Air conditioner energy-saving optimization control system and method based on multi-parameter regulation and control

Abstract

The invention relates to the technical field of air conditioner energy-saving optimization, and discloses an air conditioner energy-saving optimization control system and method based on multi-parameter regulation and control, and the method comprises the following steps: obtaining environment data; the environment data comprises building information model data, indoor environment data, electricity price data and meteorological prediction data; based on the environment data, a three-dimensional game model used for air conditioner energy-saving optimization control is constructed; and solving the three-dimensional game model through a multi-target equalization algorithm, and generating and executing a control instruction of the air conditioning system. Multi-parameter regulation and control of the air conditioning system are achieved, energy can be remarkably saved, the comfort degree of the human body is guaranteed, and meanwhile the electricity utilization cost is reasonably controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of air - conditioning energy - saving optimization, and particularly relates to an air - conditioning energy - saving optimization control system and method based on multi - parameter regulation. Background Art

[0002] Although certain achievements have been made in the research and development of energy - saving technologies, the overall energy utilization efficiency of the current air - conditioning system is still not ideal. During the actual operation of many air conditioners, various influencing factors are not fully considered to dynamically adjust the operating parameters, resulting in relatively common energy waste. Taking commercial buildings as an example, due to the large differences in the personnel density, equipment heat generation, etc. in different areas at different time periods, the existing air - conditioning control methods are difficult to perform refined multi - parameter regulation for each area, causing high overall energy consumption.

[0003] Currently, most air - conditioning control systems have relatively single functions. They often can only perform simple start - stop control or limited adjustment based on a single parameter. In practical applications, they lack the ability to comprehensively consider and analyze multiple environmental parameters. Without being able to establish a comprehensive and accurate environmental model, it is difficult to achieve intelligent energy - saving control with multi - parameter optimization and provide the best operation plan that is both energy - saving and comfortable for users.

[0004] For example, the Chinese patent with the authorization announcement number CN118856527B discloses an end - air - conditioning energy - saving optimization control system and method, including a pipeline formed by sequentially connecting a fresh - air filter, a chilled - water valve with a chilled - water valve opening controller, and a fan with a fan frequency converter. The two ends of the pipeline are respectively provided with a fresh - air inlet and a supply - air outlet. The supply - air outlet is respectively connected to a plurality of diffusers. Fresh air and return air enter the pipeline through the fresh - air inlet and reach the corresponding working areas through the diffusers respectively. A temperature sensor is respectively arranged in each working area. The fan frequency converter and the temperature sensor are respectively electrically connected to the first PID1 module, and the chilled - water valve opening controller and the temperature sensor are respectively electrically connected to the second PID2 module. This technical solution overcomes the problems that the temperature regulation response speed of the air - conditioning fan and the chilled - water valve is relatively slow, the temperature fluctuation in the working area is large or the regulation is not timely; and it avoids the defect that controlling the opening of the chilled - water valve and the fan may produce interference effects, thereby affecting the temperature regulation in the working area.

[0005] As disclosed in a Chinese patent with the authorization announcement number CN104633857B, an air conditioner control method and device are provided. The method includes: collecting and storing the current operating parameters of the chiller unit, chilled water pump, and cooling water pump of the air conditioning system at a preset period; determining the energy consumption increment of the air conditioning system in the operating state with the to-be-adjusted operating parameters relative to the current operating state according to the current operating parameters and the to-be-adjusted operating parameters input by the user; when the energy consumption increment is less than zero, adjusting the operating state of the air conditioning system according to the to-be-adjusted operating parameters. The air conditioner control method and device provided by the invention can adjust the air conditioning system according to the to-be-adjusted operating parameters when the energy consumption increment is less than zero, achieving energy conservation and being easy to implement.

[0006] The above patents all have the problems raised in this background art: it is difficult to achieve intelligent energy-saving control with multi-parameter optimization and unable to provide the best operating plan that is both energy-saving and comfortable for users.

[0007] The information disclosed in this background art section is only intended to enhance the overall understanding of the present invention and should not be regarded as an admission or any form of implication that this information constitutes prior art already known to those of ordinary skill in the art. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide an air conditioner energy-saving optimization control system and method based on multi-parameter regulation, realizing the regulation of multiple parameters of the air conditioning system, significantly saving energy, ensuring human comfort, and reasonably controlling the electricity cost at the same time.

[0009] To solve the above technical problems, the present invention provides the following technical solutions:

[0010] On the one hand, the present invention provides an air conditioner energy-saving optimization control method based on multi-parameter regulation, including the following steps: obtaining environmental data; the environmental data includes building information model data, indoor environmental data, electricity price data, and meteorological prediction data;

[0011] Based on the environmental data, constructing a three-dimensional game model for air conditioner energy-saving optimization control; specifically including:

[0012] Calculating the heat buffer potential based on the building information model data and indoor environmental data as the first game objective of the three-dimensional game model;

[0013] Calculating the comfort demand interval based on the indoor environmental data and meteorological prediction data as the second game objective of the three-dimensional game model;

[0014] Calculating the electricity cost weight based on the electricity price data and meteorological prediction data as the third game objective of the three-dimensional game model;

[0015] Solve the three-dimensional game model through a multi-objective equilibrium algorithm, and generate and execute control instructions for the air-conditioning system.

