Air conditioning load aggregation method, device and storage medium considering user response

Abstract

The invention discloses an air conditioning load aggregation method, device and storage medium considering user response, belonging to the technical field of stratified aggregation and control of air conditioning loads, and solving the problem of how to accurately and effectively obtain the overall response characteristics of an air conditioning load group, by establishing equivalent thermodynamic parameters ETP, working state and electrical model of the air conditioning load; based on the user response willingness and the difference in air conditioning equipment parameters, using the distributed control mode of the load aggregator to control the dispersed load group; clustering the groups considering the rated cooling capacity and the user response willingness, using the K-means++ algorithm to cluster and obtain the best clustering result for reducing the difference in response speed of the air conditioning equipment in the group; the invention obtains the overall power consumption law of a large number of air conditioning loads by load aggregation technology, and establishes a stratified aggregation model of air conditioning loads that comprehensively considers the user response willingness and the difference in air conditioning response characteristics, so as to optimize the control of the aggregated air conditioning loads and achieve the effect of smoothing the fluctuation of renewable energy and shaving the peak and filling the valley.

Description

Technical Field

[0001] The present invention belongs to the technical field of air-conditioning load stratification aggregation and regulation and control, and relates to an air-conditioning load aggregation method, device and storage medium taking user response into consideration. Background Art

[0002] With the gradual increase in renewable energy power generation output and grid penetration, demand-side resources have received widespread attention from power scholars in terms of participating in solving the power fluctuation and uncertainty problems brought about by the access of new energy to the grid. Among them, air-conditioning loads, which account for a large proportion of electricity consumption in summer, are the main representatives of temperature control loads. Given their advantages such as fast response speed, large response capacity and low investment cost, they are gradually becoming a reliable and effective backup form in addition to the power generation side. Reasonable allocation and optimization of air-conditioning load groups to reduce load power can achieve the purpose of alleviating power supply pressure during peak hours and smoothing out system power shortages caused by fluctuations in renewable energy output. There are many air-conditioning loads in the distribution network, and the individual parameters and response characteristics of air-conditioning vary greatly. The general air-conditioning model obtained through modeling has large errors in describing its response characteristics.

[0003] In this regard, some existing studies, such as the paper "Aggregated Modeling and Control of AirConditioning Loads for Demand Response" (Zhang W, Lian J, Chang C, et al. IEEE Transactions on Power Systems, 2013, 28(4): 4655-4664.), consider the defects of the general air conditioning model and use the load aggregation technology of the simplified mathematical model to achieve modeling and analysis of the heat exchange process between the air conditioner, the room and the outside world. However, although the model has high accuracy and fast calculation, it does not fully consider the impact of parameter differences on load aggregation characteristics.

[0004] To solve the above problems, the concept of secondary aggregation has gradually attracted attention. The concept of secondary aggregation fully considers the impact of regional differences and parameter differences on the decentralized air conditioning load. However, its defect is that it does not take the degree of willingness of air conditioning users to be controlled as a reference item. In the actual application process, the willingness of different air conditioning users to participate in the response is different, which will affect the aggregation effect. Under the influence of the incentive mechanism, some users are willing to participate in the response at any time, some users prefer to enjoy autonomy and only participate in the response when necessary, and some users refuse to participate in the response under any circumstances. Existing research, such as the document "An Evaluation of the HVAC Load Potential for Providing Load Balancing Service" (Lu N. IEEE Transactions on Smart Grid, 2012, 3(3): 1263-1270.), mostly assumes or defaults that air conditioning users are the first part of users, which is biased and slightly one-sided in the actual application process. Summary of the invention

[0005] The technical solution of the present invention is used to solve the problem of how to comprehensively consider the heterogeneity of air-conditioning loads, the differences in important parameter indicators and the differences in the willingness of different users to participate in demand response, so as to accurately and effectively obtain the overall response characteristics of the air-conditioning load group.

