WO2024051191A1 - 空调及其控制方法、存储介质 - Google Patents

空调及其控制方法、存储介质 Download PDF

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WO2024051191A1
WO2024051191A1 PCT/CN2023/092755 CN2023092755W WO2024051191A1 WO 2024051191 A1 WO2024051191 A1 WO 2024051191A1 CN 2023092755 W CN2023092755 W CN 2023092755W WO 2024051191 A1 WO2024051191 A1 WO 2024051191A1
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Prior art keywords
temperature
air conditioner
time
control
predicted
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PCT/CN2023/092755
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English (en)
French (fr)
Inventor
董明珠
赵柏扬
倪毅
薛寒冬
傅英胜
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珠海格力电器股份有限公司
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Publication of WO2024051191A1 publication Critical patent/WO2024051191A1/zh

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F24F11/47—Responding to energy costs
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/89—Arrangement or mounting of control or safety devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/50—Air quality properties
    • F24F2110/64—Airborne particle content
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/50—Air quality properties
    • F24F2110/80—Electric charge
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2130/00—Control inputs relating to environmental factors not covered by group F24F2110/00
    • F24F2130/10—Weather information or forecasts

Definitions

  • the present disclosure relates to the technical field of air conditioning control, and in particular to an air conditioner, a control method thereof, and a storage medium.
  • This disclosure proposes an air conditioner, a control method thereof, and a storage medium.
  • the air conditioning control method proposed in this disclosure includes:
  • the first control strategy is used to control the operating parameters of the air conditioner at each adjustment node in the first time scale
  • the second control strategy is used to control the operating parameters of the air conditioner.
  • the first control strategy is used to control the operating parameters of the air conditioner.
  • the first control strategy includes: predicting the terminal temperature according to the predicted outdoor temperature and the indoor preset temperature, and adjusting the operating parameters of the air conditioner according to the predicted terminal temperature.
  • the operating parameters of the air conditioner are adjusted according to the predicted terminal temperature, for example, using a PID method or a fuzzy control method.
  • the outdoor temperature is predicted based on a weather forecast or based on historical weather data.
  • the second control strategy is a strategy to obtain the lowest energy consumption of the air conditioner, and the second control strategy makes the temperature change difference of the terminal temperature satisfy a preset difference range.
  • the terminal temperature includes one of evaporation temperature, condensation temperature, outlet water temperature, return water temperature, outlet air temperature, return air temperature, and radiator temperature at the end of the air conditioner.
  • the air conditioner proposed in this disclosure includes:
  • the data collection module collects the time series of air conditioner operating data
  • the time scale identification module identifies the indoor temperature response time and the terminal temperature response time according to the time series of the operating data, and uses the indoor temperature response time as the time interval of the adjustment node of the first time scale, and uses the terminal temperature response time as the second time scale.
  • the control module uses a first control strategy to control the operating parameters of the air conditioner at each adjustment node in the first time scale, and uses a second control strategy to control the operating parameters of the air conditioner at each adjustment node in the second time scale.
  • control module includes: an end temperature prediction module, used to predict the end temperature, so that the control module controls the operating parameters of the air conditioner according to the predicted end temperature and the first control strategy.
  • control module further includes: a unit actuator control module, configured to control the operating parameters of the air conditioner controlled by the first control strategy according to the second control strategy, so that the temperature change difference of the terminal temperature satisfies Default difference range.
  • the computer-readable storage medium proposed by the present disclosure is used to store a computer program that executes the air-conditioning control method described in the above technical solution when the computer program is run.
  • This disclosure takes into account that the output capacity (cooling capacity/heat) of the air conditioner is mainly determined by the temperature difference between the terminal evaporation temperature or condensation temperature and the indoor ambient temperature. Therefore, by collecting the time series of air conditioner operation data, the air conditioner control method is obtained. Long time scale (first time scale) and short time scale (second time scale). Control the system evaporation/condensation temperature of the multi-split air conditioner on the long time scale (first time scale) to achieve the relationship between air conditioning output capacity and building cooling.
  • the matching of heat load ensures constant indoor temperature and reduces unit energy waste, improving the thermal comfort and energy saving of air conditioners; by fine-tuning the unit actuator of multi-split air conditioners on a short time scale (second time scale), The combination of each actuator is optimized to further reduce the operating energy consumption of multi-split air conditioners.
  • the present disclosure does not directly control the actuator of the multi-line air conditioner, but performs fine-tuning control of the actuator while maintaining a stable evaporation/condensation temperature of the system, it improves the operational reliability in the multi-line air conditioning control process.
  • Figure 1 is a primary flow diagram of some embodiments of the present disclosure.
  • FIG. 2 is a detailed flow diagram of some embodiments of the present disclosure.
  • Figure 3 is a structural block diagram of some embodiments of the present disclosure.
  • the air conditioning control method proposed in this disclosure includes the following steps.
  • Step 1 Collect the time series of air conditioner operating data.
  • the time series of air conditioner operating data is essentially the air conditioner operating data, but the collected air conditioner operating data is collected based on time.
  • the corresponding air conditioner operating data collected at each sampling time point has the corresponding sampling time point. time stamp to form a time series of air conditioner operating data.
  • the operating data of the multi-split air conditioner includes the indoor temperature of the room where each indoor unit is located, the outdoor temperature (that is, the outdoor ambient temperature), the system evaporation temperature, the system condensation temperature, and the execution (including compressor frequency, outdoor unit fan frequency, indoor unit expansion valve opening, outdoor unit expansion valve opening, etc.).
