Central air conditioner energy consumption optimization control method and system, electronic equipment and storage medium

Abstract

The invention relates to a central air conditioner energy consumption optimization control method and system, electronic equipment and a storage medium, and the method comprises the steps that characteristic parameters of load calculation are obtained, and according to the characteristic parameters, the cooling load requirements of a central air conditioner under different working conditions are calculated through a dynamic load calculation model; acquiring real-time operation data of the central air conditioner; establishing a digital simulation model of the central air conditioner based on the cooling load demand and the operation data, and calculating the energy consumption distribution of each device of the central air conditioner under different working conditions through the digital simulation model; with the overall energy efficiency optimization of the central air conditioner as the target, the optimal operation parameter combination of the central air conditioner is obtained through an intelligent optimization algorithm; and synchronously displaying the operation data of the central air conditioner, the energy consumption distribution of each device and the optimal operation parameter combination through a visual interface, and executing an optimized operation strategy according to a user instruction based on the optimal operation parameter combination. According to the method, the accuracy of energy consumption calculation is improved, and the overall energy consumption of the central air conditioner is reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of energy consumption design of central air-conditioning units, and in particular to a central air-conditioning unit energy consumption optimization control method, system, electronic equipment and storage medium. Background Art

[0002] Currently, domestic central air conditioning systems lack refined control throughout their lifecycle management. In actual operation, the equipment often operates at low efficiency levels. This can lead to system malfunctions and even difficulty maintaining indoor comfort over time. Traditional modeling and simulation methods, often using software like EnergyPlus to build central air conditioning energy consumption models, lack intuitive output, require specialized user expertise, and lack model stability.

[0003] Among existing energy-saving control solutions for central air conditioners, model predictive control (MPC) approaches predict future loads, plan air conditioning equipment operating strategies in advance, and adjust system parameters in real time to achieve energy savings. However, MPC approaches rely heavily on accurate modeling of the air conditioning system, which has complex nonlinear characteristics and coupled relationships between devices. Traditional MPC struggles to accurately capture these complex relationships, resulting in suboptimal control performance. Multi-agent deep reinforcement learning (MADRL) approaches have also been applied to dynamic optimal control of central air conditioners. Each agent controls a different device, achieving global optimization through collaborative learning and policy adjustment. MADRL can adapt to complex nonlinear systems, but it requires a large amount of training data and computing resources, making the training process complex and time-consuming. Training takes a long time in large-scale, multivariable environments, making timely deployment difficult. Furthermore, the complex interactions between agents in multi-agent environments can easily lead to training instability or poor convergence, potentially resulting in inconsistent optimization strategies or poor performance.

[0004] To address the problem of poor adaptability of the MPC model, current improvement solutions include introducing an adaptive adjustment mechanism (improving model adaptability through online parameter adjustment and modeling error compensation) or simplifying the control model to reduce computational complexity. However, these measures only work under specific conditions and cannot fundamentally solve the problems of model inaccuracy and poor real-time performance. In terms of MADRL, techniques such as experience replay and dynamic exploration and utilization balance are often used to improve training efficiency and stability, and a centralized learning and distributed execution strategy framework is used to enhance global coordination. However, the above improvements are limited by the complexity of intelligent agent coordination and computing resource bottlenecks, and still cannot fully meet the needs of real-time applications. Therefore, there is an urgent need for an energy-saving optimization system for central air-conditioning units based on load forecasting and real-time optimization to solve the above problems. Summary of the Invention

[0005] In order to solve the problems of insufficient accuracy, inability to dynamically adjust, and lack of overall optimization means in existing central air-conditioning unit energy consumption design methods, the present disclosure proposes a central air-conditioning energy consumption optimization control method to solve the above problems.

[0006] According to one aspect of the present disclosure, a central air-conditioning energy consumption optimization control method is provided, comprising:

[0007] S10, obtaining characteristic parameters for load calculation, and calculating the cooling load demand of the central air conditioner under different working conditions using a dynamic load calculation model based on the characteristic parameters, wherein the characteristic parameters include building characteristic parameters, occupant distribution data, and environmental parameters;

[0008] S20, obtaining real-time central air conditioning operation data, the operation data including indoor and outdoor temperature, humidity, equipment operation status and energy consumption parameters;

[0009] S30, establishing a digital simulation model of the central air conditioner based on cooling load demands under different operating conditions and operating data of the central air conditioner, and calculating energy consumption distribution of each device of the central air conditioner under different operating conditions using the digital simulation model;

[0010] S40, based on the energy consumption distribution of each device under different working conditions, with the goal of optimizing the overall energy efficiency of the central air conditioner, obtain the optimal operating parameter combination of the central air conditioner through an intelligent optimization algorithm;

[0011] S50. Synchronously display the operating data of the central air conditioner, the energy consumption distribution of each device, and the optimal operating parameter combination through a visual interface, and execute an optimized operating strategy based on the optimal operating parameter combination according to user instructions.