[0006] The present invention solves the above technical problems through the following technical solutions:

[0007] An air conditioning load aggregation method considering user response includes the following steps:

[0008] S1. Construct the equivalent thermodynamic parameter ETP model, working state model and electrical model of air conditioning load;

[0009] S2, decentralized load group control based on user response willingness and air conditioning equipment parameter differences;

[0010] S3. Cluster the air conditioning loads within the group to obtain the best clustering result.

[0011] Furthermore, the construction method of the equivalent thermodynamic parameter ETP model of the air conditioning load described in step S1 is as follows:

[0012] By simulating the heat transfer and energy balance process between the air-conditioned room and the outside world based on the equivalent circuit composed of resistors, capacitors and other circuit elements, the first-order ETP model for air-conditioning modeling can be simplified:

[0013] (1)

[0014] In formula (1), is the outdoor temperature of the air-conditioned room; is the indoor ambient temperature of the air-conditioned room. When the air-conditioning equipment is in the "off" state, exist The value at time is calculated as: , when the air conditioner is in the "on" state, exist The value at time is calculated as: , for exist The value of the moment, for exist The value of the moment, is the simulation step size; is the cooling capacity of the air conditioner; is the equivalent heat capacity of the room as a whole; is the overall equivalent thermal resistance of the room; is the energy efficiency ratio; is the rated power of the air conditioner.

[0015] Furthermore, the method for constructing the working state model described in step S1 is as follows:

[0016] The relationship between the upper and lower limits of temperature adjustment and the dead zone value of the refrigeration air conditioner is expressed as follows:

[0017] (2)

[0018] In formula (2), is the lower limit of temperature regulation; is the upper limit of temperature regulation; is the set target temperature; is the temperature dead zone width of the air conditioner;

[0019] The relationship between the start and stop status of the air conditioner and the indoor ambient temperature is expressed as:

[0020] (3)

[0021] In formula (3), Indicates the start / stop status of the air conditioner. The air conditioner is turned on; The air conditioner is turned off.

[0022] Furthermore, the method for constructing the electrical model described in step S1 is as follows:

[0023] The fixed frequency air conditioner works at a constant speed and at rated power when turned on. When running, the power is 0 when turned off. The power consumption expression of air conditioner running is:

[0024] (4)

[0025] In formula (4), The air conditioner is The power consumption at the moment, is the rated operating power of the air conditioner;

[0026] Cooling capacity of air conditioner The expression is:

[0027] (5)

[0028] In formula (5), is the rated cooling capacity of the air conditioner;

[0029] The relationship between air conditioning cooling capacity and power consumption is:

[0030] (6)

[0031] In formula (6), It is the power taken.

[0032] Furthermore, the method for controlling the distributed load group based on the user response willingness and the difference in air conditioning equipment parameters described in step S2 is specifically as follows:

[0033] S21. Users independently select the corresponding response level to participate in grid regulation based on their willingness to participate in demand response. Different response levels are ranked from high to low according to their willingness to participate in response: green, yellow, red, and black;

[0034] S22. The controller installed on each terminal air conditioning device is responsible for measuring the indoor ambient temperature, power consumption, operating status and other information of the air conditioning device, and sending relevant data information to the load aggregator control center via WIFI;

[0035] S23: Distribute the large amount of distributed air conditioning load according to the rated cooling capacity and the user's response willingness level. Divided into The air conditioner groups within a certain interval of rated cooling capacity are divided into one group; the specific operation is: first divide the air conditioner group into Then, in each range of rated cooling capacity group, green, yellow and red users are divided into three groups respectively, and all black users with different rated cooling capacity are combined into one group. Thus, all air conditioning loads are divided into Group.

[0036] Furthermore, the method for clustering the air conditioning loads in the group to obtain the best clustering result described in step S3 is specifically as follows:

[0037] S31. According to the air conditioning load ETP model, when the outside temperature is the same, the temperature change rate of the air conditioning load at the same temperature state depends on the following two indicators: and ;

[0038] S32, based on the cooling capacity of the air conditioner and user response willingness level The total air conditioning load is divided into After grouping, construct the matrix To store the Total in air conditioning group Air-conditioned and Two indicators;

[0039] S33, take the first The maximum value of the index , standard matrix is ​​obtained by normalization ,in, ; is the jth index of the i-th air conditioner;

[0040] S34. Use K-means++ algorithm to obtain the best air conditioning load clustering results.