[0016] As a preferred embodiment of the multi-parameter regulation air-conditioning energy-saving optimization control method of the present invention, wherein: the building information model data includes wall thermal parameters and spatial geometry data; wherein, the wall thermal parameters include thermal conductivity, specific heat capacity, and density; the spatial geometry data includes the volume, wall surface area, wall thickness of each control area, and the connectivity of adjacent control areas;

[0017] The indoor environment data includes the historical temperature, air supply volume, and historical heat load of each control area; the heat buffer potential is represented by a heat buffer time window; the heat buffer time window represents the maximum continuous duration for each control area to maintain the current temperature when the air conditioner is turned off.

[0018] As a preferred embodiment of the multi-parameter regulation air-conditioning energy-saving optimization control method of the present invention, wherein: calculating the heat buffer potential based on the building information model data and the indoor environment data specifically includes:

[0019] Based on a pre-trained buffer time prediction model, map the wall thermal parameters and spatial geometry data to the basic heat buffer potential of each control area; the input of the buffer time prediction model is a feature vector composed of wall thermal parameters and spatial geometry data, and the output is the basic heat buffer potential of the corresponding control area; the basic heat buffer potential is a reference value of the heat buffer time window;

[0020] Based on the historical temperature and historical heat load, correct the basic buffer potential to obtain a corrected heat buffer potential; specifically include: calculating the temperature change rate of the control area based on the historical temperature; correcting the basic buffer potential of the control area based on the temperature change rate to obtain a corrected heat buffer potential;

[0021] Based on the air supply volume, further adjust the corrected heat buffer potential to obtain the heat buffer time window of each control area; specifically include: adjusting the corrected heat buffer potential based on a preset adjustment rule to obtain the heat buffer time window, where the heat buffer time window is negatively correlated with the air supply volume.

[0022] As a preferred embodiment of the multi-parameter regulation air-conditioning energy-saving optimization control method of the present invention, wherein: the indoor environment data further includes the carbon dioxide concentration, thermal imaging data, real-time temperature, and real-time humidity of each control area; the meteorological prediction data includes outdoor temperature, outdoor humidity, and light intensity;

[0023] The comfort level range includes a temperature threshold range, a humidity fluctuation range, and a carbon dioxide concentration limit value; wherein, the temperature threshold range represents the variation range of the temperature in each control area; the humidity fluctuation range represents the value range of the humidity change amount in each control area; the carbon dioxide concentration limit value represents the maximum threshold of the carbon dioxide concentration in each control area.

[0024] As a preferred embodiment of the multi-parameter regulated air-conditioning energy-saving optimization control method of the present invention, it includes: calculating a comfort level demand range based on indoor environmental data and meteorological prediction data, specifically including:

[0025] Determining the personnel density level of each control area based on the carbon dioxide concentration and thermal imaging data; predicting the temperature change and humidity change of each control area based on the real-time temperature, real-time humidity, outdoor temperature, outdoor humidity, and light intensity;

[0026] Setting a reference comfort level demand range for each control area;

[0027] Adjusting the reference comfort level demand range based on the temperature change, humidity change, and personnel density level of the control area to obtain the comfort level demand range of each control area.

[0028] As a preferred embodiment of the multi-parameter regulated air-conditioning energy-saving optimization control method of the present invention, it includes: determining the personnel density level of each control area based on the carbon dioxide concentration and thermal imaging data, specifically including: setting a carbon dioxide concentration threshold and a number threshold; detecting the number of people in each control area based on the thermal imaging data; if the carbon dioxide concentration in any control area is greater than the carbon dioxide concentration threshold and the number of people is greater than the number threshold, the personnel density level of the corresponding control area is high density; if the carbon dioxide concentration in any control area is not greater than the carbon dioxide concentration threshold or the number of people is not greater than the number threshold, the personnel density level of the corresponding control area is medium density; if the carbon dioxide concentration in any control area is less than a preset minimum threshold or the number of people is 0, the personnel density level of the corresponding control area is low density.

[0029] As a preferred embodiment of the multi-parameter regulated air-conditioning energy-saving optimization control method of the present invention, it includes: the meteorological prediction data further includes extreme weather warning information; the electricity cost weight includes time-of-use electricity price and peak shaving power range; the time-of-use electricity price includes the electricity price of each period; the peak shaving power range represents the operating power range of the air-conditioning system;

[0030] Calculating the electricity cost weight based on electricity price data and meteorological prediction data, specifically including:

[0031] Based on the electricity price data, dividing the next 24 hours into different periods, recording the electricity price of each period to obtain the time-of-use electricity price;

[0032] Based on a preset peak shaving power strategy, map the extreme weather warning information to a peak shaving power range.

