Air conditioner control method based on user electricity charge budget, air conditioner and storage medium
The air conditioning temperature control strategy generated by the multi-objective optimization model solves the problem of excessive air conditioning electricity costs, realizes rigid control of electricity budget and comfort optimization, and meets users' electricity budget needs.
Patent Information
- Application Number
- CN202511899912.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-16
- Publication Date
- 2026-02-24
AI Technical Summary
The existing air conditioning control system cannot respond to users' electricity budget requirements, resulting in overspending and affecting user trust.
By acquiring real-time environmental and electricity price information, the optimal temperature control strategy is generated using a multi-objective optimization model. Combined with user electricity budget, energy consumption, and comfort constraints, the air conditioning operation strategy is dynamically adjusted to ensure that electricity costs remain within the expected range.
It achieves rigid control over electricity budget, reduces total electricity expenditure, improves the controllability and comfort of electricity consumption, and meets users' personalized needs.
Smart Images
Figure CN121557580A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of air conditioning technology, specifically to an air conditioning control method based on a user's electricity budget, an air conditioner using the user's electricity budget-based air conditioning control method, and a computer-readable storage medium using the user's electricity budget-based air conditioning control method. Background Technology
[0002] With increasing volatility in energy prices, users' demand for controllable and predictable electricity costs is becoming increasingly prominent. As the main electricity-consuming device in summer, air conditioning accounts for more than 30% of the total electricity consumption in households and office buildings, making it a key element in achieving energy conservation and consumption reduction.
[0003] Current mainstream air conditioning control systems still rely solely on user-set temperatures or indoor temperature feedback for control, achieving basic temperature control through start / stop or frequency conversion adjustments, with operating strategies largely based on preset, fixed logic. While some smart air conditioners offer energy-saving modes or timer functions, their core focus remains on comfort, lacking a linkage mechanism with real-time electricity pricing information. For example, continuously running the air conditioner during peak electricity price periods (such as 12:00–18:00) may maintain indoor temperature but leads to a significant increase in electricity bills, failing to achieve a synergistic optimization of comfort and economy.
[0004] One existing air conditioning control method calculates the heat buffer potential by acquiring building information model data and indoor environmental data, maximizing the heat buffer time window to reduce the number of air conditioning start-ups and shutdowns, thereby reducing power consumption. By incorporating comfort demand ranges as one of the game objectives and comprehensively considering indoor environmental data and weather forecast data, it can ensure occupant comfort. Simultaneously, it calculates electricity cost weights based on electricity price data and weather forecast data to minimize total electricity costs and reduce the pressure on the power grid.
[0005] However, the existing system cannot respond to users' rigid budget requirements for electricity bills. Users cannot predict their total electricity expenditure. When electricity prices suddenly rise, outdoor temperatures are high, or air conditioners are aging, the system continues to operate according to the original strategy, which can easily lead to electricity bills far exceeding expectations and trigger a crisis of user trust.
[0006] Therefore, it is necessary to consider more optimized air conditioning control methods. Summary of the Invention
[0007] The first objective of this invention is to provide an air conditioning control method based on the user's electricity budget that enables rigid control of electricity budget and solves the problem of electricity budget overruns.
[0008] The second objective of this invention is to provide an air conditioner that enables rigid control of electricity budget and solves the problem of electricity budget overruns.
[0009] A third objective of this invention is to provide a computer-readable storage medium that enables rigid control of electricity budget and addresses the issue of electricity budget overruns.
[0010] To achieve the aforementioned first objective, the air conditioning control method based on user electricity budget provided by the present invention includes: after the air conditioner enters the temperature adjustment operation state, acquiring real-time environmental parameters, user-set parameters, electricity price information within a preset future time period, and weather information within a preset future time period, wherein the user-set parameters include user electricity budget, user preference mode, and temperature comfort range; determining the weight coefficients of each parameter in the objective function according to the user preference mode, wherein the parameters of the objective function include electricity cost, energy consumption, and comfort deviation; determining the constraints on electricity cost, energy consumption, and comfort deviation according to the user electricity budget and temperature comfort range; generating the optimal temperature adjustment strategy within the preset future time period using a preset multi-objective optimization model based on environmental parameters, electricity price information, weather information, objective function, and constraints; and controlling the air conditioner operation according to the optimal temperature adjustment strategy.