[0041] Furthermore, the method for obtaining the best air conditioning load clustering result using the K-means++ algorithm described in step S34 is specifically as follows:

[0042] S341, from the standard matrix Randomly select a sample from As the first initial cluster center , and Represented as the values ​​of the first and second features of the initial cluster center respectively;

[0043] S342. Confirm the second initial cluster center according to the roulette method , according to the Euclidean norm principle, the remaining sample data With the existing cluster center The distance between them is:

[0044] (7)

[0045] S343. The probability of each sample data being selected as the next cluster center is expressed as:

[0046] (8)

[0047] Based on this, the roulette method is used to assign the 3rd to nth initial cluster centers Select them separately;

[0048] S344, the first Sample data According to the principle of minimum Euclidean distance, the corresponding cluster center is assigned. The calculation formula of Euclidean distance is:

[0049] (9)

[0050] In formula (9), , and They are The first and second indicators of each air conditioner;

[0051] S345. Calculate the mean of the samples in each class as the new cluster center , , … They are calculated in the first iteration. The center of the cluster;

[0052] S346. Repeat the above iterative calculation until the convergence condition is met:

[0053] (10)

[0054] In formula (10), and They are Second and The cluster centers calculated by the iteration;

[0055] S347, output the number of clusters of air conditioning loads in each group and clustering results.

[0056] A device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned air conditioning load aggregation method considering user response, and the processor is configured to execute the program stored in the memory.

[0057] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the above-mentioned air conditioning load aggregation method considering user response.

[0058] The advantages of the present invention are:

[0059] (1) In view of the fact that the high-order ETP model has too much calculation and too long calculation time when simulating the working state of the air-conditioning load, the present invention adopts a first-order ETP model that ignores the difference between the indoor ambient air temperature and the temperature of solid objects to simulate the air-conditioning load. This model can obtain a very considerable calculation speed at the expense of less calculation accuracy.

[0060] (2) The present invention uses a load aggregation-based technology to aggregate a large number of loads and uniformly group and allocate them, with the general air conditioning model obtained by simple modeling having a large error in describing its response characteristics. Its implementation mainly depends on the start-stop control mode of the load aggregator, which improves the accuracy of obtaining the overall power consumption pattern of a large number of air conditioning loads;

[0061] (3) In view of the fact that the existing air-conditioning load research based on the concept of secondary aggregation does not take the willingness of air-conditioning users to participate in demand response as a reference, this paper proposes a hierarchical aggregation model of air-conditioning load based on the concept of secondary aggregation, which better follows the willingness level of air-conditioning users to participate in demand response while considering the differences in air-conditioning load parameters;

[0062] (4) The present invention uses the K-means++ algorithm to adaptively cluster the air conditioner groups in each group to form small air conditioner clusters that can characterize the air conditioner response characteristics in the group. This improves the convergence speed and clustering quality of the traditional K-means algorithm, can converge to the global optimal solution faster, and produce better quality air conditioner load clustering results. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The figure is a flow chart of the air conditioning load aggregation method considering user response of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0066] Embodiment 1

[0067] like Figure 1 As shown, an air conditioning load aggregation method considering user response includes the following steps:

[0068] Step 1: Construct the Equivalent Thermal Parameters (ETP) model, working state model and electrical model of the air conditioning load, as follows:

[0069] Step 1.1: According to the equivalent circuit composed of resistors, capacitors and other circuit elements, the heat transfer and energy balance process between the air-conditioned room and the outside world can be simulated to simplify the first-order ETP model used for air-conditioning modeling:

[0070] (1)

[0071] In formula (1), is the outdoor temperature of the air-conditioned room; is the indoor ambient temperature of the air-conditioned room. When the air-conditioning equipment is in the "off" state, exist The value at time is calculated as: , when the air conditioner is in the "on" state, exist The value at time is calculated as: , for exist The value of the moment, for exist The value of the moment, is the simulation step size; is the cooling capacity of the air conditioner; is the equivalent heat capacity of the room as a whole; is the overall equivalent thermal resistance of the room; is the energy efficiency ratio; is the rated power of the air conditioner.