[0033] As a preferred solution of the multi-parameter regulation air-conditioning energy-saving optimization control method of the present invention, wherein: the adjustment objects of the control instructions of the air-conditioning system include the air-conditioning temperature setting value, the air supply volume, the start-stop timing of the compressor, the compressor power, and the fresh air system ventilation frequency of each control area;

[0034] Solving the three-dimensional game model through the multi-objective equilibrium algorithm specifically includes:

[0035] Generate an optimization objective and constraint conditions based on the first game objective, the second game objective, and the third game objective;

[0036] Generate an optimal solution set for the adjustment objects of the control instructions through the multi-objective equilibrium algorithm;

[0037] Based on a preset game objective priority, screen the final execution solution from the optimal solution set;

[0038] Analyze the control instructions of the air-conditioning system corresponding to the final execution solution.

[0039] As a preferred solution of the multi-parameter regulation air-conditioning energy-saving optimization control method of the present invention, wherein: the optimization objectives include: maximizing the thermal buffer time window; minimizing the humidity offset; minimizing the total electricity cost; wherein, the humidity offset is the offset of the humidity in the control area exceeding the humidity fluctuation range;

[0040] The specific constraint items of the constraint conditions include: the temperature in the control area is within the temperature threshold range; the carbon dioxide concentration is less than the carbon dioxide concentration limit value; the operating power of the air-conditioning system is within the peak shaving power range.

[0041] In a second aspect, the present invention provides a multi-parameter regulation air-conditioning energy-saving optimization control system, which includes a data acquisition module, a model construction module, a game calculation module, an algorithm solution module, and an instruction execution module; wherein:

[0042] The data acquisition module is used to acquire environmental data, including acquiring building information model data, indoor environmental data, electricity price data, and meteorological prediction data;

[0043] The model construction module constructs a three-dimensional game model for air-conditioning energy-saving optimization control based on the environmental data;

[0044] The game calculation module is used to calculate the first game objective, the second game objective, and the third game objective of the three-dimensional game model;

[0045] The algorithm solving module is used to solve the three-dimensional game model and generate the final execution solution of the air-conditioning system control instruction;

[0046] The instruction execution module is used to parse the final execution solution, obtain and execute the control instruction of the air-conditioning system.

[0047] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0048] The present invention calculates the thermal buffer potential by obtaining building information model data, indoor environment data, etc., maximizes the thermal buffer time window, can reduce the start-stop times of the air conditioner, and thus reduces power consumption. Taking the comfort demand interval as one of the game objectives, comprehensively considering indoor environment data and meteorological prediction data, it can ensure the comfort of personnel. Calculating the electricity cost weight based on electricity price data and meteorological prediction data, minimizing the total electricity cost, and reducing the operation pressure of the power grid.

[0049] By solving the three-dimensional game model through a multi-objective equilibrium algorithm, generating and executing the control instruction of the air-conditioning system, and adjusting multiple parameters such as the air-conditioning temperature setting value and the air supply volume, the energy-saving optimization control of the air conditioner with multi-parameter regulation is realized, and the accuracy and intelligence of the operation of the air-conditioning system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0051] Figure 1 is the flowchart of the energy-saving optimization control method for air-conditioning with multi-parameter regulation provided by the present invention;

[0052] Figure 2 is the structural schematic diagram of the energy-saving optimization control system for air-conditioning with multi-parameter regulation provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The technical solutions of the present invention will be described in detail below through the drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present invention and the embodiments are detailed descriptions of the technical solutions of the present invention, rather than limitations on the technical solutions of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0054] Embodiment 1

[0055] This embodiment introduces an energy-saving optimization control method for air-conditioning with multi-parameter regulation. Referring to Figure 1 , the method includes the following steps:

[0056] Obtain environmental data; the environmental data includes building information model data, indoor environmental data, electricity price data, and meteorological prediction data;

[0057] Based on the environmental data, construct a three-dimensional game model for air-conditioning energy-saving optimization control; specifically including:

[0058] Calculate the thermal buffer potential based on the building information model data and indoor environmental data, as the first game objective of the three-dimensional game model;

[0059] The building information model data includes wall thermal parameters and spatial geometry data; among them, the wall thermal parameters include thermal conductivity, specific heat capacity, and density; the wall thermal parameters are extracted based on BIM software such as Autodesk Revit and ArchiCAD; the wall thermal parameters can be used to calculate the thermal inertia time parameter of the wall. The spatial geometry data includes the volume, wall surface area, wall thickness of each control area, and the connectivity of adjacent control areas; the spatial geometry data can be used to determine the heat exchange path of each control area.

[0060] The object of the air-conditioning energy-saving optimization control of this application is the control areas divided in the building, such as office areas, warehouse areas, etc. Calculating the thermal inertia time parameter through the wall thermal parameters and determining the heat exchange path of each control area through the spatial geometry data can provide a data basis for subsequent air-conditioning energy-saving optimization control.

