Off-grid photovoltaic air conditioner adaptive control system and method based on rolling self-learning

Abstract

The invention relates to an off-grid photovoltaic air conditioner self-adaptive control system and method based on rolling self-learning, and aims to solve the problems of low power supply reliability, large battery loss, inflexible temperature control, weak adaptability and the like caused by poor matching between an existing off-grid photovoltaic system and an air conditioner load. The method mainly comprises the steps that parameters are collected in real time, loads are calculated, and air conditioning unit operation frequency bands of the off-grid system are dynamically divided based on the temperature difference; constructing a multi-target model for guaranteeing power supply, prolonging the service life of the battery and optimizing the thermal comfort of the off-grid system, and adaptively adjusting the target by utilizing a dynamic weight coefficient; the optimal sequence is solved through rolling time window optimization, and the off-grid regulation and control strategy is adjusted in real time in combination with temperature gradient feedback. According to the method, the energy dynamic matching efficiency in the off-grid supply and demand system can be improved, power supply is guaranteed, fine adjustment of temperature control is achieved, harmful charging and discharging are limited based on state optimization, loss is reduced, the service life is prolonged, and compared with a traditional regulation and control strategy, the method has remarkable advantages in reliability, comfort and economical efficiency.

Description

Technical Field

[0001] This invention relates to the field of air conditioning technology, and in particular to an adaptive control system and method for off-grid photovoltaic air conditioning based on rolling self-learning. Background Technology

[0002] With the rapid development of renewable energy technologies and the continuous expansion of application scenarios, off-grid photovoltaic air conditioning systems are receiving increasing attention due to their unique advantages in remote areas, areas without grid coverage, and emergency power supply scenarios. These systems directly drive the air conditioning load through photovoltaic power generation, supplemented by energy storage devices (such as batteries) to smooth power fluctuations and ensure continuous power supply, demonstrating significant environmental benefits and application value.

[0003] However, existing off-grid photovoltaic air conditioning systems still face many technical challenges in actual operation, which seriously restrict their performance, reliability, and user experience. These challenges are mainly reflected in the following aspects:

[0004] (1) Poor dynamic matching of energy supply and demand: Photovoltaic power generation is affected by factors such as solar radiation intensity and weather changes, exhibiting significant intermittency and fluctuation; while air conditioning load changes dynamically with indoor and outdoor ambient temperatures and user-set demands. Existing systems lack an efficient real-time power coordination mechanism, making it difficult to achieve a balance between photovoltaic power generation P(t) and energy storage charging and discharging power P. bat (t) and air conditioning load power P load (t) Precise dynamic matching among the three. This often leads to insufficient power supply reliability, resulting in power shortages (supply falling short of demand) or energy waste (supply exceeding demand), and a high risk of system power outages.

[0005] (2) Inefficient lifespan management of energy storage systems: Existing energy storage systems (especially batteries) typically employ simple and fixed charging and discharging strategies, lacking refined and adaptive management of battery state of health (SOH) and state of charge (SOC). Frequent high-current charging and discharging operations, particularly high-current discharge at low SOC(t) or improper charging and discharging at extreme SOC, significantly accelerate battery aging and capacity decay, resulting in high system operation and maintenance costs.

[0006] (3) Inflexible temperature control and poor comfort: Most existing systems use fixed air conditioning control strategies (such as fixed compressor frequency and fan speed setpoints or limited range of variation), and cannot adjust according to indoor temperature T. in (t) and the set temperature T set Real-time deviation of (t)|T in (t)-T set (t)|and its changing trend Dynamic and fine adjustment is carried out. This results in large indoor temperature fluctuations, slow response to load mutations (such as personnel entering and exiting, sudden weather changes), long time required to restore the set temperature, poor user temperature control experience, and difficult to guarantee comfort.

[0007] (4) Poor adaptability to complex working conditions: off-grid system operating environment is complex and changeable (such as severe photovoltaic power fluctuation ΔP(t), air conditioning load mutation, battery SOC(t) state limitation, etc.). Existing control methods are often based on pre-set fixed rules or parameters, lack self-learning and dynamic adjustment ability, and are difficult to effectively cope with these complex and changeable working conditions, so the system stability and robustness need to be improved.

[0008] In summary, the existing off-grid photovoltaic air conditioning system has obvious defects in energy dynamic matching efficiency, battery life management, user temperature control experience and adaptability to complex working conditions, and an intelligent and adaptive control method is needed to improve its overall performance and reliability.