[0012] Preferably, the cooling load demand of the central air conditioner under different working conditions is calculated using a dynamic load calculation model, including:

[0013] Predicting a change trend of base load based on historical load data and environmental parameters in the characteristic parameters;

[0014] Combined with the real-time monitored personnel distribution data and environmental parameters, the changing trend of the base load is dynamically optimized and adjusted through a dynamic correction algorithm, thereby obtaining the cooling load demand of the central air-conditioning system under different working conditions.

[0015] Preferably, a digital simulation model of the central air conditioner is established based on the cooling load demand under different working conditions and the operating data of the central air conditioner, including:

[0016] Based on the characteristic relationship between the energy efficiency ratio and load rate of the refrigeration unit, a performance model of the refrigeration unit is established;

[0017] According to the correlation characteristics between flow rate and head, flow rate and power, and flow rate and efficiency, a hydraulic model of the water pump is established;

[0018] Based on the principle of heat and mass transfer, a thermal exchange model of the cooling tower is established.

[0019] Preferably, the energy consumption distribution of each device of the central air conditioner under different working conditions is calculated by a digital simulation model, including:

[0020] Calculate the energy consumption distribution of the refrigeration unit under different operating conditions through the performance model of the refrigeration unit;

[0021] Through the hydraulic model of the water pump, simulate the energy consumption distribution of the water pump under different working conditions;

[0022] The energy consumption distribution of the cooling tower under different working conditions is analyzed through the thermal exchange model of the cooling tower.

[0023] Preferably, the performance model of the refrigeration unit is expressed using a polynomial fitting method as follows:

[0024] COP(PLR)=a0+a1·PLR+a2·PLR 2 +a3·PLR 3 +a4·PLR 4 ,

[0025] Where a0, a1, a2, a3, and a4 are fitting coefficients, and PLR is the partial load rate.

[0026] Preferably, with the goal of optimizing the overall energy efficiency of the central air conditioner, the optimal operating parameter combination of the central air conditioner is obtained through an intelligent optimization algorithm, including:

[0027] Optimize and calculate the chiller outlet water temperature setpoint based on current load demand and equipment performance characteristics;

[0028] Dynamically adjust the operating frequency of the chilled water pump and cooling water pump according to the system hydraulic characteristics and flow requirements;

[0029] Combined with the ambient wet-bulb temperature and cooling tower performance curve, the operation mode and speed parameters of the cooling tower fan are optimized and determined;

[0030] According to the preset load interval strategy, select the chiller operating parameter combination with the best overall energy efficiency of the central air conditioning.

[0031] Preferably, based on the optimal operating parameter combination, the optimized operating strategy is executed according to the user's instructions, including:

[0032] Transmit the chiller outlet water temperature setpoint, the operating frequency of the chilled water pump and cooling water pump, and the operating mode and speed parameters of the cooling tower fan to the corresponding equipment controller;

[0033] The chiller start-stop combination plan is automatically or manually confirmed according to the load interval strategy, and the optimized operation strategy is executed according to the chiller start-stop combination plan.

[0034] According to one aspect of the present disclosure, a central air-conditioning energy consumption optimization control system is provided, comprising:

[0035] A cooling load demand calculation module obtains characteristic parameters for load calculation and calculates the cooling load demand of the central air conditioner under different working conditions using a dynamic load calculation model based on the characteristic parameters, which include building characteristic parameters, occupant distribution data, and environmental parameters.

[0036] The central air-conditioning operation data acquisition module acquires real-time central air-conditioning operation data, including indoor and outdoor temperature, humidity, equipment operation status and energy consumption parameters;

[0037] The energy consumption distribution calculation module of each device establishes a digital simulation model of the central air conditioner based on the cooling load demand under different working conditions and the operating data of the central air conditioner. The energy consumption distribution of each device of the central air conditioner under different working conditions is calculated by the digital simulation model;

[0038] The operating parameter optimization module uses an intelligent optimization algorithm to obtain the optimal operating parameter combination for the central air conditioner based on the energy consumption distribution of each device under different working conditions and the goal of optimizing the overall energy efficiency of the central air conditioner.

[0039] The operation strategy optimization execution module synchronously displays the operation data of the central air conditioner, the energy consumption distribution of each device and the optimal operation parameter combination through a visual interface, and executes the optimized operation strategy according to user instructions based on the optimal operation parameter combination.

[0040] According to one aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the above-mentioned central air-conditioning energy consumption optimization control method.

[0041] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the central air-conditioning energy consumption optimization control method described above is implemented.

[0042] Compared with the prior art, the beneficial effects of the present disclosure are:

[0043] 1) This disclosure uses precise load calculation and detailed equipment models to accurately predict the energy consumption of central air-conditioning systems under various load conditions, thereby reducing energy waste caused by unreasonable design.