[0061] The indoor environmental data includes the historical temperature, air supply volume, and historical heat load of each control area; the historical temperature is the average temperature of the control area per hour in the past M hours; the air supply volume is the air flow velocity of the air supply outlet of the air conditioner in the corresponding control area, measured based on a wind speed sensor; the historical heat load is the working power of the air conditioner per hour in the past M hours. M is a positive integer.

[0062] The thermal buffer potential is represented by a thermal buffer time window; the thermal buffer time window represents the maximum continuous duration for each control area to maintain the current temperature when the air conditioner is turned off.

[0063] Calculate the thermal buffer potential based on the building information model data and indoor environmental data, specifically including:

[0064] Based on a pre-trained buffer time prediction model, map the wall thermal parameters and spatial geometry data to the basic thermal buffer potential of each control area;

[0065] The buffer time prediction model is any one of a polynomial regression model, a support vector regression model, and a multi-layer perceptron; the input of the buffer time prediction model is a feature vector composed of wall thermal parameters and spatial geometry data, and the output is the basic thermal buffer potential of the corresponding control area; the basic thermal buffer potential is the reference value of the thermal buffer time window. For a control area containing walls with high specific heat capacity and high density, its heat storage capacity is better than that of a lightweight partition wall, and the buffer time prediction model will map a larger basic thermal buffer potential for it. For an open control area with good connectivity, heat can be diffused through air convection, and the buffer time prediction model will map a smaller basic thermal buffer potential compared to a closed area.

[0066] Based on the historical temperature and historical heat load, the basic buffer potential is corrected to obtain a corrected thermal buffer potential; specifically, it includes: calculating the temperature change rate of the control area based on the historical temperature; correcting the basic buffer potential of the control area based on the temperature change rate to obtain a corrected thermal buffer potential.

[0067] In this embodiment, the preferred method for correcting the basic buffer potential of the control area based on the temperature change rate includes: setting a temperature change threshold; if the temperature change rate is higher than the temperature change threshold, the basic buffer potential of the corresponding control area is reduced; for example, if it is detected that the temperature of a certain control area rises by 2°C per hour and the temperature change threshold is 1°C per hour, then the actual heat storage capacity of this control area is lower than the theoretical value, and the basic buffer potential needs to be reduced.

[0068] Based on the air supply volume, the corrected thermal buffer potential is further adjusted to obtain the thermal buffer time window for each control area.

[0069] The method for further adjusting the corrected thermal buffer potential based on the air supply volume includes: adjusting the corrected thermal buffer potential based on a preset adjustment rule to obtain a thermal buffer time window, where the thermal buffer time window is negatively correlated with the air supply volume.

[0070] A preferred example of the adjustment rule in this embodiment is as follows: when it is detected that the air supply volume is greater than 3 m / s, the corrected thermal buffer potential is reduced by 15% to obtain the thermal buffer time window. High-speed air supply will accelerate indoor air convection, resulting in faster heat dissipation. Therefore, a larger air supply volume will affect the ability of the control area to maintain the current temperature.

[0071] Based on the indoor environment data and meteorological prediction data, calculate the comfort demand interval as the second game target of the three-dimensional game model.

[0072] The indoor environmental data also includes the carbon dioxide concentration, thermal imaging data, real-time temperature, and real-time humidity in each control area; among them, the carbon dioxide concentration is detected based on a gas sensor and is used to infer the occupancy density; the thermal imaging data is detected by an infrared sensor and is used together with the carbon dioxide concentration to judge the occupancy density;

[0073] The meteorological prediction data includes the outdoor temperature, outdoor humidity, and light intensity; based on the data provided by the meteorological API, the predicted values of the outdoor temperature, humidity, and light intensity for the next several hours are obtained, which are used to predict the indoor temperature and humidity changes.

[0074] The comfort demand range is used to represent the dynamic allowable range of temperature, humidity, and air quality acceptable to the human body. By setting the comfort demand range as one of the game objectives of the three-dimensional game model, the energy-saving objective and the human perception of comfort can be balanced.

[0075] The comfort range includes a temperature threshold range, a humidity fluctuation range, and a carbon dioxide concentration limit; among them, the temperature threshold range represents the change range of the temperature in each control area; the humidity fluctuation range represents the value range of the humidity change amount in each control area; the carbon dioxide concentration limit represents the maximum threshold of the carbon dioxide concentration in each control area.

[0076] Calculating the comfort demand range based on the indoor environmental data and the meteorological prediction data specifically includes:

[0077] Determining the occupancy density level of each control area based on the carbon dioxide concentration and the thermal imaging data; specifically including: setting a carbon dioxide concentration threshold and a number threshold; detecting the number of people in each control area based on the thermal imaging data; if the carbon dioxide concentration in any control area is greater than the carbon dioxide concentration threshold and the number of people is greater than the number threshold, the occupancy density level of the corresponding control area is high density; if the carbon dioxide concentration in any control area is not greater than the carbon dioxide concentration threshold or the number of people is not greater than the number threshold, the occupancy density level of the corresponding control area is medium density; if the carbon dioxide concentration in any control area is less than the preset minimum threshold or the number of people is 0, the occupancy density level of the corresponding control area is low density.

