EP4166862A1 - Method and apparatus for controlling refrigeration device, computer device, and computer readable medium - Google Patents

Method and apparatus for controlling refrigeration device, computer device, and computer readable medium Download PDF

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Publication number
EP4166862A1
EP4166862A1 EP21821753.7A EP21821753A EP4166862A1 EP 4166862 A1 EP4166862 A1 EP 4166862A1 EP 21821753 A EP21821753 A EP 21821753A EP 4166862 A1 EP4166862 A1 EP 4166862A1
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EP
European Patent Office
Prior art keywords
cooling equipment
air conditioner
neural network
network model
preset
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21821753.7A
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German (de)
English (en)
French (fr)
Inventor
Mingming Liu
Yong XIONG
Xianhong HU
Donghua LIN
Shihao QIN
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ZTE Corp
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ZTE Corp
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Publication of EP4166862A1 publication Critical patent/EP4166862A1/en
Pending legal-status Critical Current

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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
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • 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
    • F24F11/32—Responding to malfunctions or emergencies
    • F24F11/38—Failure diagnosis
    • 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
    • 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
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • 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
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature
    • F24F2110/12—Temperature of the outside air
    • 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
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2140/00—Control inputs relating to system states
    • F24F2140/50—Load

Definitions

  • the present disclosure relates to the technical field of automatic control, and in particular, to a cooling equipment control method and device, a computer device and a computer-readable medium.
  • the present disclosure provides a cooling equipment control method, including: determining a current outdoor temperature; inputting same-period historical sample data of cooling equipment loads and a preset impact factor into a first neural network model as a first input parameter to obtain a predicted load of cooling equipment on a current day; inputting same-period historical sample data of outdoor temperatures and the predicted load of the cooling equipment on the current day into a second neural network model as a second input parameter to obtain a predicted indoor temperature on the current day; inputting the predicted indoor temperature on the current day and a preset cooling efficiency factor into a third neural network model as a third input parameter to obtain optimal control parameters of the cooling equipment on the current day; and controlling the cooling equipment to operate according to the optimal control parameters.
  • the present disclosure further provides a cooling equipment control device, including: a first processing module, a second processing module, and a control module, where the first processing module is configured to determine a current outdoor temperature, the second processing module is configured to: input same-period historical sample data of cooling equipment loads and a preset impact factor into a first neural network model as a first input parameter to obtain a predicted load of cooling equipment on a current day, input same-period historical sample data of outdoor temperatures and the predicted load of the cooling equipment on the current day into a second neural network model as a second input parameter to obtain a predicted indoor temperature on the current day, and input the predicted indoor temperature on the current day and a preset cooling efficiency factor into a third neural network model as a third input parameter to obtain optimal control parameters of the cooling equipment on the current day; and the control module is configured to control the cooling equipment to operate according to the optimal control parameters.
  • the first processing module is configured to determine a current outdoor temperature
  • the second processing module is configured to: input same-period historical sample data of cooling equipment
  • the present disclosure further provides a computer device, including: one or more processors; and a storage device having one or more programs stored thereon; and when executed by the one or more processors, the one or more programs cause the one or more processors to carry out the cooling equipment control method according to the present disclosure.
  • the present disclosure further provides a computer-readable medium having stored thereon a computer program, which, when executed by a processor, causes the processor to carry out the cooling equipment control method according to the present disclosure.
  • a basic principle of the energy-saving heat exchange equipment is to take an outdoor natural environment as a cold source and perform heat exchange using outdoor low-temperature air and high-temperature air in the machine room when an outdoor temperature is lower than an indoor temperature to a certain extent to take away the heat from the machine room, so as to reduce the temperature in the machine room, thereby shortening usage time of the air conditioner and saving electric energy.
  • a conventional linkage control algorithm is a traditional temperature-controlled startup-shutdown method, and takes an ambient temperature as a main basis of the linkage control of the heat exchange equipment and the air conditioner.