[0009] The information disclosed in this BACKGROUND section is only intended to deepen the understanding of the background of the present disclosure, and should not be regarded as acknowledging or implying in any form that this information constitutes prior art known to those skilled in the art. SUMMARY

[0010] The purpose of the present application is to provide an off-grid photovoltaic air conditioner adaptive control system and method based on rolling self-learning, aiming to solve the problems of low photovoltaic power generation and air conditioning load matching efficiency, short energy storage system life, inflexible temperature control adjustment and poor adaptability to complex working conditions in the existing off-grid photovoltaic air conditioner system.

[0011] According to one aspect of the present disclosure, an off-grid photovoltaic air conditioner adaptive control method based on rolling self-learning is provided, mainly comprising:

[0012] Real-time acquisition of photovoltaic power generation P(t), energy storage state of charge SOC(t), energy storage charge and discharge power P bat (t), indoor and outdoor temperature and humidity T in (t), T out (t), H in (t), H out (t), indoor temperature change rate and air conditioner set temperature T set (t);

[0013] Based on the temperature difference between the indoor temperature T in (t) and the set temperature T set (t), the operating frequency band of the compressor and the fan is dynamically divided;

[0014] A multi-objective optimization function is constructed for the objectives of power continuity, energy storage life, and temperature control comfort:

[0015]

[0016] wherein F1(t) is a power supply and demand balance function, F2(t) is a battery loss cost function, and F3(t) is a temperature deviation function; F 1,max (t), F 2,max (t), and F 3,max (t) are respectively the maximum allowable power gap set by the user, the maximum loss cost per day, and the upper limit of the temperature deviation tolerance; and α(t), β(t), and γ(t) are dynamic weight coefficients that are adaptively adjusted in response to the state of charge of the energy storage and the temperature control demand, and satisfy a normalization constraint.

[0017] An optimal control sequence is solved based on a rolling optimization time window, and the compressor frequency, fan speed, and energy storage charge and discharge instructions are output.

[0018] In combination with real-time feedback of the indoor temperature rate of change, the compressor frequency and fan speed are dynamically adjusted.

[0019] In some embodiments of the present disclosure, the adjustment of the dynamic weight coefficients includes:

[0020] When the photovoltaic power decreases and the state of charge of the energy storage is higher than a preset threshold, the power continuity weight α(t) is increased;

[0021] When the state of charge of the energy storage is lower than a preset threshold, the battery life weight β(t) is increased to limit the charge and discharge current;

[0022] When the temperature difference exceeds a threshold and the temperature rate of change is lower than a threshold, the temperature control comfort weight γ(t) is increased.

[0023] In some embodiments of the present disclosure, the dynamic adjustment includes:

[0024] According to the real-time change direction and amplitude of the indoor temperature rate of change, the compressor frequency and fan speed are adjusted by a preset proportion;

[0025] When the temperature difference is stabilized within an allowable range, a low-frequency operation mode is maintained.

[0026] In some embodiments of the present disclosure, based on |T in (t) - T set (t)|, the operation frequency bands of the compressor and the fan are divided:

[0027] If |T in (t) - T set (t)| is less than a preset minimum temperature difference threshold, the compressor and the fan are adjusted to operate in a low-frequency band.

[0028] If |T in (t)-T set (t)| is between the preset maximum and minimum temperature difference threshold, the compressor and the fan are adjusted to operate in a middle frequency band;

[0029] If |T in (t)-T set (t)| is greater than the preset maximum temperature difference threshold, the compressor and the fan are adjusted to operate in a high frequency band.

[0030] According to another aspect of the present disclosure, there is provided an off-grid photovoltaic air conditioner control system, comprising:

[0031] A data acquisition module is configured to acquire photovoltaic power P(t), energy storage state of charge SOC(t), indoor and outdoor temperature and humidity T in (t), T out (t), H in (t), H out (t) and temperature change rate

[0032] A frequency band decision module is configured to dynamically divide the compressor and fan operating frequency band based on the temperature difference between the indoor set temperature T set (t) and the measured temperature;

[0033] An optimal control module is configured to execute the adaptive control method described above to output the compressor frequency, fan speed and energy storage charge and discharge instructions;

[0034] An execution module is configured to drive the air conditioning unit and the energy storage system to execute the instructions.