[0078] Based on the real-time temperature, real-time humidity, outdoor temperature, outdoor humidity, and light intensity, predict the temperature change and humidity change in each control area; predict the temperature change and humidity change in each control area based on a pre-trained temperature and humidity prediction model; the temperature and humidity prediction model is any one of a long short-term memory network, a self-attention model, and a gradient boosting tree; the inputs of the temperature and humidity prediction model include the real-time temperature, real-time humidity, outdoor temperature, outdoor humidity, light intensity, as well as the wall thermal parameters and spatial geometry data; the outputs of the temperature and humidity prediction model include the temperature prediction value and humidity prediction value in the control area within the next N hours. The temperature and humidity prediction model can output the change curves of temperature and humidity according to the prediction step (such as one prediction value every 15 minutes).

[0079] Set a reference comfort demand interval for each control area; include a reference temperature threshold interval, a reference humidity fluctuation interval, and a reference carbon dioxide concentration limit value.

[0080] In this embodiment, preferably, set the reference temperature threshold interval through the PMV model; by presetting the value range of the PMV value and calculating the PMV value, map the value range of the PMV value to the reference temperature threshold interval. For example, the preset value range of the PMV value is from -0.5 to 0.5.

[0081] This embodiment also preferably sets the reference carbon dioxide concentration limit value and the value range of humidity based on the ASHRAE standard; for example, the reference carbon dioxide concentration limit value is 1000 ppm; the value range of humidity is from 30% to 60%; further, based on the comparison between the real-time humidity and the value range of humidity, determine the reference humidity fluctuation interval.

[0082] Based on the temperature change, humidity change, and personnel density level in the control area, adjust the reference comfort demand interval to obtain the comfort demand interval for each control area. Specifically, it includes:

[0083] Calculate the temperature change rate and humidity change rate in the control area; respectively adjust each comfort demand interval based on the temperature change rate, humidity change rate, and personnel density level to obtain the temperature threshold interval, humidity fluctuation interval, and carbon dioxide concentration limit value for each control area.

[0084] The strategy for adjusting each comfort demand interval in this embodiment is preferably as follows:

[0085] If the temperature change rate is higher than the preset temperature change threshold, narrow the reference temperature threshold interval, and the higher the personnel density level, the smaller the reference temperature threshold interval, to compensate for the influence of human body heat dissipation on the temperature in the control area.

[0086] When the personnel density level is high and the real-time humidity is within the humidity value range, the reference humidity fluctuation range is increased to avoid humidity fluctuations caused by personnel activities and frequently trigger humidity regulation. If the real-time humidity is outside the humidity value range, the reference humidity fluctuation range remains unchanged, so as to preferentially trigger the humidity regulation instruction and ensure comfort priority.

[0087] If the personnel density level is high, and if the temperature change rate is higher than the preset temperature change threshold or the humidity change rate is higher than the preset humidity change threshold, the reference carbon dioxide concentration limit is increased to preferentially ensure temperature and humidity control.

[0088] Calculate the electricity cost weight based on electricity price data and meteorological prediction data as the third game objective of the three-dimensional game model;

[0089] The meteorological prediction data also includes extreme weather warning information, such as red high temperature warning, rainstorm warning, etc., which is used to measure the peak shaving pressure of the power grid, so as to determine the adjustable power range of the air conditioning system.

[0090] The electricity cost weight is used to represent the influence degree of air conditioning load in different time periods on the economy and stability of the power grid, including the cost weight reflecting electricity price sensitivity and power grid load demand.

[0091] The electricity cost weight includes time-of-use electricity price and peak shaving power range; the time-of-use electricity price includes the electricity price of each time period; the peak shaving power range represents the operating power range of the air conditioning system;

[0092] Calculating the electricity cost weight based on electricity price data and meteorological prediction data specifically includes:

[0093] Based on the electricity price data, divide the next 24 hours into different time periods, record the electricity price of each time period, and obtain the time-of-use electricity price;

[0094] Based on the preset peak shaving power strategy, map the extreme weather warning information to the peak shaving power range.

[0095] The peak shaving power strategy is as follows: based on the extreme weather warning information, determine the peak shaving emergency level; for example, the peak shaving emergency level corresponding to the red warning is high emergency, and the peak shaving emergency level corresponding to the yellow warning is low emergency; map the peak shaving emergency level to the peak shaving power range based on the preset peak shaving power rule. For example, the peak shaving power range corresponding to high emergency is not exceeding 70% of the rated power, indicating that under high emergency weather conditions, the maximum operating power allowed for the air conditioning system is 70% of the rated power.

[0096] Solve the three-dimensional game model through the multi-objective equilibrium algorithm, generate and execute the control instruction of the air conditioning system.

[0097] The adjustment targets of the control instructions of the air conditioning system include the air conditioning temperature setting value, the air supply volume, the start-stop sequence of the compressor, the compressor power, and the fresh air system ventilation frequency in each control area.

