynamic mismatch caused by the original fixed connection from the hardware level.

[0017] (2) The proposed solution configures plate heat exchangers on demand on the heat source side and the heat-using terminal side, and realizes the adaptive regulation and circulation of waste heat through a dynamic distribution network including electronic three-way valves. It can flexibly respond to the temporal fluctuation characteristics of the heat source intensity in the venue, match the differentiated needs of different heat-using terminals in terms of time sequence and temperature grade, avoid the abnormal operation of the main equipment caused by the imbalance of heat supply and demand, and greatly improve the waste heat recovery efficiency.

[0018] (3) The proposed scheme abandons the traditional simple threshold control or static scheduling scheme and realizes the collaborative prediction and dynamic precise control of the supply and demand behavior of multi-source and multi-load system. The scheme achieves accurate prediction of waste heat generation and heat demand in several future cycles by periodically collecting multi-dimensional monitoring data and combining deep learning models. At the same time, it achieves dynamic coordination of cooling loop flow distribution and temperature setting through objective function optimization, ensuring that waste heat is delivered on demand and accurately in stages among multiple terminals, which significantly improves energy-saving benefits compared with existing schemes.

[0019] (4) The proposed solution constructs an objective function with the goal of minimizing the total operating energy consumption of the variable frequency pump in the future, and incorporates multiple constraints such as pump flow relationship, boundary conditions, and terminal heat balance for optimization. While ensuring efficient recovery and utilization of waste heat, it minimizes the energy consumption of the transmission and distribution system itself, achieving the synergy of "heat saving" and "electricity saving", and further enhancing the comprehensive energy-saving value of the system. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below: Figure 1 This is a schematic diagram of the venue energy-saving control system based on multi-source waste heat synergy and AI prediction as described in an embodiment of the present invention; Figure 2 This is a schematic diagram of the dynamic transmission and distribution pipeline network structure in the venue energy-saving control system based on multi-source waste heat synergy and AI prediction, as described in an embodiment of the present invention.

[0021] Figure 3 This is a flowchart of the venue energy-saving control method based on multi-source waste heat synergy and AI prediction as described in the embodiments of the present invention.

[0022] Figure 4 This is an electronic device described in an embodiment of the present invention. Detailed Implementation

[0023] To better understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] Numerous specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways than those described herein, and therefore the invention is not limited to the specific embodiments disclosed in the following specification.

[0025] Example 1 The following is a detailed description of the venue energy-saving control system based on multi-source waste heat synergy and AI prediction in Embodiment 1, with reference to the accompanying drawings.

[0026] like Figure 1 As shown, a venue energy-saving control system based on multi-source waste heat synergy and AI prediction includes: Plate heat exchangers, dynamic distribution networks, and a central controller are installed on the heat source side and the heat-consuming terminal side; a first plate heat exchanger is installed in parallel on the coolant outlet pipe of the central air conditioning condenser on the heat source side; a plate heat exchanger is integrated into the heat dissipation module of the lighting equipment on the heat source side; a plate heat exchanger is installed on the water supply or circulation pipe of the domestic hot water storage tank on the heat-consuming terminal side; a plate heat exchanger is installed at the front end of the manifold of the swimming pool water circulation system and the floor heating system on the heat-consuming terminal side; the secondary side pipes of the plate heat exchangers on the heat source side and the heat-consuming terminal side form a waste heat adaptive regulation circulation system under the control of the central controller through the dynamic distribution network; The central controller specifically performs the following processing steps: periodically acquiring monitoring data from the heat source side, the heat-consuming terminal side, the environment and context, and the dynamic transmission and distribution network; based on the acquired monitoring data, calculating the current instantaneous waste heat generation of each heat source according to the principle of heat balance; based on the acquired instantaneous waste heat generation of each heat source and its corresponding historical time series, obtaining the waste heat generation prediction sequence for several future control cycles of each heat source through a pre-constructed deep learning-based waste heat generation prediction model; based on the waste heat generation prediction sequence for several future control cycles of each heat source and the heat demand prediction sequence from the heat-consuming terminal side, optimizing and solving a pre-constructed objective function aimed at minimizing the total net energy consumption cost by dynamically coordinating the flow distribution and temperature setting of each cooling loop, to obtain the optimal control parameters.

[0027] In specific implementation, such as Figure 2 As shown, the dynamic distribution network includes an inlet header and a return header. The inlet header is connected to the outlet of the secondary side pipeline of each plate heat exchanger on the heat source side and to the inlet of the secondary side pipeline of each plate heat exchanger on the heat-using terminal side. The return header is connected to the outlet of the secondary side pipeline of each plate heat exchanger on the heat-using terminal side and to the inlet of the secondary side pipeline of each plate heat exchanger on the heat source side. An electronic three-way valve is installed between the inlet header and the inlet of the secondary side pipeline of each plate heat exchanger on the heat-using terminal side.