  • the algorithm is simple but is difficult to improve.
  • a conventional linkage control process is as follows: detecting indoor temperatures and outdoor temperatures in real time, and turning on the heat exchange equipment or the air conditioner for cooling if the indoor temperature exceeds an upper limit of an operation temperature of the devices: when a start condition of the heat exchange equipment is met (if an indoor-outdoor temperature difference reaches a threshold), preferentially turning on the heat exchange equipment, otherwise, turning on the air conditioner. Switching between the air conditioner and the heat exchange equipment should not be carried out frequently, and may be carried out at intervals longer than half an hour.
  • Startup/shutdown condition parameters of the heat exchange equipment and startup/shutdown condition parameters of the air conditioner are respectively set, for example, a startup temperature/a shutdown temperature of the heat exchange equipment may be 35°C/25°C and a temperature difference may be 8 °C, that is, the heat exchange equipment is turned on when a room temperature exceeds 35 °C and is turned off when the room temperature is below 25 °C, and the heat exchange equipment is allowed to be turned on when the indoor-outdoor temperature difference exceeds 8°C.
  • the startup/shutdown condition parameters are usually difficult to determine in practical engineering applications and are not fixed due to different regions, different seasonal climates and different temperature differences between morning and evening.
  • the air conditioner may be turned on frequently, resulting in an increase of energy consumption.
  • the conventional linkage control algorithm cannot predict startup moments and startup times of the air conditioner, and is low in control accuracy and hard to improve.
  • the air conditioner does not need to be turned on in fact, which may save one startup time of the air conditioner while ensuring safety of the devices, thereby saving energy to a certain extent.
  • the present disclosure provides a cooling equipment control method capable of control operation of cooling equipment in a machine room.
  • the method is applicable to a cooling control system shown in FIG. 1 .
  • the cooling control system includes a cooling equipment control device, a Field Supervision Unit (FSU), and a cooling equipment.
  • the FSU is a field device disposed in a machine room where the cooling equipment is located, and includes an acquisition unit and an execution unit.
  • the acquisition unit is configured to acquire real-time data such as outdoor temperature, outdoor humidity, indoor temperature and equipment load, and upload the real-time data to the cooling control device.
  • the execution unit is configured to control the cooling equipment to operate according to instructions from the cooling control device.
  • the cooling equipment control device may be a cloud device, and may be the Unified Management Expert (UME) configured with a first neural network (NN) model, a second neural network model, a third neural network model, a historical sample database, and a control strategy (e.g., a cooling control algorithm) of the cooling equipment.
  • the UME may obtain a predicted control scheme of the cooling equipment according to the data reported by the FSU, the first neural network model, the second neural network model and the third neural network model, and issue the predicted control scheme to the FSU.
  • the cooling equipment may include an air conditioner and a heat exchange equipment, which are capable of operating according to the issued control scheme.
  • the following first to tenth thresholds and various duration may be set in advance in the cooling equipment control device in an initialization stage.
  • a first threshold VHT may be, for example, 45 °C, and the air conditioner is unconditionally turned on when the indoor temperature exceeds VHT.
  • a second threshold VLT may be, for example, 15 °C, the air conditioner is unconditionally turned off when the indoor temperature is lower than VLT, and the second threshold VLT is less than the first threshold VHT.
  • a third threshold HT AC may be, for example, 40°C, and the air conditioner may be turned on when the indoor temperature exceeds HT AC .
  • a fourth threshold HT HEE may be, for example, 35°C, and the heat exchange equipment may be turned on when the indoor temperature exceeds HT HEE .
  • a fifth threshold is configured to determine whether a second high temperature prestart condition for an indirect heat exchange equipment is met.
  • a sixth threshold LT may be, for example, 25 °C, the air conditioner and the heat exchange equipment may be turned off when the indoor temperature is lower than LT, and the sixth threshold LT is less than the fourth threshold HT HEE and the third threshold HT AC .