[0098] Solving the three-dimensional game model through the multi-objective equilibrium algorithm specifically includes:

[0099] Generating an optimization objective and constraint conditions based on the first game objective, the second game objective, and the third game objective; the optimization objective includes: maximizing the thermal buffer time window; minimizing the humidity offset; minimizing the total electricity cost; wherein, the humidity offset is the offset of the humidity in the control area exceeding the humidity fluctuation range. Maximizing the thermal buffer time window can reduce the number of air conditioning starts and stops, thereby reducing power consumption. The humidity offset is used as a soft constraint to participate in constructing the optimization objective, allowing the humidity to deviate from the humidity fluctuation range to a certain extent, but the offset needs to be controlled; minimizing the total electricity cost can reduce the operating pressure of the power grid while reducing energy consumption.

[0100] The specific constraint items of the constraint conditions include: the temperature in the control area is within the temperature threshold range; the carbon dioxide concentration is less than the carbon dioxide concentration limit value; the operating power of the air conditioning system is within the peak shaving power range. The temperature needs to be strictly controlled within the temperature threshold range to meet the comfort requirements; the carbon dioxide concentration needs to be less than the limit value, otherwise the fresh air system needs to be triggered for ventilation; the operating power of the air conditioning system is restricted according to the peak shaving power range to prevent excessive adjustment.

[0101] Generating an optimal solution set for the adjustment targets of the control instructions through the multi-objective equilibrium algorithm;

[0102] This application preferably uses the NSGA-II algorithm to generate an optimal solution set for the adjustment targets of the control instructions, specifically including:

[0103] Encoding the adjustment targets of multiple groups of control instructions as decision variables; any group of adjustment targets constitutes a set of solutions;

[0104] Calculating the variables corresponding to each optimization objective and each constraint item based on each group of adjustment targets; wherein,

[0105] The thermal buffer time window is calculated based on the air supply volume; the humidity of the control area is calculated by combining the air supply volume, the air change frequency of the fresh air system, and the outdoor humidity, and then the humidity offset is calculated; the higher the air supply volume, the higher the dehumidification efficiency; the fresh air system introduces outdoor air and combines the outdoor humidity to estimate the humidity of the control area. The operating power of the air conditioning system is calculated based on the compressor power, the air supply volume, and the air change frequency of the fresh air system, and the total electricity cost can be calculated by combining the time-of-use electricity price. The variables corresponding to the constraint items include the temperature of the control area, the carbon dioxide concentration, and the operating power of the air conditioning system; among them, the temperature of the control area is calculated based on the air conditioning temperature set value, the air supply volume, and the heat balance equation. The carbon dioxide concentration is calculated based on the air change frequency and the air supply volume of the fresh air system, and a carbon dioxide diffusion model can be constructed based on Fick's law. The operating power of the air conditioning system is calculated based on the compressor power, the air supply volume, and the air change frequency of the fresh air system, and combined with the energy consumption curve of each device.

[0106] Preferably, the calculation of each of the above optimization objectives and the variables corresponding to each constraint item is simulated and verified through a simulation tool. For example, the relationship between the air supply volume and the temperature and humidity of the control area is calibrated through CFD simulation.

[0107] Each set of solutions is screened based on the optimization objectives and the constraint items, and a non-dominated solution set is generated as the optimal solution set. The quality of the solutions is evaluated based on the optimization objectives, and the solutions that do not satisfy all the constraint items are eliminated based on the variables of the constraint items. For example, if the variable corresponding to the constraint item is the temperature of the control area, and the temperature of the control area is not within the temperature threshold range, then the constraint item is not satisfied. The non-dominated solution set is the Pareto optimal solution set, and each set of solutions in the Pareto optimal solution set represents different trade-off schemes for the three optimization objectives of the thermal buffer time window, the humidity offset, and the total electricity cost, and the solutions do not dominate each other (that is, it is impossible to be better in a certain optimization objective without sacrificing other optimization objectives).

[0108] Based on the preset priority of the game objectives, the final execution solutions are screened from the optimal solution set;

[0109] The following are the preferred priorities of some game objectives in this embodiment: Different game objective priorities are triggered according to the current scenario. For example, if the personnel density level in the current control area is high density, then the priority of minimizing the humidity offset is the highest, the priority of maximizing the thermal buffer time window is the second, and the priority of minimizing the total electricity cost is the lowest, so as to ensure the comfort of personnel as much as possible. The final execution solutions that can best meet the game objective priorities are screened from the optimal solution set.

[0110] Analyze the control instructions of the air conditioning system corresponding to the final execution solutions. Convert the final execution solutions into the air conditioning temperature set value, the air supply volume, the start-stop timing of the compressor, the compressor power, and the air change frequency of the fresh air system, and generate the control instructions of the air conditioning system to achieve the energy-saving optimization control of multi-parameter regulation.