[0072] Step 1.2: Construct the working state model of the air conditioner

[0073] The relationship between the upper and lower limits of temperature adjustment and the dead zone value of the refrigeration air conditioner is expressed as follows:

[0074] (2)

[0075] In formula (2), is the lower limit of temperature regulation; is the upper limit of temperature regulation; is the set target temperature; is the temperature dead zone width of the air conditioner;

[0076] The relationship between the start and stop status of the air conditioner and the indoor ambient temperature is expressed as:

[0077] (3)

[0078] In formula (3), Indicates the start / stop status of the air conditioner. The air conditioner is turned on; The air conditioner is turned off.

[0079] Step 1.3: Build the electrical model of the air conditioner

[0080] The fixed frequency air conditioner operates at a constant speed and at rated power when turned on. When running, the power is 0 when turned off. The power consumption expression of air conditioner running is:

[0081] (4)

[0082] In formula (4), The air conditioner is The power consumption at the moment, is the rated operating power of the air conditioner;

[0083] Cooling capacity of air conditioner The expression is:

[0084] (5)

[0085] In formula (5), is the rated cooling capacity of the air conditioner;

[0086] The relationship between air conditioning cooling capacity and power consumption is:

[0087] (6)

[0088] In formula (6), It is the power taken.

[0089] The first-order ETP model simulates the heat transfer and energy balance process between the air-conditioned room and the outside world based on an equivalent circuit composed of circuit components such as resistors and capacitors. It can greatly improve the calculation speed while sacrificing less calculation accuracy. It has a simple structure, a small amount of parameters to obtain, and low calculation difficulty. The establishment of the working state model and the air-conditioning electrical model clarifies the operating state of the air-conditioning equipment and the power loss of the corresponding state.

[0090] Step 2: Distributed load group control based on user response willingness and air conditioning equipment parameter differences, as follows:

[0091] Step 2.1: Users independently select the corresponding response level to participate in grid regulation based on their willingness to participate in demand response. Different response levels are ranked from high to low according to their willingness to participate in response: green, yellow, red, and black.

[0092] Step 2.2: The controller installed on each terminal air-conditioning device is responsible for measuring the indoor ambient temperature, power consumption, operating status and other information of the air-conditioning device, and sending relevant data information to the load aggregator control center via WIFI.

[0093] Step 2.3: Distribute the large amount of dispersed air conditioning load according to the rated cooling capacity and user response willingness level. Divided into Group, divide the air conditioners within a certain interval of rated cooling capacity into one group.

[0094] The specific operation is: first divide the air conditioner group into Then, in each range of rated cooling capacity group, green, yellow and red users are divided into three groups respectively, and all black users with different rated cooling capacity are combined into one group. Thus, all air conditioning loads are divided into Group.

[0095] According to the willingness of users to participate in demand response, they are divided into four categories from high to low: green, yellow, red and black users, among which black users never participate in the response. Rated cooling capacity is a key factor in characterizing the working characteristics and internal parameters of air conditioners. The rated cooling capacity values ​​of different air conditioners are relatively independent and have discrete characteristics. According to the rated cooling capacity and the user's willingness to respond level, air conditioner groups with similar parameter values ​​can be divided into one group. It is achieved that the differences in the willingness levels of different users to participate in demand response are taken into account while considering the heterogeneity of air conditioning loads and the differences in important parameter indicators.