  • a seventh threshold is configured to determine a shutdown duration of the cooling equipment.
  • An eighth threshold is configured to determine whether an indoor-outdoor temperature difference requirement in the second high temperature prestart condition for a direct heat exchange equipment is met.
  • a ninth threshold is configured to determine whether a humidity requirement in the second high temperature prestart condition for the direct heat exchange equipment is met.
  • a tenth threshold is configured to determine an error between an actual operation parameter of the air conditioner and an optimal control parameter of the air conditioner.
  • Maximum continuous operation time MAXCOT of the air conditioner and minimum continuous shutdown time MINCST of the air conditioner are set, and in general, the maximum continuous operation time MAXCOT of the air conditioner is 12 hours and the minimum continuous shutdown time MINCST of the air conditioner is 0.5 hour.
  • the first neural network model, the second neural network model and the third neural network model are built in the initialization stage. A process of building the first, second and third neural network models is described in detail below with reference to FIG. 2 .
  • the process of building the first, second and third neural network models includes the following operations S21 to S23.
  • the sample data may include outdoor temperatures, indoor temperatures, and cooling equipment loads.
  • the cooling equipment control device may acquire the historical sample data from the historical sample database which may store a large amount of historical sample data such as daily outdoor temperature T Rout , daily indoor temperature T Rin , and daily cooling equipment load L R .
  • Sampling periods may be determined according to how fast the parameters change. For example, the sampling period of the outdoor temperature T Rout may be 10 minutes, and both the sampling period of the indoor temperature T Rin and the sampling period of the cooling equipment load L R may be 5 minutes.
  • the sample data may include simulation data and sampled data.
  • the simulation data is the data obtained by simulating operation of the cooling equipment when the indoor temperature is greater than the third threshold HT AC .
  • the sampled data is the data sampled when the indoor temperature is less than the sixth threshold LT and the actual shutdown duration of the cooling equipment is greater than the seventh threshold. That is, in a case where the indoor temperature T Rin is relatively high and the cooling equipment needs to operate, simulation of the real cooling equipment may be carried out using a dummy load, and the data such as T Rout , T Rin and L R may be recorded.
  • a large amount of the existing historical sample data may be directly used, so as to accelerate an acquisition speed of the historical sample data.
  • a heat distribution map of a room environment, heat generating devices and the cooling equipment is created through simulation training by a computer, the historical sample data is subjected to analog computation, an optimal solution vector for controlling the cooling equipment on a current day (i.e., the daily optimal control parameters of the cooling equipment) is output, and the daily optimal control parameters of the cooling equipment are stored as a sample data label.
  • the air conditioner should not be turned on frequently.
  • a limitation may be imposed that the air conditioner is turned on at most 12 times every day and the heat exchange equipment is turned on at most 12 times every day. That is, for the air conditioner, if one startup moment/operation duration (T moment /T hours ) label set has 2 valid values, it is indicated that the optimal control parameters of the air conditioner on the current day are: the air conditioner is to be turned on twice that day, and the air conditioner is to be turned on at each startup moment T moment and operate during the corresponding operation duration T hours each time.
  • the first neural network model, the second neural network model, and the third neural network model are built according to the historical sample data and the daily optimal control parameters of the cooling equipment.
  • the first neural network model, the second neural network model, and the third neural network model are sequentially built.
  • the cooling equipment control method according to the present disclosure may further include operations S22' and S23'.
  • the historical sample data and the daily optimal control parameters of the cooling equipment may be normalized according to the following formula to allow the data to be between 0 and 1:
  • X * X real ⁇ X min X max ⁇ X min
  • X real is a true value of an actual sample
  • X* is the normalized data
  • X max is a maximum value or an upper limit of a corresponding type of sample data
  • X min is a minimum value or a lower limit of a corresponding type of sample data.