[0035] The present disclosure provides an off-grid photovoltaic air conditioner adaptive control method based on rolling self-learning, which effectively overcomes the defects in the above background art and achieves significant beneficial technical effects, specifically in the following aspects:

[0036] 1. Significantly improving the dynamic matching efficiency of energy supply and demand and power supply reliability:

[0037] (1) Technical means: by constructing a multi-objective optimization function, and dynamically coordinating the power distribution of photovoltaic power generation, energy storage charge and discharge and air conditioning load based on real-time data (P(t), Pbat(t), Pload(t), SOC(t), ΔP(t)). Introducing adaptive weight coefficient α(t) for flexible adjustment of the priority of power supply continuity in optimization.

[0038] (2) Technical effects:

[0039] ① Precise matching: more accurate real-time matching of power supply and demand, maximum allowable power gap F1,max (t) is within the allowable range.

[0040] ②Significantly reduce imbalance: Compared with traditional fixed strategies, the present application significantly reduces the number of power supply and demand imbalance scenarios.

[0041] ③Enhance power supply continuity: At critical moments when photovoltaic power drops (ΔP(t) < 0) and energy storage still has a surplus (SOC(t) > 30%), the system automatically increases the weight coefficient α(t) to 0.4-0.6, prioritizing air conditioner power supply. This mechanism significantly reduces the risk of system power failure in off-grid operation scenarios, significantly improving power supply reliability.

[0042] 2. Achieve fine temperature control adjustment, significantly improve user comfort experience:

[0043] (1) Technical means: Adopt rolling time window optimization (initial window ΔT = 30 min, step Δt = 5 min) strategy, and combine with real-time feedback of indoor temperature change rate to dynamically adjust the core components of the air conditioner: adaptively adjust the compressor frequency and fan speed within a wide range (compressor frequency 15-120 Hz, fan speed 300-1500 rpm).

[0044] (2) Technical effects:

[0045] ①High-precision steady-state control: When the room temperature is close to the set value (|T in (t)-T set (t)|≤0.5℃), the system automatically switches to low-frequency / low-speed section operation, stabilizes temperature fluctuations within the allowable deviation range, and maintains a highly comfortable environment.

[0046] ②Fast dynamic response: When the indoor and outdoor temperature difference suddenly changes, resulting in |T in (t)-T set (t)| exceeding 3℃, the system quickly enters high-frequency / high-speed section operation, significantly shortening the time required for the room temperature to return to the set value compared to traditional fixed strategies, and quickly eliminating discomfort.

[0047] ③Intelligent follow-up changes: The system can adjust the operating parameters in real time according to the temperature change trend (for example, when , moderately increase the compressor frequency and fan speed by about 10%), achieve smoother and more actual demand-oriented temperature transition, and significantly improve user temperature control comfort experience.

[0048] 3. Optimize energy storage life management, significantly reduce operation and maintenance costs:

[0049] (1) Technical means: Implement the adaptive weight adjustment strategy based on SOC(t), introduce the battery loss cost term F 2,max (t) into the optimization function, and optimize the charging and discharging sequence using reinforcement learning. Specifically, when the battery SOC(t) is lower than 30%, the corresponding weight coefficient β(t) is automatically increased to 0.3-0.5.

[0050] (2) Technical effects:

[0051] ① Suppress harmful discharge: In the low SOC(t)<30% state, the increased β(t) weight forcibly limits the large current discharge operation, avoiding deep damage to the battery.

[0052] ② Optimize charging and discharging behavior: Combine the reinforcement learning optimized charging and discharging sequence to guide the battery to work in a more healthy and efficient charging and discharging interval.

[0053] ③ Effectively prolong the life: The above measures significantly reduce the operation that causes the battery to accelerate aging, effectively control the actual single-day loss cost within the allowed range. Compared with the traditional strategy, the present application greatly reduces the battery loss rate and the long-term operation and maintenance cost of the system.

[0054] The present application constructs a self-learning and adaptive closed-loop control system through core means such as "dynamic coordination optimization", "rolling time window and real-time feedback", "adaptive weight adjustment" and "reinforcement learning optimization". The system organically and uniformly solves the core defects of low energy matching efficiency, poor battery life management, inflexible temperature control and weak working condition adaptability in the prior art, realizes the comprehensive technical effects of significantly enhanced power supply reliability, greatly improved user temperature control comfort, effectively prolonged storage system life and optimized overall operation economy, and greatly promotes the practicality and high performance development of off-grid photovoltaic air conditioning systems. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The flowchart of the off-grid photovoltaic air conditioner adaptive control method based on rolling self-learning in an embodiment of the present disclosure.