  • the time series of the operation data of the multi-split air conditioner refers to the numerical value of each parameter along the time sequence. Taking the indoor temperature as an example, the time series is Tin(0), Tin(1), Tin(2)...Tin(k), time The interval time of the sequence is the sampling time interval of the multi-line air conditioner operating data. If the sampling time interval is 1 minute, then k is the corresponding time mark. Through this time mark, the sampling start time and the sampling time interval, we can know each time The specific time of sampling.
  • Step 2 According to the time series of the operating data, identify the indoor temperature response time and the terminal temperature response time, use the indoor temperature response time as the time interval of the adjustment node in the first time scale, and use the terminal temperature response time as the second time scale. Adjust the time interval of nodes.
  • This disclosure is based on the response time of two temperatures that reflects the user control instructions of the air conditioner to seat the time intervals of different adjustment nodes, so that the response time of the air conditioner control is more in line with the actual situation of the air conditioner, and there are multiple time scales, so that the air conditioner can be adjusted according to the actual situation of the air conditioner.
  • the adjustment nodes of the two time scales perform corresponding more appropriate adjustments.
  • the indoor temperature response time can be obtained through the time sequence of the above air conditioner operating time.
  • the so-called indoor temperature response time is the time from the current indoor temperature to the time when the indoor temperature approaches the set indoor temperature (target temperature) after the air conditioner is operated.
  • the indoor temperature response time t1 refers to the time required for the indoor temperature to reach 90% of the final value relative to the evaporation/condensation temperature step signal.
  • the terminal temperature response time refers to the response time of the relevant temperature at the end of the air conditioner.
  • the terminal temperature includes one of the evaporation temperature, condensation temperature, outlet water temperature, return water temperature, outlet air temperature, return air temperature, and radiator temperature at the end of the air conditioner. kind.
  • the end temperature at this time is the evaporation temperature of the heat exchanger of the indoor unit (during cooling mode).
  • the air conditioner terminal referred to in this disclosure is not limited to the indoor unit.
  • the form of the air conditioner terminal is also different.
  • the terminal temperature corresponding to the end of the water cooling unit is the outlet water temperature or return water temperature, etc.
  • the terminal temperature refers to the floor temperature, or the radiator temperature, etc.
  • the terminal temperature takes the evaporation/condensation temperature of the indoor unit of the air conditioner as an example.
  • the response time t2 of the evaporation/condensation temperature refers to the step signal of the evaporation/condensation temperature of the air conditioning system reaching the final value relative to the action of the air conditioner actuator (mainly the compressor frequency). 90% of the time required.
  • the two response times in this disclosure take 90% as an example, but this disclosure does not limit the specific standard of the response time.
  • Those skilled in the art can adjust the specific standard of the response time according to different types of air conditioners and different regions where the air conditioners are applied.
  • Step 3 The present disclosure uses a first control strategy to control the operating parameters of the air conditioner at each adjustment node in the first time scale, and uses a second control strategy to control the operating parameters of the air conditioner at each adjustment node in the second time scale. .
  • the smallest unit of the first time scale is larger than the smallest unit of the second time scale.
  • the second control strategy's control of the air conditioner is actually fine-tuned based on the control of the first control strategy.
  • the first control strategy is a comfort control strategy, such as a balance control strategy that takes into account multiple factors such as comfort, energy saving, and reliability.
  • the second control strategy is an energy saving control strategy, or other control strategies. For example, some control strategies focus on energy saving while also taking into account other performance of air conditioners.
  • each time scale node uses each time scale node as an adjustment node for air conditioning control, and controls the air conditioning from multiple time scales, thereby avoiding the single time scale of air conditioning adjustment in related technologies, resulting in excessive control time scale of the air conditioning actuator. Long or too short question. At the same time, it also matches the output capacity of the air conditioner with the building's cooling/heating load, ensuring constant indoor temperature and reducing unit energy waste.
  • the indoor temperature response time t1 and the evaporation/condensation temperature response time t2 correspond to the long time scale and short time scale in multi-online control respectively.
  • the indoor temperature response time t1 is greater than the evaporation/condensation temperature response.
  • the indoor temperature response time t1 is 1 hour, which represents the time for the indoor temperature to stabilize after the evaporation/condensation temperature of the multi-line system changes;
  • the evaporation/condensation temperature response time t2 is 5 minutes, which represents the actuator action.
  • the above-mentioned first control strategy of the present disclosure includes: predicting the terminal temperature according to the predicted outdoor temperature and the indoor preset temperature, and adjusting the operating parameters of the air conditioner according to the predicted terminal temperature.
  • the terminal temperature is predicted based on the predicted outdoor temperature and the indoor preset temperature.
  • the main consideration is that the building cooling/heating load is mainly determined by the temperature difference between the building's indoor temperature and the outdoor ambient temperature. Therefore, this method has wide applicability.
  • Tin is the indoor preset temperature
  • a is the temperature influence coefficient during cooling
  • b is the room temperature influence coefficient during cooling.
  • Tc is the predicted condensation temperature
  • Tout is the predicted outdoor temperature
  • Tin is the indoor preset temperature
  • the coefficient c is the influence coefficient of temperature during heating
  • d is the influence coefficient of room temperature during heating.
  • the cooling/heating load of the building is mainly determined by the temperature difference between the indoor temperature of the building and the outdoor ambient temperature, and the fan of the indoor unit
  • the output capacity (cooling/heat) of the air conditioner is mainly determined by the temperature difference between the system evaporation temperature (cooling operation) or condensation temperature (heating operation) and the indoor temperature.