[0011] As can be seen from the above scheme, the air conditioning control method based on user electricity budget of the present invention uses the user's electricity budget as the core constraint and a multi-objective optimization model to plan the air conditioning operation strategy for a preset duration in the future. This fundamentally avoids electricity cost overruns caused by factors such as electricity price fluctuations and sudden weather changes, ensuring that the user's electricity expenditure is always within the expected range. At the same time, the weights of the objective function are dynamically adjusted according to the user's preference mode. For example, in the economy mode, the user prioritizes ensuring controllable electricity costs; in the comfort mode, the user prioritizes reducing the perceived temperature deviation, achieving comfort within the budget.
[0012] In a further proposed scheme, the objective function is: Minimize = α × electricity cost + β × energy consumption + γ × comfort deviation; where α, β, and γ are weighting coefficients, and α + β + γ = 1.
[0013] Therefore, by setting the weighting coefficients, the bias of the temperature regulation strategy can be controlled, thereby meeting the user's needs.
[0014] In a further proposed scheme, the constraint on electricity costs is: Σ(power(t)×electricity price(t)×Δt)≤system electricity cost budget, where power(t) is the compressor power at time t, electricity price(t) is the electricity price at time t, Δt is the time step, and the system electricity cost budget is the electricity cost budget at time t predicted by the preset multi-objective optimization model.
[0015] Therefore, the electricity cost constraint breaks down the user's overall electricity cost budget into time-period budgets for each time step. By controlling the sum of the product of the compressor power and the corresponding electricity price for each time period, it ensures that the total electricity cost of air conditioning operation does not exceed the user's expectations.
[0016] In a further scheme, the energy consumption constraint is: f_min≤frequency(t)≤f_max, where frequency(t) is the compressor frequency at time t, f_min is the preset minimum frequency, and f_max is the preset maximum frequency.
[0017] Therefore, by defining the operating frequency range of the compressor through energy consumption constraints, we can prevent the basic temperature control requirements from being unmet due to excessively low frequency, and avoid the surge in equipment energy consumption and increased component wear due to excessively high frequency, thus achieving controllable energy consumption.
[0018] In a further scheme, the constraint condition for the comfort deviation is: T_min≤T_actual(t)≤T_max, where T_actual(t) is the indoor ambient temperature at time t, T_min is the lowest temperature in the preset human comfort range, and T_max is the highest temperature in the preset human comfort range.
[0019] Therefore, the constraints of comfort deviation ensure that the generated temperature regulation strategy will not sacrifice the user's basic comfort in order to save electricity bills, and avoid the indoor temperature from exceeding the range that the human body can tolerate.
[0020] In a further proposed solution, the step of generating the optimal temperature regulation strategy for a future preset time period using a preset multi-objective optimization model based on environmental parameters, electricity price information, weather information, objective function, and constraints includes: generating the temperature regulation strategy corresponding to each electricity price stage within the future preset time period.
[0021] Therefore, developing differentiated strategies for different electricity price periods allows for the full utilization of low-priced electricity during off-peak hours to store cooling or heating capacity, reducing high-load compressor operation during peak hours and enabling refined control of electricity costs from a time perspective. Compared to traditional indiscriminate temperature control, this significantly reduces total electricity costs and ensures that electricity consumption is perfectly matched to time-of-use pricing, avoiding ineffective energy consumption during high-priced periods.
[0022] In a further proposed solution, the operation of the air conditioner is controlled according to the optimal temperature adjustment strategy, which also includes: obtaining the current cumulative electricity cost; if the current cumulative electricity cost is less than the electricity cost threshold corresponding to the current moment, then determining whether the current indoor temperature is not within the preset temperature range; if so, then controlling the air conditioner to perform a comfort enhancement operation.
[0023] Therefore, by obtaining the current accumulated electricity cost, and when there is a budget surplus, users' comfort needs can be further met, thus achieving a balance between electricity cost budget and comfort.