[0044] 2) The present disclosure can automatically optimize the operating parameters of the air-conditioning equipment according to real-time environmental parameters and load changes, respond to changes in working conditions in real time, and avoid the problem of delayed response of traditional control schemes.

[0045] 3) The present disclosure displays simulation and optimization results through a visual interface, making it convenient for operators to monitor system status and adjust operations according to suggestions, thus lowering the threshold for use.

[0046] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0047] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0049] Figure 1 Shows a flow chart of a central air-conditioning energy consumption optimization control method;

[0050] Figure 2 A flow chart showing a method for designing energy consumption of a central air-conditioning unit in an embodiment of the present disclosure is shown;

[0051] Figure 3 A load forecast curve diagram of the central air conditioner in an embodiment of the present disclosure is shown;

[0052] Figure 4 A COP curve diagram of the partial load performance of the refrigeration unit in an embodiment of the present disclosure is shown;

[0053] Figure 5 The energy consumption optimization flow chart of the central air conditioner in the embodiment of the present disclosure is shown;

[0054] Figure 6 A schematic diagram of a staged load operation strategy for a chiller in an embodiment of the present disclosure is shown;

[0055] Figure 7 A graph showing the strategy learning process in an embodiment of the present disclosure is shown;

[0056] Figure 8 A performance characteristic curve diagram of a chilled water pump or a cooling water pump in an embodiment of the present disclosure is shown;

[0057] Figure 9 shows a cooling tower performance graph in an embodiment of the present disclosure;

[0058] Figure 10A schematic diagram showing the system interface and energy consumption analysis results in an embodiment of the present disclosure is shown;

[0059] Figure 11 A schematic diagram of a central air-conditioning energy consumption optimization control system in an example of the present disclosure is shown. DETAILED DESCRIPTION

[0060] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0061] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0062] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0063] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0064] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments 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 part of the embodiments of the present invention, not all 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.

[0065] Example 1

[0066] Based on the above ideas, the present invention proposes a central air-conditioning energy consumption optimization control method. Figure 1 A flow chart showing a method for optimizing the control of central air conditioning energy consumption is shown. The method comprises:

[0067] S10, obtaining characteristic parameters for load calculation, and calculating the cooling load demand of the central air conditioner under different working conditions using a dynamic load calculation model based on the characteristic parameters, wherein the characteristic parameters include building characteristic parameters, occupant distribution data, and environmental parameters;

[0068] S20, obtaining real-time central air conditioning operation data, the operation data including indoor and outdoor temperature, humidity, equipment operation status and energy consumption parameters;

[0069] S30, establishing a digital simulation model of the central air conditioner based on cooling load demands under different operating conditions and operating data of the central air conditioner, and calculating energy consumption distribution of each device of the central air conditioner under different operating conditions using the digital simulation model;

[0070] S40, based on the energy consumption distribution of each device under different working conditions, with the goal of optimizing the overall energy efficiency of the central air conditioner, obtain the optimal operating parameter combination of the central air conditioner through an intelligent optimization algorithm;

[0071] S50. Synchronously display the operating data of the central air conditioner, the energy consumption distribution of each device, and the optimal operating parameter combination through a visual interface, and execute an optimized operating strategy based on the optimal operating parameter combination according to user instructions.

[0072] The flow chart of the energy consumption design method of central air-conditioning unit is as follows Figure 2 As shown, it includes: load calculation, data collection, energy consumption simulation, optimization adjustment, result output and human-computer interaction. The specific steps are as follows:

[0073] S10. Obtain characteristic parameters for load calculation, and calculate the cooling load demand of the central air conditioner under different working conditions using a dynamic load calculation model based on the characteristic parameters, wherein the characteristic parameters include building characteristic parameters, occupant distribution data, and environmental parameters.

[0074] The cooling load demand of the central air-conditioning system under different working conditions is calculated by a dynamic load calculation model, including: predicting the changing trend of the basic load based on the historical load data and environmental parameters in the characteristic parameters; dynamically optimizing and adjusting the changing trend of the basic load through a dynamic correction algorithm in combination with the real-time monitored personnel distribution data and environmental parameters, thereby obtaining the cooling load demand of the central air-conditioning system under different working conditions.

[0075] In the embodiment of the present disclosure, the cooling load demand of the central air conditioner at the current moment or in the forecast period is calculated based on factors such as the building's usage function, occupancy density, and equipment heat dissipation. The characteristic parameters and demand parameters of the building itself are obtained, for example, the building area, floor height, number of occupants, equipment power, and outdoor weather conditions are input, and the cooling load of the building at different times is obtained through heat balance calculation. For future load prediction, historical data and weather forecasts can be combined to predict the load curve. Figure 3 The following figure shows the cooling load forecast result curve of the central air conditioner during a one-month operation cycle. The horizontal axis is time (hours) and the vertical axis is cooling load (e.g., RT or kW). The curve reflects the load calculation implementation of the present disclosure's ability to predict future load changes.