  • training sample data set is established according to the normalized data, where the training sample data set includes a training set, a verification set, and a test set.
  • the training set, the verification set, and the test set may be established at a sample ratio of 6:2:2.
  • the operation of building the first neural network model, the second neural network model and the third neural network model according to the historical sample data and the daily optimal control parameters of the cooling equipment may include: building the first neural network model, the second neural network model and the third neural network model according to the training sample data set.
  • the operation of building the first neural network model, the second neural network model and the third neural network model according to the historical sample data and the daily optimal control parameters of the cooling equipment may include operations S231 to S233.
  • the first neural network model is built by taking the same-period historical sample data of the cooling equipment loads and a preset impact factor as a first input parameter and taking the historical sample data of the cooling equipment load on the current day as a first output parameter.
  • the impact factor may include one of the following factors or any combination of the following factors: a holiday impact factor F holiday , a tide impact factor F tide , and a regional event factor F event . Ranges of values of the holiday impact factor F holiday , the tide impact factor F tide and the regional event factor F event are all (0, 1), and may be determined according to artificial experience.
  • the holiday impact factor F holiday may be 0 on weekdays, 0.1 on the weekends, and 0.25 in the Spring Festival holiday; for an industrial park, the tide impact factor F tide may be 0.5 during working time periods, 0.7 during overtime periods, and 0.3 at late nights; and for a certain region, the regional event factor F event may be 0 under normal conditions, 0.1 in a presence of a commercial marketing activity, 0.2 in a presence of a gathering, and 0.3 in a presence of a concert.
  • the second neural network model is built by taking the same-period historical sample data of the outdoor temperatures and the historical sample data of the cooling equipment load on the current day as a second input parameter and taking the historical sample data of the indoor temperature on the current day as a second output parameter.
  • the third neural network model is built by taking the historical sample data of the indoor temperature on the current day and a preset cooling efficiency factor as a third input parameter and taking the historical sample data of the optimal control parameters of the cooling equipment on the current day as a third output parameter.
  • the optimal control parameters may include the startup moment T moment and the operation duration T hours , that is, the startup moment T moment-AC of the air conditioner, the startup moment T moment-HEE of the heat exchange equipment, the operation duration T hours-AC of the air conditioner and the operation duration T hours-HEE of the heat exchange equipment.
  • the cooling efficiency factor may include a heat-exchange cooling efficiency factor F eff1 and an air-conditioning cooling efficiency factor F eff2 .
  • both the heat-exchange cooling efficiency factor F eff1 and the air-conditioning cooling efficiency factor F eff2 are constants; and if the room environment is changed (for example, the cooling equipment is replaced or moved), the heat-exchange cooling efficiency factor F eff1 and the air-conditioning cooling efficiency factor F eff2 need to be adjusted to new constants.
  • T moment1 is 0.45
  • T hours1 is 0.05
  • T moment2 is 0.60
  • T hours2 is 0.10
  • 12 T moment /T hours data sets of the air conditioner have no valid values
  • the startup moment T moment is converted to the form of hh:mm:ss and the operation duration T hours is set to be standard duration
  • the meaning of the optimal control parameters of the cooling equipment on the current day is as follows:
  • the first neural network model, the second neural network model and the third neural network model may be deployed according to an actual operating environment.
  • the three neural network models may be all deployed on the UME to make full use of powerful computing resources in the cloud, thereby realizing real-time or online training. If necessary, the three neural network models may also be deployed at an edge side such as the FSU by adding a compute stick or by other means.
  • FIG. 5 is a schematic diagram of a cooling equipment control process according to the present disclosure.
  • the cooling equipment control method provided by the present disclosure may be used to control the operation of the cooling equipment, and includes operations S11 to S15.
  • the current outdoor temperature T Rout may be obtained through weighted calculation of a predicted temperature and a detected outdoor temperature, that is, an outdoor temperature within a preset time period before a current moment is determined first, and then the current outdoor temperature T Rout is determined according to the outdoor temperature within the preset time period before the current moment, a predicted temperature on the current day, a preset first weight and a preset second weight.