[0024] In a further proposed solution, after determining whether the current indoor temperature is outside the preset temperature range, the solution also includes: if the current indoor temperature is within the preset temperature range, then maintain the current temperature regulation strategy.
[0025] Therefore, when the system detects that the current cumulative electricity cost is lower than the threshold, but the indoor temperature is already within the preset comfort range, directly maintaining the original temperature adjustment strategy can avoid ineffective energy consumption caused by blindly triggering comfort enhancement operations.
[0026] In a further proposed solution, after obtaining the current cumulative electricity cost, the solution also includes: if the current cumulative electricity cost is greater than or equal to the electricity cost threshold corresponding to the current moment, then the preset energy-saving mode is activated.
[0027] Therefore, after obtaining the current cumulative electricity cost in real time, if the current cumulative electricity cost is greater than or equal to the electricity cost threshold corresponding to the current moment, the energy-saving mode will be triggered immediately to curb further overspending on electricity costs from the source and prioritize ensuring that the total budget is not exceeded.
[0028] In a further proposed solution, after controlling the air conditioner's operation according to the optimal temperature adjustment strategy, the solution also includes: acquiring user behavior data and electricity budget execution deviation data of the air conditioner in user preference mode within a preset time period; and using a preset machine learning model to learn the user behavior data and electricity budget execution deviation data to obtain the weight coefficients of each parameter in the objective function corresponding to the next execution of user preference mode by the air conditioner.
[0029] Therefore, the initial objective function weights are only set based on users' explicit preference patterns, failing to cover users' implicit needs in actual use. By learning from user behavior data, the weight coefficients can be continuously aligned with users' actual needs, achieving personalized adaptation. By learning from historical electricity budget execution deviation data, the scenarios in which deviations occur can be accurately identified, and the parameter weights for corresponding time periods can be adjusted accordingly, gradually reducing the budget deviation across all time periods and improving the match between electricity consumption and budget.
[0030] To achieve the second objective of the present invention, the present invention provides an air conditioner including a processor and a memory, the memory storing a computer program, which, when executed by the processor, implements the steps of the above-described air conditioner control method based on the user's electricity budget.
[0031] To achieve the third objective of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a controller, implements the steps of the above-described air conditioning control method based on the user's electricity budget. Attached Figure Description
[0032] Figure 1 This is a flowchart of an embodiment of the air conditioning control method based on user electricity budget of the present invention.
[0033] Figure 2 This is a flowchart illustrating the air conditioner control method based on the user's electricity budget, which controls the operation of the air conditioner according to the optimal temperature adjustment strategy in an embodiment of the present invention.
[0034] Figure 3 This is a flowchart illustrating the process of controlling the air conditioner according to the optimal temperature adjustment strategy in an embodiment of the air conditioner control method based on the user's electricity budget according to the present invention.
[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments. Detailed Implementation
[0036] Various exemplary embodiments of the invention will now be described in detail with reference to the accompanying drawings. The descriptions of the exemplary embodiments are merely illustrative and are in no way intended to limit the invention or its application or use. The invention can be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to make the invention thorough and complete, and to fully express the scope of the invention to those skilled in the art. It should be noted that, unless otherwise specifically stated, the relative arrangement of components and steps, the composition of materials, numerical expressions, and values set forth in these embodiments should be interpreted as merely exemplary and not as limiting.
[0037] The terms "first," "second," and similar words used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different parts. Words such as "including" or "comprising" mean that the element preceding the word encompasses the element listed after it, without excluding the possibility of encompassing other elements. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0038] In this invention, when a specific device is described as being located between a first device and a second device, an intermediary device may or may not be present between the specific device and the first or second device. When a specific device is described as being connected to other devices, the specific device may be directly connected to the other devices without an intermediary device, or it may be not directly connected to the other devices but have an intermediary device.
[0039] All terms used in this invention (including technical or scientific terms) have the same meaning as understood by one of ordinary skill in the art, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art, and not as having an idealized or highly formalized meaning, unless expressly defined herein.
[0040] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.
[0041] Example of an air conditioning control method based on user electricity budget: The air conditioning control method based on user electricity budget of the present invention is an application program in an air conditioner used to control the air conditioner to operate according to the user's electricity budget.