  • the evaporation temperature or condensation temperature of the indoor unit of the air conditioner is adjusted according to the future air temperature and the indoor preset temperature of the indoor unit.
  • a formula is used to calculate the predicted evaporation temperature.
  • the evaporation temperature of the machine heat exchanger, Tout is the outdoor temperature, that is, the outdoor ambient temperature, Tin_1, Tin_2,..., Tin_n is the preset indoor temperature (target temperature) of the room where each indoor unit (a total of n indoor units) is located, and the coefficient a is the influence coefficient of the temperature during cooling.
  • the range of a is, for example, [-2, 0].
  • the coefficient b is the influence coefficient of the room temperature during cooling. In some embodiments, the range of b is, for example, [0, 2]. , n is greater than or equal to 2.
  • Tc the predicted condensation temperature of the indoor unit heat exchanger.
  • the coefficient c is the influence coefficient of the temperature during heating. In some embodiments, the range of c is, for example, [-2, 0].
  • the coefficient d is the influence coefficient of the room temperature during heating. In some embodiments, the range of d is, for example, [0, 2], n is greater than or equal to 2.
  • the first control strategy in addition to considering outdoor temperature and indoor preset temperature, the first control strategy also considers changes in the number of people in the room, changes in the indoor environment, etc. to predict the end temperature, that is, the prediction of the end temperature takes into account more factors. , so that the predicted terminal temperature is more in line with the needs of the corresponding scenario, and finally the operating parameters of the air conditioner are adjusted based on the predicted terminal temperature.
  • the present disclosure adjusts the operating parameters of the air conditioner according to the predicted end temperature, for example, using the PID (proportion integration differentiation, proportion, integral, differential) control method to adjust, or using fuzzy control method to adjust.
  • PID proportion integration differentiation, proportion, integral, differential
  • the multi-split actuator such as compressor, outdoor fan, indoor/outdoor unit expansion valve, etc.
  • the multi-split actuator will evaporate/condensate the system according to PID or fuzzy control methods.
  • the condensing temperature is adjusted to the predicted value. It should be noted that during the control process, the evaporation temperature and condensation temperature are within their respective adjustment ranges, which are [5, 20] and [30, 45] respectively, and the unit is °C.
  • the constraint condition of the algorithm is to make the temperature change difference of the terminal temperature satisfy the preset difference range.
  • the second control strategy is an energy-saving control strategy
  • the second control strategy is a strategy that obtains the lowest energy consumption of the air conditioner through an automatic optimization algorithm
  • the constraint condition of the automatic optimization algorithm is such that the temperature of the terminal temperature changes The difference meets the preset difference range.
  • the predicted outdoor temperature is, for example, predicted based on a weather forecast or based on historical weather data. As the weather forecast becomes more and more accurate, the present disclosure uses the outdoor temperature predicted by the weather forecast to enable the air conditioner to perform corresponding control in advance to ensure long-term stability of the air conditioner temperature.
  • the adjustment node of the first time scale coincides with the adjustment node of the second time scale at a certain moment
  • the first control strategy is adopted. Controlling the operating parameters of the air conditioner avoids control chaos that occurs when the adjustment nodes of two time scales overlap at a certain moment.
  • the actuator of the multi-split air conditioner will give priority to long-term scale control, that is, based on the predicted system evaporation/condensation temperature.
  • the condensation temperature is controlled; if at a certain moment the response time t2 of the interval evaporation/condensation temperature is met but the response time t1 of the temperature in the interval is not met, the multi-line actuator performs short-time scale control, that is, automatic optimization control.
  • the time interval of the adjustment nodes of the first time scale is 1 hour
  • the time interval of the adjustment nodes of the second time scale is 7 minutes.
  • the adjustment nodes of the first time scale and the second time scale do not overlap.
  • the corresponding control strategy is executed, but at the 7th hour, that is, 420 minutes, the adjustment nodes of the first time scale and the second time scale coincide at this moment, then At this time, the first control strategy is executed.
  • the moment at which the adjustment nodes of the first time scale and the second time scale will overlap is related to the time interval of their adjustment nodes.
  • the adjustment range of the compressor frequency is within ⁇ 2Hz
  • the adjustment range of the fan frequency is within ⁇ 5Hz
  • the opening of the indoor/outdoor unit expansion valve is adjusted.
  • the range is within ⁇ 5% of the maximum value.
  • the actuator combination with the lowest energy consumption of the unit is obtained through an optimization algorithm.
  • the optimization algorithm uses genetic algorithm, particle swarm algorithm, etc.
  • the automatic optimization process needs to set constraints. For example, the system condensation temperature or evaporation temperature change after the actuator is fine-tuned should be within ⁇ 1°C.
  • the present disclosure also protects an air conditioner, which at least includes a data acquisition module, a time scale identification module and a control module.
  • the data acquisition module is used to collect time series of air conditioner operating data.
  • the time series of the multi-split air conditioner's operating data refers to the values of each parameter along the time sequence.
  • the time series is Tin(0), Tin(1), Tin(2) ...Tin(k)
  • the interval time of the time series is the sampling time interval of the multi-line air conditioner operating data. If the sampling time interval is 1 minute, then k is the corresponding time mark. Through this time mark, the sampling start time and the sampling time interval, you can know the specific time of each sampling.
  • the time scale identification module identifies the indoor temperature response time and the terminal temperature response time according to the time series of the operating data, and uses the indoor temperature response time as the time interval of the adjustment node of the first time scale, and the terminal temperature response time Time serves as the time interval of the regulating node of the second time scale.