[0096] Step 3: The air conditioning loads in each group are clustered to obtain the best clustering results, as follows:

[0097] Step 3.1: According to the ETP model of air conditioning load, when the outside temperature is the same, the temperature change rate of the air conditioning load in the same temperature state (similar indoor ambient temperature and consistent start and stop states) depends on the following two indicators: and ;

[0098] Step 3.2: Based on the cooling capacity of the air conditioner and user response willingness level The total air conditioning load is divided into After grouping, construct the matrix To store the Total in air conditioning group Air-conditioned and Two indicators;

[0099] Step 3.3, take the The maximum value of the index , standard matrix is ​​obtained by normalization ,in, ; is the jth index of the i-th air conditioner;

[0100] Step 3.4: Use the K-means++ algorithm to obtain the best air conditioning load clustering results, as follows:

[0101] Step 3.4.1. From the standard matrix Randomly select a sample from As the first initial cluster center , and Represented as the values ​​of the first and second features of the initial cluster center respectively;

[0102] Step 3.4.2: Determine the second initial cluster center according to the roulette wheel method , according to the Euclidean norm principle, the remaining sample data With the existing cluster center The distance between them is:

[0103] (7)

[0104] Step 3.4.3, the probability of each sample data being selected as the next cluster center is expressed as:

[0105] (8)

[0106] Based on this, the roulette method is used to assign the 3rd to nth initial cluster centers Select them separately;

[0107] Step 3.4.4: Sample data According to the principle of minimum Euclidean distance, the corresponding cluster center is assigned. The calculation formula of Euclidean distance is:

[0108] (9)

[0109] In formula (9), , and They are The first and second indicators of each air conditioner;

[0110] Step 3.4.5: Calculate the mean of the samples in each class as the new cluster center , , … They are calculated in the first iteration. The center of the cluster;

[0111] Step 3.4.6, repeat the above iterative calculation until the convergence condition is met:

[0112] (10)

[0113] In formula (10), and They are Second and The cluster centers calculated by the iteration;

[0114] Step 3.4.7: Output the number of clusters of air conditioning loads in each group and clustering results.

[0115] For Air conditioning unit , K-means++ clustering technology is used to reduce the differences in the response speed (temperature rise / temperature drop rate) of air-conditioning equipment in the group. According to the air-conditioning load ETP model, when the outside temperature is the same, the temperature change rate of the air-conditioning load in the same temperature state (similar indoor ambient temperature, consistent start-stop state) depends on and Two indicators. Based on and The value pair of the indicator The air-conditioning groups were clustered separately, and the cluster centers in each group were used to represent the characteristic indicators of the individuals in each group.

[0116] The present invention adopts a hierarchical aggregation method of air-conditioning loads and its control strategy that comprehensively considers the differences in user response willingness and air-conditioning response characteristics. It uses a first-order ETP model to simulate the heat transfer and energy balance process between the air-conditioned room and the outside world, and accurately simulates the working state of the air-conditioning load. The concept of secondary aggregation of air-conditioning loads that depends on load aggregators is adopted. A large number of loads are aggregated and uniformly grouped and deployed based on load aggregation technology. The concept of secondary aggregation is used to comprehensively consider the differences in air-conditioning load parameters and the willingness level of users to participate in demand response. The K-means++ algorithm is used to adaptively cluster the air-conditioning groups in each group to form small air-conditioning clusters that can characterize the air-conditioning response characteristics in the group.

[0117] Embodiment 2

[0118] A device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the air conditioning load aggregation method considering user response in embodiment 1, and the processor is configured to execute the program stored in the memory.

[0119] Embodiment 3

[0120] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the air conditioning load aggregation method considering user response in embodiment 1.