[0111] Example 2

[0112] This example is the second example of the present invention; based on the same inventive concept as Example 1, referring to Figure 2 , this example introduces a multi-parameter regulated air-conditioning energy-saving optimization control system, which consists of a data acquisition module, a model construction module, a game calculation module, an algorithm solving module, and an instruction execution module; among them:

[0113] The data acquisition module is used to acquire environmental data, including acquiring building information model data, indoor environmental data, electricity price data, and meteorological prediction data; this module extracts wall thermal parameters and spatial geometric data from BIM software; measures the historical temperature, air supply volume, historical heat load, carbon dioxide concentration, thermal imaging data, real-time temperature, and real-time humidity of each control area; obtains meteorological prediction data through the meteorological API and records electricity price data to provide a basis for subsequent analysis and decision-making.

[0114] The model construction module constructs a three-dimensional game model for air-conditioning energy-saving optimization control based on the above environmental data. This module determines three game objectives of the three-dimensional game model by calculating the thermal buffer potential, comfort demand interval, and electricity cost weight, providing a model framework for realizing air-conditioning energy-saving optimization control.

[0115] The game calculation module is used to calculate the first game objective, the second game objective, and the third game objective of the three-dimensional game model; this module calculates the basic thermal buffer potential using the buffer time prediction model, modifies and adjusts it in combination with the historical temperature, historical heat load, and air supply volume to obtain the thermal buffer time window of each control area; determines the personnel density level according to the carbon dioxide concentration and thermal imaging data, predicts the temperature and humidity changes with the help of the temperature and humidity prediction model, combines the reference comfort demand interval, and adjusts according to the personnel density level and the temperature and humidity change rate to obtain the actual comfort demand interval; divides the time period according to the electricity price data to determine the time-of-use electricity price, and determines the peak shaving power range according to the extreme weather warning information and the peak shaving power strategy, so as to obtain the electricity cost weight.

[0116] The algorithm solving module is used to solve the above three-dimensional game model and generate the final execution solution of the air-conditioning system control instruction; this module takes maximizing the thermal buffer time window, minimizing the humidity offset, and minimizing the total electricity cost as the optimization objectives, takes the temperature, carbon dioxide concentration, and the operating power limit of the air-conditioning system in the control area as the constraint conditions, and generates the final execution solution that best meets the current scenario requirements through the multi-objective equilibrium algorithm and the preset game objective priority.

[0117] The instruction execution module is used to parse the final execution solution, obtain and execute the control instructions of the air conditioning system. Based on the control instructions, the air conditioning temperature setting value, the air supply volume, the start-stop timing of the compressor, the compressor power, and the ventilation frequency of the fresh air system are adjusted to achieve the energy-saving optimization control of the air conditioner with multi-parameter regulation.

[0118] For the specific function implementation of each of the above modules, refer to the relevant content in the multi-parameter regulation air conditioner energy-saving optimization control method described in Embodiment 1, which will not be elaborated here.

[0119] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the purpose and scope of the present invention. All of these are within the protection scope of the present invention.

Claims

1. An air conditioning energy-saving optimization control method based on multi-parameter regulation, characterized in that: The following steps are involved: Acquire environmental data; the environmental data includes building information model data, indoor environment data, electricity price data, and weather forecast data; Based on the environmental data, a three-dimensional game model for air conditioning energy-saving optimization control is constructed; specifically including: Calculate the thermal buffer potential based on the building information model data and indoor environment data as the first game objective of the three-dimensional game model; Calculate the comfort demand range based on indoor environment data and weather forecast data as the second game goal of the three-dimensional game model; The electricity cost weight is calculated based on electricity price data and weather forecast data as the third game objective of the three-dimensional game model; The three-dimensional game model is solved by a multi-objective equilibrium algorithm to generate and execute control instructions for the air-conditioning system.

2. The air conditioning energy-saving optimization control method based on multi-parameter regulation according to claim 1, characterized in that: The building information model data includes wall thermal parameters and spatial geometry data; wherein the wall thermal parameters include thermal conductivity, specific heat capacity, and density; the spatial geometry data includes the volume of each control area, the wall surface area, the wall thickness, and the connectivity of adjacent control areas; The indoor environment data includes the historical temperature, air supply volume, and historical heat load of each control area; the heat buffer potential is represented by a heat buffer time window; the heat buffer time window represents the maximum duration for each control area to maintain the current temperature when the air conditioner is turned off.

3. The air conditioning energy-saving optimization control method based on multi-parameter regulation according to claim 2, characterized in that: Calculate the thermal buffer potential based on building information model data and indoor environmental data, including: Based on the pre-trained buffer time prediction model, the wall thermal parameters and spatial geometry data are mapped to the basic thermal buffer potential of each control area; the input of the buffer time prediction model is the feature vector composed of the wall thermal parameters and spatial geometry data, and the output is the basic thermal buffer potential of the corresponding control area; the basic thermal buffer potential is the reference value of the thermal buffer time window; The basic buffer potential is corrected based on the historical temperature and the historical heat load to obtain a corrected heat buffer potential; specifically comprising: calculating the temperature change rate of the control area based on the historical temperature; correcting the basic buffer potential of the control area based on the temperature change rate to obtain a corrected heat buffer potential; The modified thermal buffer potential is further adjusted based on the air supply volume to obtain a thermal buffer time window for each control area; specifically including: adjusting the modified thermal buffer potential based on a preset adjustment rule to obtain a thermal buffer time window, wherein the thermal buffer time window is negatively correlated with the air supply volume.