  • the control module uses a first control strategy to control the operating parameters of the air conditioner at each adjustment node in the first time scale, and uses a second control strategy to control the operating parameters of the air conditioner at each adjustment node in the second time scale.
  • the present disclosure controls the air conditioner from multiple time scales, thereby avoiding the problem of too long or too short control time scale of the air conditioner actuator caused by a single time scale of air conditioning adjustment in related technologies. At the same time, it also matches the output capacity of the air conditioner with the building's cooling/heating load, ensuring constant indoor temperature and reducing unit energy waste.
  • control module includes a terminal temperature prediction module.
  • the terminal temperature prediction module is used to predict the terminal temperature so that the control module can control the operating parameters of the air conditioner according to the predicted terminal temperature and the first control strategy.
  • the terminal temperature can quickly reflect the specific conditions of the air conditioner operating towards the target temperature. By predicting the terminal temperature, the operating parameters of the air conditioner can be accurately controlled to ensure the comfort of the air conditioner.
  • control module also includes a unit actuator control module.
  • the unit actuator control module is used to control the operating parameters of the air conditioner controlled by the first control strategy according to the second control strategy, so that the temperature change difference of the terminal temperature meets the preset difference range.
  • the unit actuator of the present disclosure fine-tunes the operating parameters of the air conditioner under the first control strategy. It performs fine-tuning control on the actuator while maintaining the stability of the system evaporation/condensation temperature, which improves the efficiency of the multi-line air conditioning control process. operational reliability while also optimizing the energy saving of the air conditioner.
  • the multi-spring air conditioner includes a data acquisition module, a system evaporation/condensation temperature prediction module (terminal temperature prediction module) and a unit actuator optimization module (also called a unit actuator control module).
  • the data acquisition module obtains the weather forecast data and multi-line operation data of the weather station