[0042] like Figure 1 As shown in this embodiment, the air conditioning control method based on the user's electricity budget first executes step S1 to determine whether the air conditioner has entered the temperature regulation operation state. When it is necessary to turn on the air conditioner to enter the temperature regulation operation state such as cooling or heating, it can be controlled by a remote control or control panel.
[0043] If the air conditioner has not entered temperature setting mode, continue with step S1 for continuous monitoring. Once the air conditioner enters temperature setting mode, proceed to step S2 to acquire real-time environmental parameters, user-defined parameters, electricity price information for the next preset duration, and weather information for the next preset duration. User-defined parameters include the user's electricity budget, preferred mode, and temperature comfort range. Environmental parameters can be obtained through the air conditioner's sensors and include indoor temperature, indoor humidity, outdoor temperature, and the presence of people. User-defined parameters can be input via an app or remote control. Users can set their electricity budget, preferred mode, and temperature comfort range according to their needs. For example, the user's electricity budget could be "1.4 yuan per night." Preferred modes include energy-saving priority mode, comfort priority mode, and balanced mode, which the user can choose. The temperature comfort range is 23℃ to 27℃. Simultaneously, the system can also connect to the internet to acquire electricity price information and weather information for the next preset duration, which can be set according to user needs.
[0044] After acquiring real-time environmental parameters, user-defined parameters, electricity price information for the next preset time period, and weather information for the next preset time period, step S3 is executed to determine the weight coefficients of each parameter in the objective function based on the user preference mode. The parameters of the objective function include electricity cost, energy consumption, and comfort deviation. In this embodiment, the objective function is: Minimize = α × Electricity Cost + β × Energy Consumption + γ × Comfort Deviation; where α, β, and γ are weight coefficients, and α + β + γ = 1. Different user preference modes result in different weight coefficients for each parameter in the objective function, allowing the objective function to focus on parameters influencing the user preference mode. For example, in the power saving priority mode, α = 0.6, β = 0.3, γ = 0.1; in the comfort priority mode, α = 0.3, β = 0.2, γ = 0.5; and in the balanced mode, α = 0.4, β = 0.3, γ = 0.3. If a user selects the power saving priority mode, it means that electricity cost accounts for 60% of the decision-making weight and is the primary optimization objective.
[0045] After determining the weighting coefficients of each parameter in the objective function, step S4 is executed to determine the constraints on electricity costs, energy consumption, and comfort deviations based on the user's electricity budget and temperature comfort range. To ensure the feasibility of the strategy, constraints on electricity costs, energy consumption, and comfort deviations need to be introduced.
[0046] In this embodiment, the constraints on electricity costs are: Σ(power(t)×electricity price(t)×Δt)≤system electricity cost budget, where power(t) is the compressor power at time t, electricity price(t) is the electricity price at time t, Δt is the time step, and the system budget is the electricity cost budget predicted by the preset multi-objective optimization model at time t. The constraints on energy consumption are: f_min≤frequency(t)≤f_max, where frequency(t) is the compressor frequency at time t, f_min is the preset minimum frequency, and f_max is the preset maximum frequency. The constraints on comfort deviation are: T_min≤T_actual(t)≤T_max, where T_actual(t) is the indoor ambient temperature at time t, T_min is the minimum temperature within the preset human comfort range, and T_max is the maximum temperature within the preset human comfort range. The electricity cost constraints decompose the user's overall electricity cost budget into time-period budgets for each time step. By controlling the sum of the products of compressor power and corresponding electricity price for each time period, the total electricity cost of air conditioning operation is ensured not to exceed the user's expectations. By defining the compressor's operating frequency range through energy consumption constraints, we can prevent situations where the frequency is too low to meet basic temperature control requirements, while avoiding situations where the frequency is too high, leading to a surge in equipment energy consumption and increased component wear, thus achieving controllable energy consumption. Comfort deviation constraints ensure that the generated temperature regulation strategy does not sacrifice basic user comfort to save on electricity bills, preventing indoor temperatures from exceeding the human body's tolerance range.