[0028] In specific implementation, the monitoring data on the heat source side and the heat-using terminal side include: the condenser inlet water temperature, outlet water temperature, coolant flow rate and compressor power of the central air conditioning system; the power of each lighting circuit, the temperature of the plate heat exchanger and the coolant inlet water temperature; the return water temperature of the water storage tank, swimming pool and underfloor heating; historical data of domestic hot water flow rate; ambient temperature, tap water temperature, future activity schedule characteristics and predicted number of attendees.

[0029] In specific implementation, such as Figure 1 and Figure 2 As shown, the solution described in this embodiment first requires the following modifications to the existing equipment in the venue: (1) Modification of the heat source side: Central air conditioning condensing heat recovery: A plate heat exchanger (with coolant flowing through the primary side and an independent heat recovery circulating water circuit through the secondary side) is installed in parallel on the coolant outlet pipe of the central air conditioning condenser to efficiently extract condensing heat.

[0030] Lighting heat recovery: A plate heat exchanger is integrated into the heat dissipation module of the high-power LED lamp to conduct the heat dissipated by the lamp to the coolant circuit built into the plate heat exchanger and collect it.

[0031] (2) Modification of the heat terminal side: Install a plate heat exchanger on the water supply / circulation pipeline of the domestic hot water storage tank.

[0032] Plate heat exchangers are installed at the front end of the manifolds of the swimming pool water circulation system and the floor heating system, respectively.

[0033] (3) Construction of dynamic transmission and distribution network: For the secondary piping of the newly added heat exchangers on the heat source side and the heat consumption terminal side, a network consisting of several intelligent three-way regulating valves and variable frequency circulating water pumps (such as...) is used. Figure 2 Connect the components (as shown) to form a waste heat adaptive control cycle system.

[0034] (4) Sensor arrangement In one or more embodiments, the heat source-side data that needs to be collected in the scheme includes: Central air conditioning: Condenser inlet water temperature Outlet water temperature Coolant flow rate (i.e., circulation flow rate), compressor power ; Lighting: Power of each lighting circuit Plate heat exchanger collector plate temperature Coolant inlet temperature ; In one or more embodiments, the data that needs to be collected at the heat-using terminal side includes: Water storage tank, swimming pool, and underfloor heating return water temperature Where j takes values ​​of {1, 2, 3}, representing the historical data of domestic hot water flow rates from the water storage tank, swimming pool, and underfloor heating return water, respectively. ; Environmental and future event schedule characteristics, including ambient temperature Tap water temperature Characteristics of future event schedules (For example: no event = 0, event = 1) and predicted starting attendance. wait.

[0035] The above data can be obtained by setting the sensor at the preset location. The specific setting point can be designed according to actual needs, as long as it can obtain the above data. It will not be elaborated here.

[0036] In one or more embodiments, the primary side piping of the plate heat exchanger added to the central air conditioning unit on the heat source side needs to be connected to the medium circuit of the condenser. The primary side of the plate heat exchanger added to the lighting unit is usually concealed, without a pipeline for the flowing working medium. Instead, heat is obtained directly from the source through the secondary side piping. For plate heat exchangers on the heat-using terminal side, the primary side piping needs to be connected to the corresponding medium flow pipeline. For example, the primary side piping of the plate heat exchanger at the domestic hot water storage tank needs to be connected to the pipeline before ordinary tap water enters the storage tank.

[0037] In practice, the supply capacity and heat demand forecasts are processed as follows: (1) Central air conditioning condensing heat prediction: Calculation of instantaneous condensation heat dissipation based on the principle of thermal balance: in, Let t be the instantaneous condensation heat dissipation of the central air conditioning condenser. Let be the coolant flow rate at time t. The isobaric specific heat capacity of water is taken as 4.18 kJ / m³. , Let t be the condenser outlet water temperature. Let t be the condenser inlet water temperature at time t.

[0038] Based on the instantaneous condensation heat dissipation of the central air conditioning condenser, a pre-trained LSTM network model (i.e., Long Short-Time Memory network model) is used to predict the recoverable heat sequence for the next N control cycles (e.g., N=3, each cycle is 20 minutes): in, exist Predictable recoverable heat power at any given time The value can be {1, 2, 3}; ( ) represents the parameter. Long Short-Term Memory Network Model; Historical sequence data; Characteristics of future event schedules.