  • the system evaporation/condensation temperature prediction module predicts the condensation temperature (heating mode) or evaporation temperature (cooling mode) of the system in the first time scale based on the above data.
  • the unit actuator optimization module optimizes the unit's actuator actions under the second time scale.
  • the present disclosure also protects a computer-readable storage medium, which is used to store a computer program.
  • a computer program When the computer program is run, the air-conditioning control method of the above technical solution of the present disclosure is executed.

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Abstract

本公开提供了一种空调及其控制方法、存储介质。其中，空调的控制方法，包括：采集空调的运行数据的时间序列；根据运行数据的时间序列识别室内温度响应时间和末端温度响应时间，并将室内温度响应时间作为第一时间尺度的调节节点的时间间隔，将末端温度响应时间作为第二时间尺度的调节节点的时间间隔；在第一时间尺度的每个调节节点采用第一控制策略对空调的运行参数进行控制；在第二时间尺度的每个调节节点采用第二控制策略对空调的运行参数进行控制。

Description

空调及其控制方法、存储介质
相关申请的交叉引用
本公开是以中国申请号为202211097899.4，申请日为2022年09月08日的申请为基础，并主张其优先权，该中国申请的公开内容在此作为整体引入本公开中。
技术领域
本公开涉及空调控制的技术领域，尤其涉及一种空调及其控制方法、存储介质。
背景技术
空调控制的优化一直是空调厂家的一个重要研究方向，通过对空调的控制的优化来实现空调能耗最低的控制。
相关技术中，根据当地气候特点和空调系统运行特性，考虑建筑物内外温度湿度变化以及人数变化的影响，利用统计或者机器学习，找到最佳COP(Coefficient Of Performance，性能系数)对应的运行方式，以此对相关运行特性参数以及预控时间进行调节，以希望达到舒适性和节能性的平衡。
发明内容
本公开提出了空调及其控制方法、存储介质。
本公开提出的空调的控制方法，包括：
采集空调的运行数据的时间序列；
根据运行数据的时间序列识别室内温度响应时间和末端温度响应时间，并将室内温度响应时间作为第一时间尺度的调节节点的时间间隔，将末端温度响应时间作为第二时间尺度的调节节点的时间间隔；
在第一时间尺度的每个调节节点采用第一控制策略对空调的运行参数进行控制；
在第二时间尺度的每个调节节点采用第二控制策略对空调的运行参数进行控制。
在一些实施例中，当第一时间尺度的调节节点与第二时间尺度的调节节点在某个时刻重合时，则采用第一控制策略对空调的运行参数进行控制。
在一些实施例中，所述第一控制策略包括：根据预测的室外温度与室内预设温度预测末端温度，根据预测的末端温度对空调的运行参数进行调整。
在一些实施例中，当空调运行制冷工况时，根据预测的室外温度与室内预设温度，预测末端温度，通过公式Te＝a*Tout+b*Tin来预测，Te为预测的蒸发温度，Tout为预测的室外温度，Tin为室内预设温度，a为制冷时气温影响系数，b为制冷时室温影响系数。
在一些实施例中，当空调运行制热工况时，根据预测的室外温度与室内预设温度，通过公式Tc＝c*Tout+d*Tin预测末端温度，其中Tc为预测的冷凝温度，Tout为预测的室外温度，Tin为室内预设温度，系数c为制热时气温影响系数，d为制热时室温影响系数。
在一些实施例中，当空调具有n个空调末端时，所述室内预设温度通过公式Tin＝(Tin_1+…+Tin_n)/n计算得到，n≥2，Tin_1,…,Tin_n为各个空调末端的室内预设温度。
在一些实施例中，根据预测的末端温度对空调的运行参数进行调整，例如采用PID方法进行调整，或者采用模糊控制方法进行调整。
在一些实施例中，所述室外温度根据天气预报进行预测，或者根据历史天气数据进行预测。
在一些实施例中，所述第二控制策略为得到空调能耗最低的策略，且所述第二控制策略使得末端温度的温度变化差值满足预设差值范围。
在一些实施例中，所述末端温度包括空调末端的蒸发温度、冷凝温度、出水温度、回水温度、出风温度、回风温度、暖气片温度当中的一种。
本公开提出的空调，包括：
据采集模块，采集空调的运行数据的时间序列；
时间尺度识别模块，根据运行数据的时间序列识别室内温度响应时间和末端温度响应时间，并将室内温度响应时间作为第一时间尺度的调节节点的时间间隔，将末端温度响应时间作为第二时间尺度的调节节点的时间间隔；
控制模块，在第一时间尺度的每个调节节点采用第一控制策略对空调的运行参数进行控制，在第二时间尺度的每个调节节点采用第二控制策略对空调的运行参数进行控制。
在一些实施例中，所述控制模块包括：末端温度预测模块，用于预测末端温度，以便所述控制模块根据预测的末端温度以及第一控制策略对空调的运行参数进行控制。
在一些实施例中，所述控制模块还包括：机组执行器控制模块，用于根据第二控制策略对第一控制策略控制后的空调的运行参数进行控制，使得末端温度的温度变化差值满足预设差值范围。
本公开提出的计算机可读存储介质，用于存储计算机程序，所述计算机程序运行时执行上述技术方案所述的空调的控制方法。
本公开考虑到空调的输出能力(冷量/热量)主要是由末端蒸发温度或冷凝温度与室内环境温度的温差决定的情况，因而通过采集空调运行数据的时间序列，得到空调控制的 长时间尺度(第一时间尺度)和短时间尺度(第二时间尺度)，在长时间尺度(第一时间尺度)上控制多联机空调的系统蒸发/冷凝温度，实现了空调输出能力与建筑冷/热负荷的匹配，保证了室内温度恒定和减少了机组能量浪费，提高了空调的热舒适性和节能性；通过在短时间尺度(第二时间尺度)上微调多联机空调的机组执行器，优化了各执行器间的组合方式，进一步降低了多联机空调的运行能耗。同时，由于本公开并不是直接对多联机的执行器进行控制，而是在保持系统蒸发/冷凝温度稳定的情况下对执行器进行微调控制，提高了多联机空调控制过程中的运行可靠性。