[0047] After determining the objective function and constraints, step S5 is executed. A pre-defined multi-objective optimization model is used to generate the optimal temperature regulation strategy for a future preset time period based on environmental parameters, electricity price information, weather information, the objective function, and constraints. The pre-defined multi-objective optimization model can employ a lightweight multi-objective optimization algorithm, such as an improved NSGA-II non-dominated sorting genetic algorithm or a particle swarm optimization algorithm. This involves generating a set of random control strategies (e.g., time series of different frequencies, wind speeds, and set temperatures); calculating the objective function value for each strategy; retaining the individual with the smallest objective function value; performing crossover and mutation to generate new strategy combinations; iterating until convergence; and outputting the optimal solution set, selecting the strategy that satisfies the budget constraints and has the lowest total cost. This is a technique well-known to those skilled in the art and will not be elaborated upon here.
[0048] In this embodiment, the step of generating the optimal temperature regulation strategy for a future preset time period using a preset multi-objective optimization model based on environmental parameters, electricity price information, weather information, objective function, and constraints includes: generating temperature regulation strategies corresponding to each electricity price stage within the future preset time period. Developing differentiated strategies for different electricity price stages allows for full utilization of low-priced electricity to store cooling or heating capacity during off-peak hours, reducing high-load compressor operation during peak hours, and achieving refined electricity cost management from a time perspective. Compared to traditional indiscriminate temperature control, this significantly reduces total electricity costs and ensures that electricity consumption perfectly matches time-of-use pricing, avoiding ineffective energy consumption during high-priced periods.
[0049] For example, the temperature adjustment strategies for each electricity price period within a preset future timeframe are as follows: 00:00–08:00 (flat electricity price): Start pre-cooling mode, set compressor frequency to 75%, fan speed to medium, and temporarily lower the set temperature to 23℃; 08:00–12:00 (peak electricity price): Compressor frequency drops to 65%, fan speed is adjusted to low to medium, and the set temperature is raised to 25℃; 12:00–14:00 (peak electricity price): Compressor frequency drops to 50%, fan speed is adjusted to "sleep mode," and the set temperature is raised to 26℃; 14:00–22:00 (peak electricity price): Compressor frequency is maintained at 60%, fan speed is medium, and the set temperature is restored to 24℃; 22:00–24:00 (peak electricity price): The system returns to normal operation, but maintains low power consumption operation based on the advantage of off-peak electricity prices at night.
[0050] After generating the optimal temperature control strategy, step S6 is executed to control the air conditioner's operation according to the optimal temperature control strategy. The air conditioner controls the compressor, fan, and other actuators to perform temperature control operations according to the optimal temperature control strategy.
[0051] In this embodiment, see Figure 2 When the air conditioner is running according to the optimal temperature control strategy, step S11 is also executed to obtain the current accumulated electricity cost. The current accumulated electricity cost can be obtained according to the following formula: Σ(power(t)×electricity price(t)×Δt).
[0052] After obtaining the current accumulated electricity cost, step S12 is executed to determine whether the current accumulated electricity cost is less than the electricity cost threshold corresponding to the current time. The electricity cost threshold is the electricity cost budget corresponding to the current time, which is obtained by predicting the current time through a preset multi-objective optimization model.
[0053] If the current accumulated electricity cost is less than the electricity cost threshold corresponding to the current moment, then step S13 is executed to determine whether the current indoor temperature is not within the preset temperature range. The preset temperature range can be set according to user needs. By obtaining the current accumulated electricity cost, and assuming a budget surplus, the system further determines whether the current indoor temperature is not within the preset temperature range, in order to perform further adjustment and control.
[0054] If the current indoor temperature is not within the preset temperature range, step S14 is executed to control the air conditioner to perform a comfort enhancement operation. This further satisfies the user's comfort needs, balancing electricity costs and comfort, provided there is sufficient budget. When controlling the air conditioner to perform the comfort enhancement operation, the indoor temperature can be accelerated to reach the preset temperature range by increasing the compressor frequency, raising the fan speed, or adjusting the set temperature.
[0055] If the current indoor temperature is within the preset temperature range, proceed to step S15 to maintain the current temperature regulation strategy. When the system detects that the current accumulated electricity cost is lower than the threshold, but the indoor temperature is already within the preset comfort range, it directly maintains the original temperature regulation strategy, which can avoid ineffective energy consumption caused by blindly triggering comfort enhancement operations.