4. The air conditioning energy-saving optimization control method based on multi-parameter regulation according to claim 3, characterized in that: The indoor environment data also includes the carbon dioxide concentration, thermal imaging data, real-time temperature, and real-time humidity of each control area; the weather forecast data includes outdoor temperature, outdoor humidity, and light intensity; The comfort interval includes a temperature threshold interval, a humidity fluctuation interval, and a carbon dioxide concentration limit; wherein the temperature threshold interval represents the temperature variation range of each control area; the humidity fluctuation interval represents the value range of the humidity variation amount of each control area; and the carbon dioxide concentration limit represents the maximum threshold of the carbon dioxide concentration of each control area.

5. The air conditioning energy-saving optimization control method based on multi-parameter regulation according to claim 4, characterized in that: The comfort level requirement range is calculated based on indoor environment data and weather forecast data, including: Determine the personnel density level of each control area based on the carbon dioxide concentration and thermal imaging data; predict the temperature change and humidity change of each control area based on the real-time temperature, real-time humidity, outdoor temperature, outdoor humidity, and light intensity; Set a reference comfort requirement range for each control area; The reference comfort requirement interval is adjusted based on the temperature change, humidity change and personnel density level of the control area to obtain the comfort requirement interval of each control area.

6. The air conditioning energy-saving optimization control method based on multi-parameter regulation according to claim 5, characterized in that: Determining the personnel density level of each control area based on the carbon dioxide concentration and thermal imaging data specifically includes: setting a carbon dioxide concentration threshold and a number threshold; detecting the number of people in each control area based on the thermal imaging data; if the carbon dioxide concentration in any control area is greater than the carbon dioxide concentration threshold, and the number of people is greater than the number threshold, then the personnel density level of the corresponding control area is high density; if the carbon dioxide concentration in any control area is not greater than the carbon dioxide concentration threshold, or the number of people is not greater than the number threshold, then the personnel density level of the corresponding control area is medium density; if the carbon dioxide concentration in any control area is less than a preset minimum threshold or the number of people is 0, then the personnel density level of the corresponding control area is low density.

7. The air conditioning energy-saving optimization control method based on multi-parameter regulation according to claim 6, characterized in that: The meteorological forecast data also includes extreme weather warning information; the electricity cost weight includes time-of-use electricity price and peak-shaving power range; the time-of-use electricity price includes the electricity price for each period; the peak-shaving power range represents the operating power range of the air-conditioning system; The electricity cost weight is calculated based on electricity price data and weather forecast data, including: Based on the electricity price data, the next 24 hours are divided into different time periods, and the electricity price of each time period is recorded to obtain the time-of-use electricity price; Based on a preset peak-shaving power strategy, the extreme weather warning information is mapped into a peak-shaving power range.

8. The air conditioning energy-saving optimization control method based on multi-parameter regulation according to claim 7, characterized in that: The control instructions of the air conditioning system include the air conditioning temperature setting value, air supply volume, compressor start and stop sequence, compressor power, and fresh air system ventilation frequency of each control area; Solving the three-dimensional game model by a multi-objective equilibrium algorithm specifically includes: Generate optimization objectives and constraints based on the first game objective, the second game objective, and the third game objective; Generate the optimal solution set of the control command regulation object through the multi-objective balancing algorithm; Based on the preset game objective priority, selecting the final execution solution from the optimal solution set; The control instruction of the air-conditioning system corresponding to the final execution solution is analyzed.

9. The air conditioning energy-saving optimization control method based on multi-parameter regulation according to claim 8, characterized in that: The optimization objectives include: maximizing the thermal buffer time window; minimizing the humidity offset; minimizing the total electricity cost; wherein the humidity offset is the offset of the humidity in the control area beyond the humidity fluctuation range; Specific constraint items of the constraint condition include: the temperature of the control area is within the temperature threshold range; the carbon dioxide concentration is less than the carbon dioxide concentration limit; and the operating power of the air-conditioning system is within the peak-shaving power range.

10. An air conditioning energy-saving optimization control system based on multi-parameter regulation, which is used to implement the air conditioning energy-saving optimization control method based on multi-parameter regulation as described in any one of claims 1 to 9, characterized in that: It includes data acquisition module, model building module, game calculation module, algorithm solving module and instruction execution module; among which: The data acquisition module is used to acquire environmental data, including building information model data, indoor environment data, electricity price data, and weather forecast data; The model building module builds a three-dimensional game model for air conditioning energy-saving optimization control based on the environmental data; The game calculation module is used to calculate the first game target, the second game target, and the third game target of the three-dimensional game model; The algorithm solving module is used to solve the three-dimensional game model and generate the final execution solution of the air conditioning system control instruction; The instruction execution module is used to parse the final execution solution, obtain and execute the control instructions of the air-conditioning system.

Images