附图说明
下面结合实施例和附图对本公开进行详细说明，其中：
图1是本公开的一些实施例的主要流程图。
图2是本公开的一些实施例的细节流程图。
图3是本公开的一些实施例的结构框图。
具体实施方式
为了使本公开所要解决的技术问题、技术方案及有益效果更加清楚明白，以下结合附图及实施例，对本公开进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本公开，并不用于限定本公开。
由此，本说明书中所指出的一个特征将用于说明本公开的一个实施方式的其中一个特征，而不是暗示本公开的每个实施方式必须具有所说明的特征。此外，应当注意的是本说明书描述了许多特征。尽管某些特征组合在一起以示出系统设计，但是这些特征也能够用于其他的未明确说明的组合。由此，除非另有说明，所说明的组合并非旨在限制。
相关技术中，根据当地气候特点和空调系统运行特性，考虑建筑物内外温度湿度变化以及人数变化的影响，利用统计或者机器学习，找到最佳COP对应的运行方式，以此对相关运行特性参数以及预控时间进行调节，以希望达到舒适性和节能性的平衡。发明人意识到，这种方法直接对空调执行器如压缩机、风机和阀门进行控制，极大影响系统的运行可靠性；同时，该方法并未考虑室内温度(响应时间长)与空调系统状态(响应时间短)在响应时间上的差异，造成空调执行器的控制时间尺度过长或过短，限制了空调的控制效果。因此，亟需一种控制时间尺度恰当的空调的控制方法。
本公开提出的空调的控制方法，包括以下步骤。
步骤1，采集空调的运行数据的时间序列。
空调的运行数据的时间序列实质上就是空调的运行数据，但是所采集的空调的运行数据是根据时间来采集的，每个采样时间点采集的对应的空调的运行数据都具备该采样时间点对应的时间标记，从而形成空调的运行数据的时间序列。
如图1、图2所示，以多联机空调为例，多联机空调的运行数据包括各室内机所在房间的室内温度、室外温度(即室外环境温度)、系统蒸发温度、系统冷凝温度、执行器动作(包括压缩机频率、室外机风机频率、室内机膨胀阀开度、室外机膨胀阀开度等)。多联机空调的运行数据的时间序列指各个参数沿着时间顺序的数值，以室内温度为例，其时间序列为Tin(0),Tin(1),Tin(2)…Tin(k)，时间序列的间隔时间即为多联机空调运行数据的采样时间间隔，如采样时间间隔为1分钟，那么k就是对应的时间标记，通过该时间标记以及采样开始时间和采样时间间隔，就能够得知每次采样的具体时间。
步骤2，根据运行数据的时间序列，识别室内温度响应时间和末端温度响应时间，并将室内温度响应时间作为第一时间尺度的调节节点的时间间隔，将末端温度响应时间作为第二时间尺度的调节节点的时间间隔。本公开基于反映空调的用户控制指令的两个温度的响应时间来座位不同调节节点的时间间隔，使得空调的控制的响应时间更加贴合空调的实际情况，且存在多种时间尺度，使得空调根据两种时间尺度的调节节点进行对应的更加合适的调节。通过上述空调的运行时间的时间序列，能够得到室内温度响应时间，所谓室内温度响应时间就是当前室内温度开始，到空调运行后室内温度接近用于设定的室内温度(目标温度)的时间。例如，室内温度响应时间t1指室内温度相对于蒸发/冷凝温度阶跃信号达到最终数值90％所需要的时间。而末端温度响应时间指的是空调末端的相关温度的响应时间，末端温度包括空调末端的蒸发温度、冷凝温度、出水温度、回水温度、出风温度、回风温度、暖气片温度当中的一种。例如空调末端是室内机，那么此时末端温度是室内机的换热器的蒸发温度(制冷模式时)，再例如是室内机换热器的冷凝温度(制热模式时)，或是室内机的出风温度等。当然本公开所指的空调末端不仅限于室内机，根据空调的类型不同，空调末端的形式也不相同，例如水冷机组的末端对应的末端温度，就是出水温度或者回水温度等。再例如当空调为地暖空调时，那么末端温度指的是地板温度，或者是暖气片温度等。末端温度以空调的室内机的蒸发/冷凝温度为例，蒸发/冷凝温度的响应时间t2指空调系统蒸发/冷凝温度相对于空调执行器动作(以压缩机频率为主)阶跃信号达到最终数值90％所需要的时间。
本公开的两个响应时间均是以90％为例，但是本公开并不限制响应时间的具体标准， 本领域技术人员能够根据空调的类型不同，空调应用的地域不同，来调节响应时间的具体标准。
步骤3，本公开在第一时间尺度的每个调节节点采用第一控制策略对空调的运行参数进行控制，在第二时间尺度的每个调节节点采用第二控制策略对空调的运行参数进行控制。
由于末端温度的响应时间通常都要快于室内温度响应时间，因而第一时间尺度的最小单位大于第二时间尺度的最小单位。
在一些实施例中，第二控制策略对空调的控制实际上是在第一控制策略的控制基础上进行微调。例如，第一控制策略是舒适性控制策略，例如兼顾了舒适性、节能性、可靠性当中的多个因素的平衡性控制策略，第二控制策略是节能性控制策略，或是其他控制策略，例如，以节能性为主，同时再兼顾空调其他性能的一些控制策略等。
本公开将每个时间尺度的节点作为空调控制的一个调节节点，从多个时间尺度来对空调进行控制，从而避免相关技术中空调调节的时间尺度单一，导致的空调执行器的控制时间尺度过长或过短的问题。同时也使得空调的输出能力与建筑冷/热负荷匹配，保证了室内温度恒定和减少了机组能量浪费。
还是以空调的室内机为例，室内温度响应时间t1和蒸发/冷凝温度的响应时间t2分别对应多联机控制中的长时间尺度和短时间尺度，室内温度响应时间t1大于蒸发/冷凝温度的响应时间t2。例如通过步骤S2，得到室内温度响应时间t1为1小时，代表多联机系统蒸发/冷凝温度发生变化后，室内温度达到稳定的时间；蒸发/冷凝温度的响应时间t2为5分钟，代表执行器动作发生变化后，多联机系统蒸发/冷凝温度达到稳定的时间。