[0056] Figure 2 The indoor temperature stability comparison chart under two control strategies in an embodiment of the present disclosure.

[0057] Figure 3 The actual single-day loss cost comparison chart of the energy storage under two control strategies in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0058] In order to better understand the technical scheme of the present application, the above technical scheme will be described in detail below in combination with the drawings of the specification and specific embodiments.

[0059] Example one

[0060] The example discloses an off-grid photovoltaic air conditioner adaptive control method based on rolling self-learning (see Figure 1 ), comprising the following steps:

[0061] 1. Real-time acquisition of photovoltaic power P(t), energy storage system state of charge SOC(t), energy storage charge and discharge power P bat (t), real-time detection of indoor and outdoor temperature and humidity T in (t), T out (t), H in (t), H out (t), indoor temperature gradient air conditioner set temperature T set (t), and calculation of air conditioner real-time load demand P load (t) based on indoor and outdoor temperature and humidity.

[0062] 2. According to |T in (t)-T set (t)| size, the running frequency band of the compressor and the fan is divided:

[0063] If |T in (t)-T set (t)| is less than the preset minimum temperature difference threshold, the compressor and the fan are adjusted to operate in the low frequency band;

[0064] If |T in (t)-T set (t)| is between the preset maximum and minimum temperature difference threshold, the compressor and the fan are adjusted to operate in the middle frequency band;

[0065] If |T in (t)-T set (t)| is greater than the preset temperature difference maximum temperature difference threshold, the compressor and the fan are adjusted to operate in the high frequency band.

[0066] 3. A multi-objective collaborative optimization model is constructed, and a multi-objective optimization function is established with power reliability, energy storage life and user temperature control demand as targets:

[0067]

[0068] Wherein, F1(t) is a function of P(t), P bat (t), P load (t), F 1,max (t), F 2,max (t), F 3,max(t) represents the maximum allowable power deficit, maximum daily loss cost, and temperature deviation tolerance limit set by the user, respectively; α(t), β(t), and γ(t) are dynamic weighting coefficients that are adaptively adjusted according to SOC(t) and user temperature control requirements, and satisfy the normalization constraint α(t) + β(t) + γ(t) = 1.

[0069] In the above multi-objective optimization function, the dynamic weight coefficient adjustment rules are as follows:

[0070] When ΔP(t) < 0 and SOC(t) is greater than the preset minimum power threshold, increase α(t) to ensure power supply;

[0071] When SOC(t) is less than the preset minimum charge threshold, β(t) is increased to limit high current discharge.

[0072] When |T in (t)-T set (t)| is greater than the preset minimum temperature difference threshold and When the change is less than the preset minimum temperature gradient change threshold, γ(t) is increased to prioritize meeting the user's temperature control needs.

[0073] 4. Set the initial system running time reference point t k Determine the initial optimization step size Δt, in [t k ,t k The internal calculations for the air conditioner compressor frequency f(k), indoor fan speed v(k), and energy storage charging / discharging power P are performed within the +Δt] function. bat The optimal control sequence of (k) is obtained through... Feedback-based dynamic adjustment of compressor and fan control strategies:

[0074] like If the change is less than the preset minimum change threshold, the compressor frequency and fan speed will increase by 10%.

[0075] like If the change is between the preset maximum and minimum change thresholds, the compressor frequency and fan speed will remain unchanged.

[0076] like If the change exceeds the preset maximum change threshold, the compressor frequency and fan speed will be reduced by 10%.

[0077] When |T in (t)-T set (t)|The temperature remains stable within the allowable deviation range of the set temperature, and this mode is maintained during low-frequency operation.

[0078] Example 2: Application Case

[0079] 1. Scene Description:

[0080] Location: Zhengzhou, Henan Province; Time period: typical summer day (24 hours); Solar intensity: 0-1000 W / m 2 ; Outdoor temperature: 22-36℃; Air conditioning set temperature: 26℃.

[0081] 2. System configuration:

[0082] Photovoltaic power generation unit: single-crystal silicon photovoltaic panel (peak power 5kW, open-circuit voltage 48V); Energy storage unit: lithium iron phosphate battery pack (capacity 20kWh, rated voltage 48V); Air conditioning unit: off-grid direct-current frequency conversion air conditioner (cooling capacity 2.65kW); Sensor module: photovoltaic power sensor, temperature and humidity sensor, energy storage current / voltage sensor; Central controller: embedded microprocessor with built-in optimization algorithm and reinforcement learning module.