[0076] S20: Acquire real-time central air-conditioning operation data, including indoor and outdoor temperature, humidity, equipment operation status, and energy consumption parameters.

[0077] In this embodiment, a sensor network installed throughout the central air conditioning system collects real-time operating status and environmental parameters of each unit. Data such as indoor and outdoor temperature and humidity, supply and return air temperatures, water pump flow, valve opening, and refrigeration unit start and stop status are continuously monitored and recorded. This operating data is uploaded to a central controller via a communications network and serves as real-time input for simulation and optimization.

[0078] S30. Based on the cooling load demand under different working conditions and the operating data of the central air conditioner, a digital simulation model of the central air conditioner is established, and the energy consumption distribution of each device of the central air conditioner under different working conditions is calculated by the digital simulation model.

[0079] Based on the cooling load demand under different working conditions and the operating data of the central air-conditioning, a digital simulation model of the central air-conditioning is established, including: establishing a performance model of the refrigeration unit based on the characteristic relationship between the energy efficiency ratio and load rate of the refrigeration unit; establishing a hydraulic model of the water pump based on the correlation characteristics of flow rate and head, flow rate and power, and flow rate and efficiency; and establishing a thermal exchange model of the cooling tower based on the principle of heat and mass transfer.

[0080] In this example, cooling load requirements and central air conditioning (CA) operating data under different operating conditions are input into a digital simulation model to calculate various energy consumption indicators for the central air conditioner under these conditions. The simulation model calculates the real-time COP and power consumption of the refrigeration unit, the pump head and power, and the cooling tower fan power, and summarizes the total power consumption and real-time COP of the central air conditioner.

[0081] The energy consumption distribution of each device in the central air-conditioning under different working conditions is calculated through a digital simulation model, including: calculating the energy consumption distribution of the refrigeration unit under different operating conditions through the performance model of the refrigeration unit; simulating the energy consumption distribution of the water pump under different working conditions through the hydraulic model of the water pump; and analyzing the energy consumption distribution of the cooling tower under different working conditions through the heat exchange model of the cooling tower.

[0082] The performance model of the refrigeration unit is expressed using the polynomial fitting method as follows:

[0083] COP(PLR)=a0+a1·PLR+a2·PLR 2 +a3·PLR 3+a4·PLR 4 ,

[0084] Among them, a0, a1, a2, a3 and a4 are fitting coefficients, and PLR is the partial load rate. The partial load performance COP curve of the refrigeration unit is as follows: Figure 4 As shown. Among them: X-axis (horizontal axis): represents PLR (partial load ratio), which reflects the operating status of the equipment under different load conditions. Y-axis (vertical axis): represents CCOP (partial load coefficient of performance), which measures the energy efficiency of the equipment under partial load. Z-axis (vertical axis): represents cooling water temperature, which shows the change of cooling water temperature with PLR and CCOP. The curve fluctuates in three-dimensional space, showing multiple peaks and valleys. This shows that there is a complex nonlinear relationship between PLR and CCOP, and the cooling water temperature is also affected by these variables. The fluctuation of the curve may reflect the performance changes of the equipment under different load conditions. For example, the energy efficiency of the equipment is higher (CCOP is higher) under certain loads, and the energy efficiency is lower under other loads.

[0085] Similarly, the head-flow curve, power-flow curve, and efficiency-flow curve of the chilled water pump and cooling water pump can also be expressed by polynomial fitting as follows:

[0086] H pump (v) = c0 + c1v + c2v 2 +c3v 3 +c4v 4 ,

[0087] P pump (v) = b0 + b1v + b2v 2 +b3v 3 +b4v 4 ,

[0088] μ pump (v) = d0 + d1v + d2v 2 +d3v 3 +d4v 4 ,

[0089] Where v is the pump flow rate, and the fitting coefficients for different pumps (c0-c4, b0-b4, and d0-d4) can be determined based on their respective performance curves. Using this fitting model, the simulation model can quickly calculate the power consumption and efficiency of each chiller unit and pump under given operating conditions.

[0090] For the cooling tower, the simulation model uses the Merkel number method to establish a thermal performance model. The implementation begins by importing the air's dry-bulb temperature, wet-bulb temperature, and atmospheric pressure to determine the air inlet and outlet moisture content and enthalpy. Simultaneously, parameters such as the cooling water inlet temperature, flow rate, water density, and specific heat capacity are imported to calculate the difference in enthalpy between the cooling water inlet and outlet. Based on the principle of conservation of heat, the heat released by the cooling water side is equal to the heat absorbed by the air side. Numerical iteration (such as the fsolve algorithm for solving nonlinear equations) is used to calculate unknown parameters such as the air outlet temperature, thereby determining the temperature distribution of the air and water along the height of the cooling tower. The enthalpy distribution of the water and air at different temperatures is then calculated and integrated to obtain the Merkel number, a cooling tower performance evaluation metric. Based on the calculated Merkel number and the rated performance parameters of the cooling tower, simulation can estimate the number of cooling tower fans that need to be activated and their power consumption under the current environmental and load conditions.