[0039] (2) Lighting heat dissipation prediction: First, calculate the single-loop recovery efficiency. The temperature difference A linear function, that is: ; The heat recovered by this circuit is: Total calories: in, For time t, the i-th path instantly recovers heat; Let be the power input of the i-th lighting path at time t. The total recovered heat of all lighting circuits at time t.

[0040] The recoverable heat sequence for the next N control cycles (e.g., N=3, each cycle 20 minutes) is predicted using a gated cycle unit, as shown below: in, ( ) represents a gated loop unit. For historical observation sequence data related to lighting, To predict the number of attendees at the start, Characteristics of future event schedules.

[0041] (3) Domestic hot water terminal demand forecast: Net heat demand power for its future N control cycles It consists of three items: in, The density of water is 1000 kg / m³. 3 , For the water tank volume, For the predicted water tank temperature, Set the temperature for domestic hot water. , For the allowable heating time constant, This refers to the predicted future water quality flow rate (kg / s) obtained through a time series forecasting model (e.g., the Prophet model). This refers to the temperature of tap water. Let be the heat transfer coefficient of the water tank. The outer surface area of ​​the water tank. The value of is {1,2,3}.

[0042] Based on the obtained N control cycles Obtain the net heat demand power of domestic hot water terminals .

[0043] Understandably, the process of forecasting the terminal demand for swimming pools and floor heating is basically the same as that for domestic hot water. The main idea is to obtain the net heat demand power of the swimming pool based on a time series forecasting model combined with the above formula. Net heat demand power of floor heating terminals Therefore, it will not be elaborated further here.

[0044] In specific implementation, the solution for the optimal control command is as follows: Based on all the obtained forecast data: and ; Construct and solve the following rolling time-domain optimization problem: Optimization objective: Minimize the total operating energy consumption of the variable frequency pump over the next N steps. in, , , , These are the power-frequency characteristic coefficients of the variable frequency pump. for The frequency of the variable frequency pump at any given time. These represent the opening degrees of the first through fourth electronic three-way valves, respectively. Constraints: (1) Pump flow rate relationship: in, for The flow rate of the variable frequency pump at any given time. , , This is the flow-frequency characteristic coefficient of the variable frequency pump.

[0045] (2) Boundary conditions: in, for The opening ratio of the j-th electronic three-way valve at time j.

[0046] (3) Terminal heat equation ( ): in, For the density of water, The specific heat capacity of water, for The temperature of the mixed water in the inlet manifold at all times. for The predicted return water temperature is constantly measured using the heat terminal j. The pipeline transmission efficiency to the heat-using terminal j (between 0 and 1) represents the losses along the way from the inlet header to the inlet of the plate heat exchanger at the heat-using terminal. Let be the heat exchanger efficiency of the heat-using terminal j (between 0 and 1), which represents the heat exchange efficiency of the heat exchanger itself.

[0047] (4) Heat demand constraint ( ): in, express The actual heat demand of terminal j at any given time. for The predicted heat demand at terminal j corresponds to the previously obtained .

[0048] (5) Maximum energy capacity of the inlet header: in, To optimize the heat transfer efficiency from the central air conditioning heat source to the main water inlet pipe, pipe losses from the central air conditioning heat source outlet to the main pipe were taken into account. To optimize the heat transfer efficiency from the lighting heat source to the main water inlet pipe, pipe losses from the lighting heat source outlet to the main pipe were considered. and These represent the times of the central air conditioning and lighting heat sources, respectively. The predicted available heat.

[0049] (6) Bypass thermal shock constraint: in, Let be the bypass backflow influence coefficient for heat terminal j, which represents the degree of influence of the hot water flowing out of the j-th valve on the return water temperature of the heat source. This is the maximum allowable return water temperature rise for the heat source.

[0050] (7) Heat source return water temperature constraint: For the heat source AC, i.e., central air conditioning: For the heat source, light, i.e., illumination: in, and These are the proportional coefficients for the bypass flow from the heat terminal j to the heat source ac and light, respectively. and Other return flow factors affecting heat source AC and heat source Light, including system water replenishment and leakage, etc. and These are the secondary circulation pump flow rates for heat source AC and heat source Light, respectively. and These are the maximum allowable return water temperatures for heat source AC and heat source Light, respectively.

[0051] Finally, the objective function is solved using the IPOPT solver under the above constraints to obtain the optimal control commands for the next N (N=3) steps, including the main pump frequency and the opening degrees of the four electronic three-way valves. , ={1,2,3}, and set the first step instruction (i.e. =1) Issued to the on-site implementing agency.