在一些实施例中，本公开的上述第一控制策略包括：根据预测的室外温度与室内预设温度预测末端温度，根据预测的末端温度对空调的运行参数进行调整。根据预测的室外温度与室内预设温度来预测末端温度，主要考虑的是建筑冷/热负荷主要由建筑室内温度和室外环境温度的温差决定，因而该方法适用性广泛。当空调运行制冷工况时，根据预测的室外温度与室内预设温度预测末端温度，通过公式Te＝a*Tout+b*Tin来预测，Te为预测的蒸发温度，Tout为预测的室外温度，Tin为室内预设温度，a为制冷时气温影响系数，b为制冷时室温影响系数。当空调运行制热工况时，根据预测的室外温度与室内预设温度预测末端温度通过公式Tc＝c*Tout+d*Tin，其中Tc为预测的冷凝温度，Tout为预测的室外温度，Tin为室内预设温度，系数c为制热时气温影响系数，d为制热时室温影响系数。
建筑冷/热负荷主要由建筑室内温度和室外环境温度的温差决定，而在室内机的风机 转速一定的情况下，空调的输出能力(制冷/热量)主要由系统蒸发温度(制冷工况)或冷凝温度(制热工况)与室内温度的温差决定。为了使空调的输出能力匹配建筑冷/热负荷，根据未来气温和室内机的室内预设温度来调节空调的室内机的蒸发温度或冷凝温度。
以多联机空调为例，在制冷工况下，采用公式来计算预测的蒸发温度，公式为Te＝a*Tout+b*(Tin_1+Tin_2+…+Tin_n)/n，其中，Te为预测的室内机换热器的蒸发温度，Tout为室外温度，即室外环境温度，Tin_1,Tin_2,…,Tin_n为各个室内机(一共n台室内机)所在房间的预设室内温度(目标温度)，系数a为制冷时气温影响系数，在一些实施例中，a的范围例如为[-2，0]，系数b为制冷时室温影响系数，在一些实施例中，b的范围例如为[0，2]，n大于等于2。在制热工况下，采用公式来计算预测的冷凝温度，公式为Tc＝c*Tout+d*(Tin_1+Tin_2+…+Tin_n)/n，其中Tc为预测的室内机换热器的冷凝温度，系数c为制热时气温影响系数，在一些实施例中，c的范围例如为[-2，0]，系数d为制热时室温影响系数，在一些实施例中，d的范围例如为[0，2]，n大于等于2。
在一些实施例中，第一控制策略除了考虑室外温度与室内预设温度以外，还考虑室内的人数变化、室内的环境变化等来预测末端温度，即对末端温度的预测考虑更多的因素进来，以便预测的末端温度更加符合对应的场景的需求，最后再根据预测的末端温度对空调的运行参数进行调整。
通过上述方法得到预测的末端温度之后，本公开在根据预测的末端温度对空调的运行参数进行调整时，例如采用PID(proportion integration differentiation，比例，积分，微分)控制方法进行调整，或者采用模糊控制方法进行调整。
还是以多联机空调为例，当得到系统蒸发/冷凝温度预测值后，多联机执行器(如压缩机、室外风机、室内/外机膨胀阀等)根据PID或模糊控制等方式将系统蒸发/冷凝温度调节在预测值。需要说明的是，控制过程中蒸发温度和冷凝温度处于各自的调节范围内，分别为[5，20]和[30,45]，单位为℃。也就是说第二控制策略(如节能控制策略)在采用相应的算法得到空调能耗最低的策略时，对算法的约束条件为使得末端温度的温度变化差值满足预设差值范围。在一些实施例中，当第二控制策略为节能控制策略时，第二控制策略为通过自动寻优算法得到空调能耗最低的策略，且自动寻优算法的约束条件为使得末端温度的温度变化差值满足预设差值范围。本公开不限制自动寻优算法的具体种类，自动寻优算法例如采用遗传算法，或采用粒子群算法，并且不限于本公开所列举的这两种自动寻优算法。
在一些实施例中，预测的室外温度例如是根据天气预报进行预测得到的，或是根据历史天气数据进行预测得到的。随着天气预报的预测越来越精准，本公开采用天气预报预测的室外温度，能够提前使得空调进行相应的控制，确保空调温度的长时间稳定。
由于本公开涉及到多个时间尺度，会存在多个时间尺度重合的时间节点，当第一时间尺度的调节节点与第二时间尺度的调节节点在某个时刻重合时，则采用第一控制策略对空调的运行参数进行控制，避免了两个时间尺度的调节节点在某个时刻重合时出现的控制混乱。以多联机空调为例，若某一时刻既满足间隔室内温度响应时间t1又满足间隔蒸发/冷凝温度的响应时间t2，多联机空调的执行器优先进行长时间尺度控制，即根据预测系统蒸发/冷凝温度进行控制；若某一时刻满足间隔蒸发/冷凝温度的响应时间t2时刻但不满足间隔室内温度响应时间t1时刻，则多联机执行器进行短时间尺度控制，即自动寻优控制。
举例来说，第一时间尺度的调节节点的时间间隔为1小时，第二时间尺度的调节节点的时间间隔为7分钟，在第一时间尺度和第二时间尺度的调节节点不会重合的情况下，在某个时间尺度的时间节点到达时，执行对应的控制策略，但是在第7个小时，也就是420分钟时，第一时间尺度和第二时间尺度的调节节点在这个时刻重合，则此时执行第一控制策略。第一时间尺度和第二时间尺度的调节节点会在哪个时刻重合，与它们的调节节点的时间间隔有关。执行器在执行自动寻优控制时，各执行器的动作只进行微调，压缩机频率的调整范围在±2Hz以内，风机频率的调整范围在±5Hz以内，室内/外机膨胀阀开度的调整范围在最大值±5％以内，通过寻优算法得到机组能耗最低的执行器组合，寻优算法采用遗传算法、粒子群算法等。另外，自动寻优过程需要设置约束条件，例如，执行器微调后的系统冷凝温度或蒸发温度变化在±1℃以内。
本公开还保护一种空调，该空调至少包括数据采集模块、时间尺度识别模块以及控制模块。
数据采集模块用于采集空调的运行数据的时间序列。以多联机空调为例，多联机空调的运行数据的时间序列指各个参数沿着时间顺序的数值，以室内温度为例，其时间序列为Tin(0),Tin(1),Tin(2)…Tin(k)，时间序列的间隔时间即为多联机空调运行数据的采样时间间隔，如采样时间间隔为1分钟，那么k就是对应的时间标记，通过该时间标记以及采样开始时间和采样时间间隔，就能够得知每次采样的具体时间。
时间尺度识别模块根据运行数据的时间序列识别室内温度响应时间和末端温度响应时间，并将室内温度响应时间作为第一时间尺度的调节节点的时间间隔，将末端温度响应 时间作为第二时间尺度的调节节点的时间间隔。
控制模块在第一时间尺度的每个调节节点采用第一控制策略对空调的运行参数进行控制，在第二时间尺度的每个调节节点采用第二控制策略对空调的运行参数进行控制。
本公开从多个时间尺度来对空调进行控制，从而避免相关技术中空调调节的时间尺度单一，导致的空调执行器的控制时间尺度过长或过短的问题。同时也使得空调的输出能力与建筑冷/热负荷匹配，保证了室内温度恒定，减少了机组能量浪费。