[0056] If, during step S12, the current accumulated electricity cost is greater than or equal to the electricity cost threshold at the current moment, then step S16 is executed to activate the preset energy-saving mode. After obtaining the current accumulated electricity cost in real time, if the current accumulated electricity cost is greater than or equal to the electricity cost threshold at the current moment, the energy-saving mode is immediately triggered to curb further overspending and prioritize ensuring the total budget is not exceeded. When activating the preset energy-saving mode, the air conditioner's energy consumption can be reduced by lowering the compressor frequency, reducing the fan speed, or adjusting the set temperature.
[0057] In this embodiment, see Figure 3 After executing step S6, which controls the air conditioner's operation according to the optimal temperature control strategy, step S21 is further executed to obtain user behavior data and electricity budget execution deviation data of the air conditioner in user preference mode within a preset time period. User behavior data includes the number of times the user manually adjusts the temperature and comfort ratings. Electricity budget execution deviation data can be obtained by comparing the actual electricity cost with the electricity budget.
[0058] After obtaining user behavior data and electricity budget execution deviation data, step S22 is executed. A preset machine learning model is used to learn from the user behavior data and electricity budget execution deviation data to obtain the weight coefficients of each parameter in the objective function corresponding to the next execution of the user preference mode by the air conditioner. Preferably, the preset machine learning model adopts a decision tree model. The initial objective function weights are only set based on the user's explicit preference mode, and the implicit needs of the user in actual use cannot be covered. By learning from user behavior data, the weight coefficients continuously move closer to the user's actual needs, achieving personalized adaptation. By learning from historical electricity budget execution deviation data, the scenarios in which deviations occur can be accurately located, and the parameter weights for corresponding time periods can be adjusted accordingly, gradually reducing the budget deviation range throughout the entire time period, and improving the matching degree between electricity consumption and budget.
[0059] As described above, the air conditioning control method based on user electricity budget of the present invention uses the user's electricity budget as the core constraint and a multi-objective optimization model to plan the air conditioning operation strategy for a preset duration in the future. This fundamentally avoids electricity cost overruns caused by factors such as electricity price fluctuations and sudden weather changes, ensuring that the user's electricity expenditure is always within the expected range. Simultaneously, the objective function weights are dynamically adjusted according to the user's preference pattern. For example, in the economy mode, the user prioritizes ensuring controllable electricity costs; in the comfort mode, the user prioritizes reducing perceived temperature deviations, achieving comfort within the budget.
[0060] Air conditioner example: The air conditioner in this embodiment includes a controller, which executes the steps in the above embodiment of the air conditioner control method based on the user's electricity budget when executing a computer program.
[0061] For example, a computer program can be divided into one or more modules, one or more of which are stored in memory and executed by a controller to complete the present invention. One or more modules can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in an air conditioner.
[0062] An air conditioner may include, but is not limited to, a controller and a memory. Those skilled in the art will understand that an air conditioner may include more or fewer components, or a combination of certain components, or different components; for example, an air conditioner may also include input / output devices, network access devices, buses, etc.
[0063] For example, the controller can be a Central Processing Unit (CPU), or other general-purpose controllers, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose controller can be a microcontroller or any conventional controller. The controller is the control center of the air conditioner, connecting all parts of the air conditioner through various interfaces and lines.
[0064] The memory can be used to store computer programs and / or modules. The controller implements various functions of the air conditioner by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. For example, the memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound receiving function, sound-to-text function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, text data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
[0065] Examples of computer-readable storage media: If the modules integrated into the air conditioner in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the air conditioner control method based on user electricity budget can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the controller, it can implement the steps of the above embodiments of the air conditioner control method based on user electricity budget. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The storage medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in computer-readable media may be appropriately added to or subtracted from the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, computer-readable media may not include electrical carrier signals and telecommunication signals, in accordance with legislation and patent practice.
[0066] It should be noted that the above are only preferred embodiments of the present invention, but the design concept of the invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept also fall within the protection scope of the present invention.