[0083] 3. Implementation steps (1) Data collection: every 1min collects: photovoltaic power generation power P(t), energy storage state of charge SOC(t), energy storage charge and discharge power P bat (t), indoor and outdoor temperature and humidity T in (t), T out (t), H in (t), H out (t), indoor temperature change rate and air conditioning set temperature T set (t).

[0084] (2) Dynamic frequency range division

[0085] if ΔT: = |T in (t) - T set (t) |:

[0086] if ΔT≤0.5℃:freq_range="low frequency" (15-50Hz)

[0087] elif 0.5℃<ΔT≤3℃:freq_range="medium frequency" (50-85Hz)

[0088] else:freq_range="high frequency" (85-120Hz) # preset threshold can be configured.

[0089] (3) Multi-objective optimization function

[0090] A multi-objective optimization function is established with power supply reliability, energy storage life and user temperature control demand as targets:

[0091]

[0092] Dynamic weight adjustment rule: a(t) = 0.4-0.6 when ΔP(t) < 0 & SOC(t) > 30%; β(t) = 0.3-0.5 when SOC(t) < 30%;

[0093] γ(t) = 0.6-0.8 when

[0094] (4) Rolling optimization and feedback

[0095] Time window: ΔL = 30min, step Δt = 5min

[0096] Gradient feedback adjustment:

[0097]

[0098] compressor_freq* = 1.1 # increase 10% output

[0099]

[0100] compressor_freq* = 0.9 # decrease 10%

[0101] (5) Verification experiment and effect

[0102] The comparison of the traditional control method and the control method of the application is shown in Table 1.

[0103] Table 1 Comparison of indexes of two methods

[0104]

[0105] The index calculation in the table is as follows:

[0106] ① Indoor temperature stability (fluctuation range)

[0107] Fluctuation range = ±max |T in (t) - T set (t) |, wherein: T in (t) is the real-time indoor temperature; T set (t) is the air conditioner set temperature.

[0108] Calculation logic: within 24 hours of test period, T in (t) is collected every 1 min, the absolute value of temperature difference |T in (t) - T set (t) | of all time is calculated, and the maximum value is taken as the boundary of the fluctuation range.

[0109] Traditional fixed strategy: the traditional strategy adopts fixed compressor frequency (50Hz) and fan speed (800rpm), which cannot be dynamically adjusted according to temperature difference. For example,Figure 2 As shown, the maximum temperature difference is calculated to be 1.2℃, so the fluctuation range is ±1.2℃.

[0110] The control method of this invention: based on dynamic frequency band division according to temperature difference ( Low-frequency operation Intermediate frequency operation, (High-frequency operation), combined with temperature change rate Real-time feedback and adjustments. For example... Figure 2 As shown, the calculated maximum deviation is 0.5℃, therefore the fluctuation range is ±0.5℃.

[0111] ②Rest time at room temperature

[0112] The formula for calculating room temperature recovery time is as follows:

[0113]

[0114] Where: T0 is the initial temperature (in this example, the initial temperature is 29℃); T set The set temperature is 26℃ in this example; C is the effective heat capacity of the room (85kJ / ℃ in this example); η is the frequency band trigger factor (0.7 in the traditional example, 1 in this example); P max This is the maximum cooling capacity of the air conditioner (in this example, the maximum cooling capacity of the air conditioner is 2.65KW); This represents the rate of temperature change (0.3 in this example). Q is the rate of change feedback gain (0.5 in this example); Q is the real-time heat load (approximately 1.8 kW in this example).

[0115] Therefore, we can calculate:

[0116]

[0117] ③ Actual daily loss cost of energy storage

[0118] The formula for calculating the actual daily loss cost of energy storage is as follows:

[0119]

[0120] Where: N 正常 The number of normal charge-discharge cycles per day (SOC(t) within the range of 30%-80%); N 低soc For the number of high-current charge-discharge cycles per day (SOC(t) < 30%); C 电池总造价 The total purchase cost of the energy storage battery (in this example, the lithium iron phosphate battery pack is calculated at 24,000 yuan); N 总循环寿命The total cycle number of the battery is designed (6000 cycles in this example).

[0121] Then, the air conditioner energy consumption is 2.65kW*24h=63.6kWh, the photovoltaic supplement is 5kW*2h+1.25kW*8h=22.5kWh, the battery supplement is 63.6kWh-22.5kWh=41.1kWh, and the cycle number of the battery is 41.1kWh / (0.8*20kWh)≈2.57 times. Figure 3

[0122] As can be seen, the traditional fixed strategy has no SOC(t) protection mechanism, and the large current charging and discharging is still allowed when the SOC(t) is not in the range of 30%-80%. Calculation shows that (2*1+1.5*1)*4=(2+2.5)*4=14yuan / day.