[0121] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An air conditioning load aggregation method considering user response, characterized in that: The following steps are involved: S1. Construct the equivalent thermodynamic parameter ETP model, working state model and electrical model of air conditioning load; The construction method of the equivalent thermodynamic parameter ETP model of the air conditioning load is as follows: By simulating the heat transfer and energy balance process between the air-conditioned room and the outside world based on the equivalent circuit composed of resistors, capacitors and other circuit elements, the first-order ETP model for air-conditioning modeling can be simplified: （1） In formula (1), is the outdoor temperature of the air-conditioned room; is the indoor ambient temperature of the air-conditioned room. When the air-conditioning equipment is in the "off" state, exist The value at time is calculated as: , when the air conditioning equipment is in the "on" state, exist The value at time is calculated as: , for exist The value of the moment, for exist The value of the moment, is the simulation step size; is the cooling capacity of the air conditioner; is the equivalent heat capacity of the room as a whole; is the overall equivalent thermal resistance of the room; is the energy efficiency ratio; is the rated power of the air conditioner; The method for constructing the working state model is as follows: The relationship between the upper and lower limits of temperature adjustment and the dead zone value of refrigeration air conditioning is expressed as follows: （2） In formula (2), is the lower limit of temperature regulation; is the upper limit of temperature regulation; is the set target temperature; is the temperature dead zone width of the air conditioner; The relationship between the start and stop status of the air conditioner and the indoor ambient temperature is expressed as: （3） In formula (3), Indicates the start / stop status of the air conditioner. The air conditioner is turned on; The air conditioner is turned off; The electrical model is constructed as follows: The fixed frequency air conditioner operates at a constant speed and at rated power when turned on. When running, the power is 0 when turned off. The power consumption expression of air conditioner running is: （4） In formula (4), The air conditioner is The power consumption at the moment, is the rated operating power of the air conditioner; Cooling capacity of air conditioner The expression is: （5） In formula (5), is the rated cooling capacity of the air conditioner; The relationship between air conditioning cooling capacity and power consumption is: （6） In formula (6), is the power taken; S2. Distributed load group control based on user response willingness and air conditioning equipment parameter differences. The specific method is as follows: S21. Users independently select the corresponding response level to participate in grid regulation based on their willingness to participate in demand response. Different response levels are ranked from high to low according to their willingness to participate in response: green, yellow, red, and black; S22. The controller installed on each terminal air conditioning device is responsible for measuring the indoor ambient temperature, power consumption, operating status and other information of the air conditioning device, and sending relevant data information to the load aggregator control center via WIFI; S23: Distribute the large amount of distributed air conditioning load according to the rated cooling capacity and the user's response willingness level. Divided into The air conditioner groups within a certain interval of rated cooling capacity are divided into one group; the specific operation is: first divide the air conditioner group into Then, in each range of rated cooling capacity group, green, yellow and red users are divided into three groups respectively, and all black users with different rated cooling capacity are combined into one group. Thus, all air conditioning loads are divided into Group; S3. Cluster the air conditioning loads within the group to obtain the best clustering result. The specific method is as follows: S31. According to the air conditioning load ETP model, when the outside temperature is the same, the temperature change rate of the air conditioning load at the same temperature state depends on the following two indicators: and ; S32, based on the cooling capacity of the air conditioner and user response willingness level The total air conditioning load is divided into After grouping, construct the matrix To store the Total in air conditioning group Air-conditioned and Two indicators; S33, take the first The maximum value of the index , standard matrix is ​​obtained by normalization ,in, ; is the jth index of the i-th air conditioner; S34. Use K-means++ algorithm to obtain the best air conditioning load clustering results.

2. The air conditioning load aggregation method considering user response according to claim 1, characterized in that: The method for obtaining the best air conditioning load clustering result using the K-means++ algorithm described in step S34 is specifically as follows: S341, from the standard matrix Randomly select a sample from As the first initial cluster center , and Represented as the values ​​of the first and second features of the initial cluster center respectively; S342. Confirm the second initial cluster center according to the roulette method , according to the Euclidean norm principle, the remaining sample data With the existing cluster center The distance between them is: （7） S343. The probability of each sample data being selected as the next cluster center is expressed as: （8） Based on this, the roulette method is used to assign the 3rd to nth initial cluster centers Select them separately; S344, the first Sample data According to the principle of minimum Euclidean distance, the corresponding cluster center is assigned. The calculation formula of Euclidean distance is: （9） In formula (9), , and They are The first and second indicators of each air conditioner; S345. Calculate the mean of the samples in each class as the new cluster center , , … They are calculated in the first iteration. The center of the cluster; S346. Repeat the above iterative calculation until the convergence condition is met: （10） In formula (10), and They are Second and The cluster centers calculated by the iteration; S347, output the number of clusters of air conditioning loads in each group and clustering results.

3. A device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the air conditioning load aggregation method considering user response as described in any one of claims 1 to 2, and the processor is configured to execute the program stored in the memory.

4. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the air conditioning load aggregation method considering user response according to any one of claims 1 to 2 are executed.

Images