在一些实施例中，控制模块包括末端温度预测模块。
末端温度预测模块用于预测末端温度，以便控制模块能够根据预测的末端温度以及第一控制策略，对空调的运行参数进行控制。
末端温度能够快速反应空调朝着目标温度运行的具体情况，通过预测末端温度，能够对空调的运行参数进行准确的控制，确保空调的舒适性。
在一些实施例中，控制模块还包括机组执行器控制模块。
机组执行器控制模块用于根据第二控制策略对第一控制策略控制后的空调的运行参数进行控制，使得末端温度的温度变化差值满足预设差值范围。
本公开的机组执行器在第一控制策略下，对空调的运行参数进行微调，是在保持系统蒸发/冷凝温度稳定的情况下，对执行器进行微调控制，提高了多联机空调控制过程中的运行可靠性，同时还优化了空调的节能性。
如图3所示，以多联机为例，多联机空调包括数据采集模块、系统蒸发/冷凝温度预测模块(末端温度预测模块)和机组执行器寻优模块(也叫机组执行器控制模块)，其中，数据采集模块获取气象站的天气预报数据和多联机运行数据，系统蒸发/冷凝温度预测模块根据以上数据进行预测第一时间尺度下系统的冷凝温度(制热模式)或蒸发温度(制冷模式)，机组执行器寻优模块优化第二时间尺度下机组的执行器动作。
本公开还保护一种计算机可读存储介质，该计算机可读存储介质用于存储计算机程序，该计算机程序运行时执行本公开上述技术方案的空调的控制方法。
以上所述并不用以限制本公开，凡在本公开的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本公开的保护范围之内。

Claims (17)

  1. 一种空调的控制方法，包括：
    采集空调的运行数据的时间序列；
    根据所述运行数据的时间序列识别室内温度响应时间和末端温度响应时间，并将所述室内温度响应时间作为第一时间尺度的调节节点的时间间隔，将所述末端温度响应时间作为第二时间尺度的调节节点的时间间隔；
    在所述第一时间尺度的每个调节节点采用第一控制策略对空调的运行参数进行控制；
    在所述第二时间尺度的每个调节节点采用第二控制策略对空调的运行参数进行控制。
  2. 如权利要求1所述的空调的控制方法，其中，在所述第一时间尺度的调节节点与所述第二时间尺度的调节节点在某个时刻重合的情况下，采用所述第一控制策略对空调的运行参数进行控制。
  3. 如权利要求1或2所述的空调的控制方法，其中，所述第一控制策略包括：
    根据预测的室外温度与室内预设温度，预测末端温度；
    根据所述预测的末端温度对空调的运行参数进行调整。
  4. 如权利要求3所述的空调的控制方法，其中，所述根据预测的室外温度与室内预设温度，预测末端温度，包括：
    在空调运行制冷工况的情况下，通过公式Te＝a*Tout+b*Tin预测，其中，Te为预测的蒸发温度，Tout为预测的室外温度，Tin为室内预设温度，a为制冷时气温影响系数，b为制冷时室温影响系数。
  5. 如权利要求3或4所述的空调的控制方法，其中，所述根据预测的室外温度与室内预设温度，预测末端温度，包括：
    在空调运行制热工况的情况下，通过公式Tc＝c*Tout+d*Tin预测，其中，Tc为预测的冷凝温度，Tout为预测的室外温度，Tin为室内预设温度，系数c为制热时气温影响系数，d为制热时室温影响系数。
  6. 如权利要求4或5所述的空调的控制方法，其中，在空调具有n个空调末端的情况下，所述室内预设温度通过公式Tin＝(Tin_1+…+Tin_n)/n计算得到，n≥2，Tin_1,…,Tin_n为各个空调末端的室内预设温度。
  7. 如权利要求3至6任一项所述的空调的控制方法，其中，所述根据预测的末端温 度对空调的运行参数进行调整包括：
    采用PID方法进行调整，或者采用模糊控制方法进行调整。
  8. 如权利要求3至7任一项所述的空调的控制方法，其中，所述室外温度根据天气预报进行预测，或者根据历史天气数据进行预测。
  9. 如权利要求1至8任一项所述的空调的控制方法，其中，所述第二控制策略为得到空调能耗最低的策略，且所述第二控制策略使得末端温度的温度变化差值满足预设差值范围。
  10. 如权利要求1至9任一项所述的空调的控制方法，其中，所述末端温度包括空调末端的蒸发温度、冷凝温度、出水温度、回水温度、出风温度、回风温度、暖气片温度中的一种。
  11. 一种空调，包括：
    数据采集模块，采集空调的运行数据的时间序列；
    时间尺度识别模块，根据所述运行数据的时间序列识别室内温度响应时间和末端温度响应时间，并将所述室内温度响应时间作为第一时间尺度的调节节点的时间间隔，将所述末端温度响应时间作为第二时间尺度的调节节点的时间间隔；
    控制模块，在所述第一时间尺度的每个调节节点采用第一控制策略对空调的运行参数进行控制，在所述第二时间尺度的每个调节节点采用第二控制策略对空调的运行参数进行控制。
  12. 如权利要求11所述的空调，其中，所述控制模块包括：
    末端温度预测模块，用于预测末端温度，以便所述控制模块根据所述预测的末端温度以及所述第一控制策略对空调的运行参数进行控制。
  13. 如权利要求12所述的空调，其中，所述控制模块包括：
    机组执行器控制模块，用于根据所述第二控制策略对所述第一控制策略控制后的空调的运行参数进行控制，使得末端温度的温度变化差值满足预设差值范围。
  14. 一种计算机可读存储介质，用于存储计算机程序指令，所述计算机程序指令被处理器运行时实现如权利要求1至10任意一项所述的空调的控制方法。
  15. 一种空调的控制装置，包括：
    存储器；以及
    耦接至所述存储器的处理器，所述处理器被配置为基于存储在所述存储器的指令，执 行如权利要求1至10任意一项所述的空调的控制方法。
  16. 一种空调，包括如权利要求15所述的空调的控制装置。
  17. 一种计算机程序，包括：
    指令，所述指令当由处理器执行时使所述处理器执行如权利要求1至10任意一项所述的空调的控制方法。
PCT/CN2023/092755 2022-09-08 2023-05-08 空调及其控制方法、存储介质 WO2024051191A1 (zh)

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