  • the preset time period may be 1 hour
  • the predicted temperature on the current day may be the temperature at that day predicted by the weather forecast.
  • the outdoor temperature T Rout the temperature predicted by the weather forecast*0.8+the measured outdoor temperature within the last one hour*0.2.
  • the FSU may collect the data such as the indoor and outdoor temperatures, the indoor and outdoor humidity, and the cooling equipment loads, and upload the data to the LTME.
  • the same-period historical sample data of the cooling equipment loads and the preset impact factor are input into the first neural network model as the first input parameter to obtain a predicted load of the cooling equipment on the current day.
  • short-period refers to the same period in the history, for example, the same moment of the current day in the last year and the same moment of the current day in the year before last can both be the same periods of the current day.
  • the same-period historical sample data L N of the cooling equipment loads, the holiday impact factor F holiday , the tide impact factor F tide , and the regional event factor F event are input into the first neural network model to obtain the predicted load L R of the cooling equipment on the current day as an output value of the first neural network model.
  • the same-period historical sample data T Rout of the outdoor temperatures and the predicted load L R of the cooling equipment on the current day are input into the second neural network model to obtain the predicted indoor temperature T Rin on the current day as an output value of the second neural network model.
  • the predicted indoor temperature on the current day and the preset cooling efficiency factor are input into the third neural network model as the third input parameter to obtain optimal control parameters of the cooling equipment on the current day.
  • the predicted indoor temperature T Rin on the current day i.e., the output value of the second neural network
  • the heat-exchange cooling efficiency factor F eff1 i.e., the heat-exchange cooling efficiency factor F eff1
  • the air-conditioning cooling efficiency factor F eff2 are input into the third neural network model to obtain the optimal control parameters of the air conditioner on the current day (i.e., the startup moment T moment-AC of the air-conditioner and the operation duration T hours-AC of the air-conditioner) and the optimal control parameters of the heat exchange equipment on the current day (i.e., the startup moment T moment-HEE of the heat exchange equipment and the operation duration T hours-HEE of the heat exchange equipment).
  • startup moment T moment may be converted to the form of hh:mm:ss and the operation duration T hours may be set to be the standard duration (e.g., X hours) in practical applications.
  • the UME sequentially runs the first neural network model, the second neural network model and the third neural network model to output the optimal control parameters of the cooling equipment on the current day.
  • the optimal control parameters of the air conditioner may include the startup moment T moment-AC of the air conditioner and the operation duration T hours-AC of the air-conditioner
  • the optimal control parameters of the heat exchange equipment may include the startup moment T moment-HEE of the heat exchange equipment and the operation duration T hours-HEE of the heat exchange equipment.
  • the optimal control parameters for each day may include at most 12 sets of the startup moment T moment-AC of the air-conditioner and the operation duration T hours-AC of the air-conditioner, and at most 24 sets of the startup moment T moment-HEE of the heat exchange equipment and the operation duration T hours-HEE of the heat exchange equipment.
  • the cooling equipment is controlled to operate according to the optimal control parameters.
  • the air conditioner is controlled to operate according to the optimal control parameters of the air conditioner
  • the heat exchange equipment is controlled to operate according to the optimal control parameters of the heat exchange equipment.
  • the prediction of the control scheme of the air conditioner and the heat exchange equipment and the linkage control of the air conditioner and the heat exchange equipment are realized by using the neural network models based on the parameters such as the current outdoor temperature, the same-period historical sample data of the cooling equipment loads, the impact factors and the cooling efficiency factors, so that the predicted control scheme has relatively high accuracy, the defect of the traditional algorithm, i.e., the traditional algorithm is difficult to improve, is overcome, active control of the air conditioner and the heat exchange equipment is realized, operation efficiency is optimized, and the energy consumption is reduced; in addition, by combining the historical data and the current measured data and considering the impact factors of special events and the cooling efficiency factors of the cooling equipment, the predicted control scheme is more accurate, can adapt to a change of the room environment, and has a widened application range.