[0052] In its implementation, the scheme also includes a closed-loop update process, as detailed below: (1) Command issuance and execution: The opening degree of the four electronic three-way valves and the frequency of the variable frequency pump in the first control cycle obtained by optimization are issued to the field actuator.

[0053] (2) Rolling optimization: Every control cycle (e.g., 20 minutes), the above process is repeated starting from the latest measured state to achieve model predictive control.

[0054] (3) Online model learning: Periodically use actual running data to retrain prediction models such as LSTM, GRU, and Prophet.

[0055] Example 2 In one or more embodiments, such as Figure 3 As shown, the solution described in this embodiment also provides a venue energy-saving control method based on multi-source waste heat synergy and AI prediction. This method, based on the aforementioned venue energy-saving control system based on multi-source waste heat synergy and AI prediction, includes: Periodically acquire monitoring data from the heat source side, the heat consumption terminal side, the environment and context, and the dynamic transmission and distribution network; Based on the obtained monitoring data, the instantaneous waste heat generation of each heat source is calculated according to the principle of heat balance. Based on the instantaneous waste heat generation of each heat source and its corresponding historical time series, a waste heat generation prediction sequence for several future control cycles of each heat source is obtained through a pre-constructed waste heat generation prediction model based on deep learning. Based on the predicted waste heat generation sequence of each heat source for several future control cycles and the predicted heat demand sequence of the heat consumption terminal, the optimal control parameters are obtained by optimizing the pre-constructed objective function that aims to minimize the total net energy consumption cost by dynamically coordinating the flow distribution and temperature setting of each cooling loop.

[0056] Example 3 like Figure 4 As shown, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on it. When the processor executes the program, it implements the above-mentioned venue energy-saving control method based on multi-source waste heat synergy and AI prediction.

[0057] Example 4 This embodiment provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned venue energy-saving control method based on multi-source waste heat synergy and AI prediction.

[0058] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A venue energy-saving control system based on multi-source waste heat synergy and AI prediction, characterized in that, include: Plate heat exchangers, dynamic distribution networks, and a central controller are installed on the heat source side and the heat-consuming terminal side; a first plate heat exchanger is installed in parallel on the coolant outlet pipe of the central air conditioning condenser on the heat source side; a plate heat exchanger is integrated into the heat dissipation module of the lighting equipment on the heat source side; a plate heat exchanger is installed on the water supply or circulation pipe of the domestic hot water storage tank on the heat-consuming terminal side; a plate heat exchanger is installed at the front end of the manifold of the swimming pool water circulation system and the floor heating system on the heat-consuming terminal side; the secondary side pipes of the plate heat exchangers on the heat source side and the heat-consuming terminal side form a waste heat adaptive regulation circulation system under the control of the central controller through the dynamic distribution network; The central controller specifically performs the following processing steps: periodically acquiring monitoring data from the heat source side, the heat-consuming terminal side, the environment and context, and the dynamic transmission and distribution network; based on the acquired monitoring data, calculating the current instantaneous waste heat generation of each heat source according to the principle of heat balance; based on the acquired instantaneous waste heat generation of each heat source and its corresponding historical time series, obtaining the waste heat generation prediction sequence for several future control cycles of each heat source through a pre-constructed deep learning-based waste heat generation prediction model; based on the waste heat generation prediction sequence for several future control cycles of each heat source and the heat demand prediction sequence from the heat-consuming terminal side, optimizing and solving a pre-constructed objective function aimed at minimizing the total net energy consumption cost by dynamically coordinating the flow distribution and temperature setting of each cooling loop, to obtain the optimal control parameters.

2. The venue energy-saving control system based on multi-source waste heat synergy and AI prediction as described in claim 1, characterized in that, The dynamic distribution network includes an inlet header and a return header. The inlet header is connected to the outlet of the secondary side pipeline of each plate heat exchanger on the heat source side and to the inlet of the secondary side pipeline of each plate heat exchanger on the heat-using terminal side. The return header is connected to the outlet of the secondary side pipeline of each plate heat exchanger on the heat-using terminal side and to the inlet of the secondary side pipeline of each plate heat exchanger on the heat source side. An electronic three-way valve is installed between the inlet header and the inlet of the secondary side pipeline of each plate heat exchanger on the heat-using terminal side.

3. The venue energy-saving control system based on multi-source waste heat synergy and AI prediction as described in claim 2, characterized in that, The inlet of the electronic three-way valve is connected to the inlet header, the first outlet is connected to the inlet of the secondary side pipe of the plate heat exchanger, and the second outlet is connected to the return header.