[0091] By leveraging the detailed data provided by the digital simulation model, this embodiment can not only optimize unit startup and shutdown, but also further optimize the detailed parameter settings for each device. For example, based on the cooling tower Merkel number and the current outdoor wet-bulb temperature obtained through simulation, the optimal number of cooling tower fans to be activated and their speed can be determined to ensure that the cooling water temperature meets the condenser's requirements while minimizing fan power consumption. For another example, based on the simulated power curve of the chilled water pump, the variable frequency speed of the pump can be adjusted to operate in the high-efficiency zone. All of these optimization measures combined improve the real-time COP of the central air-conditioning system and avoid unnecessary energy waste.

[0092] After the energy consumption data of each device is calculated through digital simulation, the energy consumption optimization will optimize the operating parameters of the central air conditioner accordingly.

[0093] S40. Based on the energy consumption distribution of each device under different working conditions, with the goal of optimizing the overall energy efficiency of the central air conditioner, the optimal operating parameter combination of the central air conditioner is obtained through an intelligent optimization algorithm.

[0094] With the goal of optimizing the overall energy efficiency of central air conditioning, the optimal operating parameter combination of central air conditioning is obtained through intelligent optimization algorithms, including: optimizing the calculation of the outlet water temperature set value of the chiller based on the current load demand and equipment performance characteristics; dynamically adjusting the operating frequency of the chilled water pump and the cooling water pump according to the system hydraulic characteristics and flow requirements; optimizing the operating mode and speed parameters of the cooling tower fan based on the ambient wet-bulb temperature and the cooling tower performance curve; and selecting the chiller operating parameter combination with the optimal overall energy efficiency of the central air conditioning according to the preset load range strategy. The energy consumption optimization flow chart of central air conditioning is as follows: Figure 5The energy consumption optimization process in the figure includes the steps of simulation data input, optimization algorithm execution, parameter adjustment and result feedback, which reflects the decision-making process of reducing energy consumption during the optimization process.

[0095] In this example, the operating parameters of the central air conditioning units are optimized based on the simulation results to reduce energy consumption. An intelligent algorithm automatically searches for the optimal parameter combination, adjusting, for example, the outlet water temperature setting of each chiller, the variable frequency speed of the water pump, and the number of cooling tower fan starts and stops to minimize total energy consumption while meeting indoor temperature and humidity requirements. If the central control system has automatic control capabilities, the optimized control strategy calculated by the optimization model can be directly issued for execution; otherwise, it will be presented as a suggestion prompting the operator to make adjustments.

[0096] It should be noted that this embodiment fully considers the characteristics of central air conditioners of different sizes in the optimization control strategy. For example, for a chiller room with multiple refrigeration units, the schematic diagram of the chiller unit load operation strategy is as follows: Figure 6 The entire cooling capacity demand is divided into multiple intervals, and each interval has a pre-defined unit activation strategy to ensure efficient matching of the units to the current load. Different colors or areas represent the range of different numbers of units in operation, such as a single unit at low load, a two-unit combination at medium load, and all three units at high load.

[0097] Figure 7 The following graph shows the strategy learning process, which illustrates the convergence trend of the control strategy during training. Initially, the algorithm's performance indicators fluctuated significantly, but the amplitude of the oscillations gradually decreased and stabilized. This demonstrates that through continuous learning and adjustment, the intelligent optimization strategy ultimately achieved stable convergence and was able to effectively adapt to the dynamic optimization needs of the central air conditioning system.

[0098] For example, suppose the system has three chillers, numbered 1, 2, and 3, with different cooling capacities. The central air conditioning load can be divided into the following ranges, and a start / stop combination for each range can be determined.

[0099]

[0100] The above interval division and unit combination are merely examples. In actual applications, the division scheme can be adjusted based on unit capacity and load characteristics. This condition-based control strategy ensures that no matter which range the actual cooling load falls within, there is always an appropriate unit combination to meet the demand, thus avoiding units operating in inefficient ranges or frequent starts and stops. Within each load range, by adjusting the number and combination of units in operation, the units can be operated at an optimal load factor, improving overall energy efficiency.

[0101] When load ranges change, the central air conditioning system smoothly switches between starting and stopping the corresponding units, adjusting the operating conditions of the water pumps and cooling towers to avoid impacting the indoor environment. While determining the unit combination, the energy consumption levels of each combination are comprehensively evaluated to select the optimal solution. For example, under medium loads (such as the 350-860RT range in the table above), there may be multiple two-unit combinations that can meet the load requirements. In this case, the instantaneous total energy consumption of each combination is calculated, and the combination with the lowest energy consumption is prioritized for operation.

[0102] S50. Synchronously display the operating data of the central air conditioner, the energy consumption distribution of each device, and the optimal operating parameter combination through a visual interface, and execute an optimized operating strategy based on the optimal operating parameter combination according to user instructions.