Claims
1. An air conditioning control method based on user electricity budget, characterized in that: include: After the air conditioner enters the temperature adjustment operation state, it acquires real-time environmental parameters, user-set parameters, electricity price information within the future preset time period, and weather information within the future preset time period. The user-set parameters include the user's electricity budget, user preference mode, and temperature comfort range. The weight coefficients of each parameter in the objective function are determined based on the user preference pattern, wherein the parameters of the objective function include electricity cost, energy consumption, and comfort deviation. The constraints on the electricity cost, energy consumption, and comfort deviation are determined based on the user's electricity budget and the temperature comfort range. The optimal temperature regulation strategy for the future preset time period is generated using a preset multi-objective optimization model based on the environmental parameters, electricity price information, weather information, objective function, and constraints. The air conditioner is controlled to operate according to the optimal temperature regulation strategy.
2. The air conditioning control method based on user electricity budget according to claim 1, characterized in that: The objective function is: Minimize = α × electricity cost + β × energy consumption + γ × comfort deviation; In this context, α, β, and γ are all weighting coefficients, and α+β+γ=1.
3. The air conditioning control method based on user electricity budget according to claim 1, characterized in that: The constraints on the electricity cost are as follows: Σ(Power(t)×ElectricityPrice(t)×Δt)≤SystemElectricityBudget, where power(t) is the compressor power at time t, electricity price(t) is the electricity price at time t, Δt is the time step, and the system electricity budget is the electricity budget at time t predicted by the preset multi-objective optimization model.
4. The air conditioning control method based on user electricity budget according to claim 1, characterized in that: The energy consumption constraint is as follows: f_min≤frequency(t)≤f_max, where frequency(t) is the compressor frequency at time t, f_min is the preset minimum frequency, and f_max is the preset maximum frequency.
5. The air conditioning control method based on user electricity budget according to claim 1, characterized in that: The constraint condition for the comfort deviation is: T_min≤T_actual(t)≤T_max, where T_actual(t) is the indoor ambient temperature at time t, T_min is the lowest temperature within the preset human comfort range, and T_max is the highest temperature within the preset human comfort range.
6. The air conditioning control method based on user electricity budget according to claim 1, characterized in that: The steps of generating the optimal temperature regulation strategy for the future preset time period using a preset multi-objective optimization model based on the environmental parameters, the electricity price information, the weather information, the objective function, and the constraints include: Generate temperature regulation strategies corresponding to each electricity price stage within the preset future time period.
7. The air conditioning control method based on user electricity budget according to any one of claims 1 to 6, characterized in that: Controlling the operation of the air conditioner according to the optimal temperature regulation strategy further includes: Get the current accumulated electricity bill; If the current cumulative electricity cost is less than the electricity cost threshold corresponding to the current moment, it is determined whether the current indoor temperature is not within the preset temperature range. If so, the air conditioner is controlled to perform a comfort enhancement operation.
8. The air conditioning control method based on user electricity budget according to claim 7, characterized in that: After determining whether the current indoor temperature is outside the preset temperature range, the following steps are also included: If the current indoor temperature is within the preset temperature range, the current temperature regulation strategy will be maintained.
9. The air conditioning control method based on user electricity budget according to claim 7, characterized in that: After obtaining the current accumulated electricity bill, the process also includes: If the current cumulative electricity cost is greater than or equal to the electricity cost threshold corresponding to the current moment, then the preset energy-saving mode is activated.
10. The air conditioning control method based on user electricity budget according to any one of claims 1 to 6, characterized in that: After controlling the air conditioner to operate according to the optimal temperature regulation strategy, the system further includes: Acquire user behavior data and electricity budget execution deviation data of the air conditioner in the user preference mode within a preset time period; By using a preset machine learning model to learn from the user behavior data and the electricity budget execution deviation data, the weight coefficients of each parameter in the objective function corresponding to the next execution of the user preference mode by the air conditioner are obtained.
11. An air conditioner, comprising a processor and a memory, characterized in that: The memory stores a computer program that, when executed by the processor, implements the steps of the air conditioning control method based on the user's electricity budget as described in any one of claims 1 to 10.
12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the controller, it implements the steps of the air conditioning control method based on the user's electricity budget as described in any one of claims 1 to 10.