  • FIG. 7 is a schematic diagram of a control process of the air conditioner according to the present disclosure.
  • control process of the air conditioner provided by the present disclosure includes operations S31 to S39.
  • operation S31 if a current indoor temperature is greater than the first threshold VHT, operation S36 is performed; otherwise, operation S32 is performed.
  • the operation S31 if the current indoor temperature is greater than VHT, which indicates that the current indoor temperature is too high, whether the air conditioner is operating overtime may be further determined (i.e., the operation S36 is performed); if the current indoor temperature is less than or equal to VHT, whether the current indoor temperature is too low may be further determined (i.e., the operation S32 is performed).
  • operation S32 if the current indoor temperature is less than the second threshold VLT, operation S39 is performed; otherwise, operation S33 is performed.
  • the air conditioner may be turned off due to a low temperature anomaly (i.e., the operation S39 is performed); and if the current indoor temperature is greater than or equal to the second threshold VLT, which indicates that the current indoor temperature does not cause a shutdown due to a high temperature anomaly or a shutdown due to the low temperature anomaly, whether a first high temperature prestart condition is met may be further determined (i.e., the operation S33 is performed).
  • operation S33 if the first high temperature prestart condition is met, operation S34 is performed; otherwise, the operation S31 is performed.
  • the air conditioner in a case where the current indoor temperature is less than or equal to the first threshold VHT and greater than or equal to the second threshold VLT, if the first high temperature prestart condition is met, the air conditioner is controlled to operate according to the optimal control parameters of the air conditioner on the current day (i.e., the operation S34 is performed); and if the first high temperature prestart condition is not met, the operation S31 is performed.
  • the first high temperature prestart condition may include that the startup moment T moment-AC of the air conditioner is reached, the current indoor temperature is greater than the third threshold HT AC , and the actual shutdown duration of the air conditioner is greater than the minimum continuous shutdown time MINCST of the air conditioner.
  • maximum operation duration T on-max of the air conditioner is set to be the smaller one of the operation duration T hours-AC of the air conditioner and the maximum continuous operation time MAXCOT of the air conditioner.
  • the smaller one of the operation duration T hours-AC of the air conditioner and the maximum continuous operation time MAXCOT of the air conditioner may be taken as a control parameter for actually controlling the operation of the air conditioner, so as to ensure reliability and safety of the operation of the air conditioner.
  • operation S35 the air conditioner is turned on, and operation S38 is performed.
  • operation S36 if the actual shutdown duration T off-AC of the air conditioner is greater than the minimum continuous shutdown time MINCST of the air conditioner, operation S37 is performed; otherwise, the operation S31 is performed.
  • the operation S36 in the case where the current indoor temperature is greater than the first threshold VHT, if the current actual shutdown duration T off-AC of the air conditioner is greater than the minimum continuous shutdown time MINCST of the air conditioner, which indicates that a high-temperature anomaly start condition is met, a high-temperature anomaly start operation of the air conditioner is performed (i.e., the operation 37 is performed); and if the current actual shutdown duration T off-AC of the air conditioner is less than or equal to the minimum continuous shutdown time MINCST of the air conditioner, the operation S31 is performed.
  • the maximum operation duration T on-max of the air conditioner is set to be the maximum continuous operation time MAXCOT of the air conditioner, and the operation S35 is performed.
  • the operation duration of the air conditioner is directly controlled according to the preset maximum continuous operation time MAXCOT of the air conditioner.
  • operation S38 if the actual operation duration T on-AC of the air conditioner is greater than or equal to the maximum operation duration T on-max of the air conditioner, operation S39 is performed; otherwise, the air conditioner is kept in a current state.