[0103] Based on the optimal operating parameter combination, the optimized operating strategy is executed according to user instructions, including: transmitting the chiller outlet water temperature set value, the operating frequency of the chilled water pump and the cooling water pump, and the operating mode and speed parameters of the cooling tower fan to the corresponding equipment controller; automatically or manually confirming the chiller start-stop combination plan according to the load interval strategy, and executing the optimized operating strategy according to the chiller start-stop combination plan.

[0104] In this embodiment, the optimized operation plan and energy consumption analysis results are presented to the user through the user interface, which facilitates the user to fully understand the performance status of the current central air-conditioning system. The interface will display key indicators such as the power consumption of each device under the current working conditions, total power consumption, system COP, and the energy consumption comparison before and after optimization. For example, the interface can intuitively display the COP-load curve of the refrigeration unit and the characteristic curves of each water pump, such as Figure 8 As shown, Figure 8 (a) is the performance diagram between the cooling water pump flow and head. Figure 8 (b) is the performance diagram between the flow rate, efficiency and head of cooling water pump 1. Figure 9 As shown, Figure 9 (a) is the water temperature-enthalpy value line graph, Figure 9 (b) in the figure is a line graph of air temperature and enthalpy. It shows how cooling tower performance indicators change under different operating conditions. For example, the relationship between the cooling tower's inlet and outlet water temperatures and the air's wet-bulb temperature, or the cooling efficiency curve calculated based on the Merkel number, illustrates how cooling tower energy consumption changes with environmental conditions. It also provides specific optimization suggestions (such as "shut down a chiller" or "lower the supply air temperature by 1°C") and displays a predicted 24-hour load curve for managers to use in developing operational plans.

[0105] The diagram of the visual interface is as follows Figure 10As shown in Figure 2, the interface includes functional areas such as parameter input, model calculation, and result output. The interface lists the operating parameters of each key device and calculated energy consumption indicators, such as the power consumption of each refrigeration unit and water pump, the current total power consumption, and the system COP value. Users can also view performance curves and historical data trends for each device and generate energy consumption analysis reports. Through this interface, operators can easily adjust the central air conditioning operation strategy based on optimization suggestions, thereby achieving efficient and energy-saving operation management.

[0106] The above steps are repeated in a loop, achieving continuous monitoring, simulation, and optimized control of the central air conditioner. When the external environment or load changes, a new cycle will automatically begin, recalculating and optimizing to ensure that the air conditioner always operates at a high energy efficiency.

[0107] The disclosed embodiment proposes a central air-conditioning energy consumption optimization control method, which comprehensively applies load forecasting, simulation, and optimization control strategies to reduce the overall energy consumption of the central air-conditioning unit and improve energy efficiency, thereby realizing an integrated solution for the central air-conditioning system from load forecasting, energy consumption simulation to optimization control. In summary, this embodiment, through multi-source data fusion and digital simulation optimization methods, can significantly improve the accuracy and optimization level of central air-conditioning energy consumption calculations, adapt to complex and changeable actual working conditions, output optimization control solutions in real time, and significantly improve the operating efficiency and energy saving level of the central air-conditioning system.

[0108] Example 2

[0109] To further illustrate the effectiveness of the present invention, the application effects of the system of the present invention are introduced below in combination with actual cases.

[0110] This embodiment takes the energy-saving optimization of the central air-conditioning system of a commercial building in Luoyang as an example. The commercial building in Luoyang has a construction area of ​​50,000 square meters and is equipped with three chillers (two centrifugal chillers and one screw chiller), multiple chilled water pumps and cooling water pumps, and a cooling tower group. The original system selected equipment based on traditional experience. The total cooling capacity margin of each unit was large, and there was a problem of low partial load efficiency during operation. Using the digital simulation energy consumption design system provided by the present disclosure, the energy-saving optimization design of the central air-conditioning system of the shopping mall was carried out. The specific process is as follows:

[0111] First, a load calculation was conducted to estimate and forecast the mall's air conditioning cooling load on a typical day. The results showed that the mall's cooling load was approximately 1800 RT during peak daytime hours and dropped to approximately 300 RT during nighttime hours, indicating significant load fluctuations. Data acquisition provided real-time information on indoor temperature and humidity, operating parameters of various units and pumps, and outdoor environmental conditions. This data was then input into a digital simulation model for analysis.

[0112] Using a digital simulation model, the energy consumption of different chiller combinations at various loads was compared. The simulation results show that operating only a single large-capacity centrifugal chiller at low and medium loads results in partial load operation, significantly reducing efficiency. However, operating a single screw chiller instead maintains a higher COP at the same load, saving approximately 10% of cooling power. When the load approaches peak (approximately 1800 RT), the total energy consumption of two centrifugal chillers operating together is lower than that of all three chillers operating. Therefore, there is no need to activate a third chiller until the load reaches its maximum.