4. The venue energy-saving control system based on multi-source waste heat synergy and AI prediction as described in claim 1, characterized in that, The objective function, which aims to minimize the total net energy cost, aims to minimize the total operating energy consumption of the variable frequency pump over several future steps.

5. The venue energy-saving control system based on multi-source waste heat synergy and AI prediction as described in claim 4, characterized in that, The objective function and its related constraints are specifically represented as follows: in, , , , These are the power-frequency characteristic coefficients of the variable frequency pump. for The frequency of the variable frequency pump at any given time. These represent the opening degrees of the 1st to 4th electronic three-way valves, respectively. Its constraints include: Pump flow rate constraints: in, for The flow rate of the variable frequency pump at any given time. , , This refers to the flow-frequency characteristic coefficient of the variable frequency pump. for The frequency of the variable frequency pump at any given moment; Boundary condition constraints: in, for The opening ratio of the j-th electronic three-way valve at time j; Terminal heat equation constraints: in, For the density of water, The specific heat capacity of water, for The temperature of the mixed water in the inlet manifold at all times. for The predicted return water temperature is constantly measured using the heat terminal j. The pipeline transmission efficiency to the heat-using terminal j is given by the loss along the way from the inlet header to the inlet of the plate heat exchanger at the heat-using terminal. Let be the heat exchanger efficiency of the heat-using terminal j, which represents the heat exchange efficiency of the heat exchanger itself. Heat demand constraint: in, express The actual heat demand of terminal j at any given time. for The predicted heat demand at terminal j corresponds to the previously obtained ; Upper limit of energy for inlet header: in, To optimize the heat transfer efficiency from the central air conditioning heat source to the main water inlet pipe, pipe losses from the central air conditioning heat source outlet to the main pipe were taken into account. To optimize the heat transfer efficiency from the lighting heat source to the main water inlet pipe, pipe losses from the lighting heat source outlet to the main pipe were considered. and These represent the times of the central air conditioning and lighting heat sources, respectively. Predictable available heat; Bypass thermal shock constraint: in, Let be the bypass backflow influence coefficient for heat terminal j, which represents the degree of influence of the hot water flowing out of the j-th valve on the return water temperature of the heat source. This is the maximum allowable return water temperature rise for the heat source; Heat source return water temperature constraint: For the heat source AC, i.e., central air conditioning: For the heat source, light, i.e., illumination: in, and These are the proportional coefficients for the bypass flow from the heat terminal j to the heat source ac and light, respectively. and Other return flow factors affecting heat source AC and heat source Light, including system water replenishment and leakage, etc. and These are the secondary circulation pump flow rates for heat source AC and heat source Light, respectively. and These are the maximum allowable return water temperatures for heat source AC and heat source Light, respectively.

6. The venue energy-saving control system based on multi-source waste heat synergy and AI prediction as described in claim 1, characterized in that, The monitoring data on the heat source side and the heat consumption terminal side include: the condenser inlet water temperature, outlet water temperature, coolant flow rate and compressor power of the central air conditioning system; the power of each lighting circuit, the temperature of the plate heat exchanger and the coolant inlet water temperature; the return water temperature of the water storage tank, swimming pool and underfloor heating; historical data of domestic hot water flow rate; ambient temperature, tap water temperature, future activity schedule characteristics and predicted number of attendees.

7. A venue energy-saving control method based on multi-source waste heat synergy and AI prediction, which is based on the venue energy-saving control system based on multi-source waste heat synergy and AI prediction as described in any one of claims 1-6, comprising: Periodically acquire monitoring data from the heat source side, the heat consumption terminal side, the environment and context, and the dynamic transmission and distribution network; Based on the obtained monitoring data, the instantaneous waste heat generation of each heat source is calculated according to the principle of heat balance. Based on the instantaneous waste heat generation of each heat source and its corresponding historical time series, a waste heat generation prediction sequence for several future control cycles of each heat source is obtained through a pre-constructed waste heat generation prediction model based on deep learning. Based on the predicted waste heat generation sequence of each heat source for several future control cycles and the predicted heat demand sequence of the heat consumption terminal, the optimal control parameters are obtained by optimizing the pre-constructed objective function that aims to minimize the total net energy consumption cost by dynamically coordinating the flow distribution and temperature setting of each cooling loop.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running thereon, wherein the processor executes the program to implement the venue energy-saving control method based on multi-source waste heat synergy and AI prediction as described in claim 7.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the venue energy-saving control method based on multi-source waste heat synergy and AI prediction as described in claim 7.

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