  • the control process of the air conditioner may further include: turning off the air conditioner if the current indoor temperature is less than the sixth threshold LT.
  • the air conditioner When the actual room temperature exceeds the first threshold VHT, the air conditioner can be turned on due to the high temperature anomaly; and when the actual room temperature is lower than the second threshold VLT, the air conditioner can be turned off due to the low temperature anomaly; when the startup moment T moment-AC of the air conditioner is reached, the actual room temperature exceeds the third threshold HT AC and an operation interval exceeds the minimum continuous shutdown time MINCST, the air conditioner can operate according to the predicted scheme output by the third neural network model, that is, the air conditioner is turned on at the startup moment T moment-AC of the air conditioner and operates for the operation duration T hours-AC of the air conditioner.
  • FIG. 8 is a schematic diagram of a control process of the heat exchange equipment according to the present disclosure.
  • control process of the heat exchange equipment includes operations S41 to S44.
  • operation S41 if the second high temperature prestart condition is met, operation S42 is performed; otherwise, the heat exchange equipment is kept in a current state.
  • the heat exchange equipment may include a direct heat exchange equipment and an indirect heat exchange equipment
  • the direct heat exchange equipment may include a fresh air system
  • the indirect heat exchange equipment may include Heat Pipe Equipment (HPE).
  • HPE Heat Pipe Equipment
  • the second high temperature prestart condition may include that the startup moment T moment-HEE of the heat exchange equipment is reached, the current indoor temperature is greater than the fourth threshold HT HEE , and a difference between the current indoor temperature and the current outdoor temperature is greater than the fifth threshold.
  • the second high temperature prestart condition includes one of the following conditions:
  • operation S43 if the actual operation duration T on-HEE of the heat exchange equipment is greater than or equal to the operation duration T hours-HEE of the heat exchange equipment, operation S44 is performed; otherwise, the heat exchange equipment is kept in the current state.
  • the control process of the heat exchange equipment may further include: turning off the heat exchange equipment if the current indoor temperature is less than the sixth threshold LT.
  • the air conditioner and the indirect heat exchange equipment can operate simultaneously, but the operation of the air conditioner and the operation of the direct heat exchange equipment are mutually exclusive, that is, the air conditioner and the direct heat exchange equipment cannot operate simultaneously.
  • the direct heat exchange equipment needs to be turned off immediately and an air valve needs to be closed immediately, so as to ensure the safety.
  • the cooling equipment control method may further include: if the air conditioner is turned on, turning off the heat exchange equipment; and if the heat exchange equipment is turned on, turning off the air conditioner.
  • control algorithm of the air conditioner and the heat exchange equipment may run on the LTME; and if necessary, the control algorithm of the air conditioner and the heat exchange equipment may also be copied to the FSU to be executed locally, in which case the UME needs to issue the cooling equipment control scheme predicted by the third neural network model to the FSU in advance.
  • control of the air conditioner and the control of the heat exchange equipment may be carried out concurrently, and the operations S11 to S14 shown in FIG. 5 are performed once before zero every day, so as to output the optimal control parameters of the cooling equipment at that day.
  • FIG. 9 is a schematic diagram of a process of re-determining and updating the optimal control parameters of the cooling equipment on the current day according to the present disclosure.
  • the cooling equipment control method according to the present disclosure may further include operations S51 to S53.
  • operation S51 if an error between actual operation parameters of the air conditioner on the current day and the optimal control parameters of the air conditioner on the current day exceeds the tenth threshold, operation S52 is performed; otherwise, the process is ended.
  • the training sample data set is updated according to the re-determined optimal control parameters of the air conditioner on the current day.
  • the optimal control parameters of the air conditioner on the current day need to be predicted again, and the training sample data set is updated according to the re-predicted optimal control parameters of the air conditioner on the current day, thereby improving timely response capability of the cooling control strategy and timeliness and accuracy of the control prediction.