[0113] Based on simulation analysis, the disclosed embodiment proposes an optimized unit scheduling strategy: screw units are prioritized when the load is low, and two centrifugal units share the load when the load is high, and simultaneous operation of three units is avoided as much as possible. In actual implementation, under the gradually increasing load during the day, the units are put into operation in sequence according to the optimization strategy. When the load exceeds a certain threshold, the second centrifugal unit is started to operate together; during the evening when the load decreases, one centrifugal unit is shut down first, and only the screw unit is kept running to meet the base load. Throughout the process, the start and stop of each unit transitions smoothly, and the indoor temperature and humidity are always maintained within the set range.

[0114] After running for a period of time according to the above optimization control strategy, energy-saving effect data was collected. Compared with before optimization, the average daily total power consumption of the shopping mall's central air-conditioning system has been reduced by about 15%, and the instantaneous power during peak hours has been reduced by about 20%. More importantly, due to more efficient operation of the unit, the temperature difference between the supply and return water of chilled water has increased, the supply air temperature and humidity control has become more stable, and the overall comfort of customers has been improved. The energy consumption analysis report generated by the visual user interface shows that the COP in different load ranges has increased: the COP in the low-load area has increased by about 10%, and the COP in the medium and high load areas has increased by about 5%, and the overall energy efficiency has been significantly improved. Figure 10 The interface report shown can intuitively show the comparison of energy consumption indicators before and after optimization, proving the energy-saving effect of the embodiment of the present disclosure in practical applications.

[0115] Example 3

[0116] As another aspect of the embodiment of the present disclosure, a central air-conditioning energy consumption optimization control system 100 is also provided. Figure 11 Shown, including:

[0117] The cooling load demand calculation module 1 obtains characteristic parameters for load calculation and calculates the cooling load demand of the central air conditioner under different working conditions using a dynamic load calculation model based on the characteristic parameters, which include building characteristic parameters, occupant distribution data, and environmental parameters.

[0118] Central air-conditioning operation data acquisition module 2, which acquires real-time central air-conditioning operation data, including indoor and outdoor temperature, humidity, equipment operation status and energy consumption parameters;

[0119] The energy consumption distribution calculation module 3 of each device establishes a digital simulation model of the central air conditioner based on the cooling load demand under different working conditions and the operating data of the central air conditioner, and calculates the energy consumption distribution of each device of the central air conditioner under different working conditions through the digital simulation model;

[0120] The operating parameter optimization module 4 uses an intelligent optimization algorithm to obtain the optimal operating parameter combination of the central air conditioner based on the energy consumption distribution of each device under different working conditions and with the goal of optimizing the overall energy efficiency of the central air conditioner;

[0121] The operation strategy optimization execution module 5 synchronously displays the operation data of the central air conditioner, the energy consumption distribution of each device and the optimal operation parameter combination through a visual interface, and executes the optimized operation strategy according to the user's instructions based on the optimal operation parameter combination.

[0122] In the absence of any contradiction, the above modules in the system of the embodiment of the present disclosure can implement any implementation of the above method.

[0123] Based on the description of the above embodiments, it can be seen that the embodiments of the present disclosure can achieve the following technical effects:

[0124] 1) This disclosure uses precise load calculation and detailed equipment models to accurately predict the energy consumption of central air-conditioning systems under various load conditions, thereby reducing energy waste caused by unreasonable design.

[0125] 2) The present disclosure can automatically optimize the operating parameters of the air-conditioning equipment according to real-time environmental parameters and load changes, respond to changes in working conditions in real time, and avoid the problem of delayed response of traditional control schemes.

[0126] 3) The present disclosure displays simulation and optimization results through a visual interface, making it convenient for operators to monitor system status and adjust operations according to suggestions, thus lowering the threshold for use.

[0127] The present disclosure also provides an electronic device comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the aforementioned central air conditioning energy consumption optimization control method. The electronic device may be provided as a terminal, server, or other device.

[0128] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implements the above-mentioned central air conditioning energy consumption optimization control method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0129] Those skilled in the art will understand that in the above-mentioned central air-conditioning energy consumption optimization control method and system of the specific implementation method, the writing order of each step does not mean a strict execution order and constitutes any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0130] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0131] While various embodiments of the present disclosure have been described above, the above descriptions are illustrative, non-exhaustive, and not intended to be limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technical improvements to existing technologies, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A central air conditioning energy consumption optimization control method, characterized in that: The steps include: S10, obtaining characteristic parameters for load calculation, and calculating the cooling load demand of the central air conditioner under different working conditions using a dynamic load calculation model based on the characteristic parameters, wherein the characteristic parameters include building characteristic parameters, occupant distribution data, and environmental parameters; S20, obtaining real-time central air conditioning operation data, the operation data including indoor and outdoor temperature, humidity, equipment operation status and energy consumption parameters; S30, establishing a digital simulation model of the central air conditioner based on cooling load demands under different operating conditions and operating data of the central air conditioner, and calculating energy consumption distribution of each device of the central air conditioner under different operating conditions using the digital simulation model; S40, based on the energy consumption distribution of each device under different working conditions, with the goal of optimizing the overall energy efficiency of the central air conditioner, obtain the optimal operating parameter combination of the central air conditioner through an intelligent optimization algorithm; S50. Synchronously display the operating data of the central air conditioner, the energy consumption distribution of each device, and the optimal operating parameter combination through a visual interface, and execute an optimized operating strategy based on the optimal operating parameter combination according to user instructions.