[0123] The control method of the application has the battery life weight β(t) dynamically increased to 0.3-0.5 when the SOC(t)<30%, the charging and discharging current is limited, and the SOC(t) loss is reduced. Calculation shows that (1.8*1+0.3*1.5)*4=(1.8+0.45)*4=9yuan / day.

[0124] Although some preferred embodiments of the present application have been described, those skilled in the art can make further changes and modifications to these embodiments once they know the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including all the preferred embodiments and all the changes and modifications falling within the scope of the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the inventive concept. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.​

Claims

1. A self-adaptive control method for off-grid photovoltaic air conditioner based on rolling self-learning, characterized in that, The application relates to an off-grid air conditioner system, which comprises the following: Real-time acquisition of photovoltaic power P(t), energy storage state of charge SOC(t), energy storage charge and discharge power P bat (t), indoor and outdoor temperature and humidity T in (t), T out (t), H in (t), H out (t), indoor temperature change rate and air conditioner set temperature T set (t); Based on the indoor temperature T in (t) and the set temperature T set The temperature difference (t) dynamically divides the operating frequency bands of the compressor and the fan; A multi-objective optimization function is constructed to target off-grid system power continuity, energy storage life and temperature control comfort: Wherein, F1(t) is a power supply and demand balance function, F2(t) is a battery loss cost function, and F3(t) is a temperature deviation function; F 1,max (t) is a user-set maximum allowable power gap, F 2,max (t) is a single-day maximum loss cost, and F 3,max (t) is a temperature deviation tolerance upper limit; α(t), β(t), and γ(t) are dynamic weight coefficients that are adaptively adjusted in response to the state of charge of the energy storage and the temperature control demand and satisfy a normalization constraint. An optimal control sequence is solved based on a rolling optimization time window, and compressor frequency, fan speed and energy storage charging and discharging instructions are outputted; In combination with real-time feedback of the indoor temperature change rate, the compressor frequency and the fan speed are dynamically adjusted.

2. The off-grid photovoltaic air conditioner adaptive control method according to claim 1, characterized in that, The adjustment of the dynamic weight coefficient comprises: When photovoltaic power decreases and the energy storage state of charge is higher than a preset threshold, the off-grid power continuity weight alpha (t) is increased; When the energy storage state of charge is lower than a preset threshold, the battery life weight beta (t) is increased to limit the charging and discharging current; When the temperature difference exceeds a threshold and the temperature change rate is lower than a threshold, the temperature control comfort weight gamma (t) is increased.

3. The off-grid photovoltaic air conditioner adaptive control method according to claim 1, characterized in that, The dynamic adjustment comprises: According to the real-time change direction and amplitude of the indoor temperature change rate, the compressor frequency and the fan speed are adjusted at a preset ratio; When the temperature difference is stabilized within an allowable range, a low-frequency operation mode is maintained.

4. The off-grid photovoltaic air conditioner adaptive control method according to claim 1, characterized in that, Based on |T in (t)-T set (t) | Size division compressor and fan operating frequency band: if |T in (t) - T set (t) | is less than a preset minimum temperature difference threshold, adjusting the compressor and the fan to operate in a low frequency band. if |T in (t) - T set (t) is between the preset maximum and minimum temperature difference threshold, the compressor and the fan are adjusted to operate in the medium frequency band. if |T in (t) - T set (t) | is greater than a preset temperature difference maximum temperature difference threshold value, the compressor and the fan are adjusted to operate in a high frequency band.

5. An off-grid photovoltaic air conditioner adaptive control system, characterized in that, The application further relates to an off-grid air conditioner system, which comprises the following: A data acquisition module is configured to acquire the off-grid photovoltaic power P(t), the energy storage state of charge SOC(t), the indoor and outdoor temperature and humidity T in (t), T out (t), H in (t), H out (t) and the temperature change rate The frequency band decision module is configured to determine the indoor setting temperature T set (t) dynamically divides the compressor and the fan operation frequency band according to the temperature difference between the measured temperature and the indoor setting temperature. An optimization control module is used for executing the control method in any one of claims 1-3, and compressor frequency, fan speed and energy storage charging and discharging instructions are outputted; An execution module is used for driving an air conditioner unit and an energy storage system to execute the instructions.

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