2. The method according to claim 1, characterized in that The dynamic load calculation model is used to calculate the cooling load demand of central air conditioners under different working conditions, including: Predicting a change trend of base load based on historical load data and environmental parameters in the characteristic parameters; Combined with the real-time monitored personnel distribution data and environmental parameters, the changing trend of the base load is dynamically optimized and adjusted through a dynamic correction algorithm, thereby obtaining the cooling load demand of the central air-conditioning system under different working conditions.

3. The method according to claim 1, characterized in that Based on the cooling load demand under different working conditions and the operating data of the central air conditioner, a digital simulation model of the central air conditioner is established, including: Based on the characteristic relationship between the energy efficiency ratio and load rate of the refrigeration unit, a performance model of the refrigeration unit is established; According to the correlation characteristics between flow rate and head, flow rate and power, and flow rate and efficiency, a hydraulic model of the water pump is established; Based on the principle of heat and mass transfer, a thermal exchange model of the cooling tower is established.

4. The method according to claim 3, characterized in that The digital simulation model is used to calculate the energy consumption distribution of each device in the central air-conditioning under different working conditions, including: Calculate the energy consumption distribution of the refrigeration unit under different operating conditions through the performance model of the refrigeration unit; Through the hydraulic model of the water pump, simulate the energy consumption distribution of the water pump under different working conditions; The energy consumption distribution of the cooling tower under different working conditions is analyzed through the thermal exchange model of the cooling tower.

5. The method according to any one of claims 3 or 4, characterized in that The performance model of the refrigeration unit is expressed using the polynomial fitting method as follows: COP(PLR)=a0+a1·PLR+a2·PLR 2 +a3·PLR 3 +a4·PLR 4 , Where a0, a1, a2, a3, and a4 are fitting coefficients, and PLR is the partial load rate.

6. The method according to claim 1, wherein With the goal of optimizing the overall energy efficiency of central air conditioning, the optimal operating parameter combination of central air conditioning is obtained through intelligent optimization algorithms, including: Optimize and calculate the chiller outlet water temperature setpoint based on current load demand and equipment performance characteristics; Dynamically adjust the operating frequency of the chilled water pump and cooling water pump according to the system hydraulic characteristics and flow requirements; Combined with the ambient wet-bulb temperature and cooling tower performance curve, the operation mode and speed parameters of the cooling tower fan are optimized and determined; According to the preset load interval strategy, select the chiller operating parameter combination with the best overall energy efficiency of the central air conditioning.

7. The method according to claim 6, characterized in that Based on the optimal combination of operating parameters, the optimized operation strategy is executed according to user instructions, including: Transmit the chiller outlet water temperature setpoint, the operating frequency of the chilled water pump and cooling water pump, and the operating mode and speed parameters of the cooling tower fan to the corresponding equipment controller; The chiller start-stop combination plan is automatically or manually confirmed according to the load interval strategy, and the optimized operation strategy is executed according to the chiller start-stop combination plan.

8. Central air conditioning energy consumption optimization control system, characterized in that, include: A cooling load demand calculation module obtains characteristic parameters for load calculation and calculates the cooling load demand of the central air conditioner under different working conditions using a dynamic load calculation model based on the characteristic parameters, which include building characteristic parameters, occupant distribution data, and environmental parameters. The central air-conditioning operation data acquisition module acquires real-time central air-conditioning operation data, including indoor and outdoor temperature, humidity, equipment operation status and energy consumption parameters; The energy consumption distribution calculation module of each device establishes a digital simulation model of the central air conditioner based on the cooling load demand under different working conditions and the operating data of the central air conditioner. The energy consumption distribution of each device of the central air conditioner under different working conditions is calculated by the digital simulation model; The operating parameter optimization module uses an intelligent optimization algorithm to obtain the optimal operating parameter combination for the central air conditioner based on the energy consumption distribution of each device under different working conditions and the goal of optimizing the overall energy efficiency of the central air conditioner. The operation strategy optimization execution module synchronously displays the operation data of the central air conditioner, the energy consumption distribution of each device and the optimal operation parameter combination through a visual interface, and executes the optimized operation strategy according to user instructions based on the optimal operation parameter combination.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the central air-conditioning energy consumption optimization control method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the central air-conditioning energy consumption optimization control method according to any one of claims 1 to 7 is implemented.

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