  • the neural network models may be deployed and run on the cloud, and may also be continuously trained in real time or online when external parameters are continuously changed, so that prediction accuracy of the neural network models can be continuously improved, and the neural network models can be trained and adjusted to adapt to abnormal conditions such as a change of the room environment.
  • the air conditioner and the heat exchange equipment may be switched in a presence of failures, and correspondingly, the cooling equipment control method according to the present disclosure may further include: if one type of the cooling equipment that is operating currently fails and the other type of the cooling equipment is normal, turning off the failed cooling equipment, and turning on the normal cooling equipment; and if both of the two types of the cooling equipment that are operating currently fail, turning on the cooling equipment whose failure is eliminated during elimination of failures. That is, if the cooling equipment that is turned on currently fails, the failed cooling equipment is turned off, and the normal cooling equipment is turned on, and when the failure is eliminated, the previously failed cooling equipment is turned on again, and the other cooling equipment is turned off.
  • the air conditioner and the heat exchange equipment as backups of each other to be turned on and operated in the presence of the failures, the danger of abnormal high temperature of the machine room can be avoided.
  • the cooling equipment control method may further include: if the current indoor temperature is less than the sixth threshold LT and the actual shutdown duration of the cooling equipment is greater than the seventh threshold, training the second neural network model according to currently acquired sample data, which includes the outdoor temperatures, the indoor temperatures and the cooling equipment loads. That is, the real-time or online training of the model can be supported when environmental conditions are good (for example, the FSU and the UME are connected on a fast Ethernet, and the computing power resources in the cloud are sufficient).
  • the second neural network model can be trained online in real time according to the data collected in real time such as the outdoor temperatures, the cooling equipment loads and the indoor temperatures.
  • the cooling equipment control method may further include: adding the sample data acquired on the current day and the actual operation parameters of the cooling equipment on the current day to the training sample data set, so as to train the first neural network model and the third neural network model according to the training sample data set.
  • the training set and the test set may be enriched, so that the prediction accuracy of the models can be improved when the first neural network model and the third neural network model are trained online.
  • the FSU may automatically run a built-in temperature startup-shutdown control algorithm, or may receive and store a cooling control plan which is issued by the UME in advance, and locally run a cooling linkage control algorithm copied from the UME.
  • the cooling equipment control method may further include: deploying the first neural network model, the second neural network model and the third neural network model on the FSU to determine the optimal control parameters of the cooling equipment on the current day when the FSU fails to communicate with the UME, and controlling the cooling equipment to operate according to the optimal control parameters.
  • a base station room in which the heat productivity of communication devices in the room is less than 10KW is generally a data base station room, or a transmission base station room, or an exchange base station room of an operator.
  • the base station room is generally a data base station room, or a transmission base station room, or an exchange base station room of an operator.
  • an air conditioner is originally installed as the cooling equipment in the base station room; and in order to reduce the energy consumption of the air conditioner, an indirect heat exchange equipment, i.e. the intelligent HPE, is added after an external environment, the heat productivity and an installation condition of the base station room are considered, so as to provide a solution for the room through the linkage control of the air conditioner and the HPE.
  • the HPE adopts the heat pipe technology which does not need mechanical cooling and can basically keep the indoor-outdoor temperature difference to be about 6 °C, so that the HPE is applicable for more than 90% of the time all the year round. Meanwhile, the energy consumption of the components of the HPE is much less than that of the traditional compressor-type air conditioner, and the energy consumption of the HPE is merely about 1/5 of the energy consumption of the original air conditioner system, so that the power consumption of the air conditioner can be greatly saved.
  • an application environment of a machine room will be fully considered no matter for building a new machine room or expanding an existing machine room, so as to select proper heat exchange equipment.
  • Adopting the indirect heat exchange equipment such as the HPE or a heat exchanger can realize isolation of an indoor environment from an outdoor environment and has a wide application range, but needs a relatively high initial cost.
  • a fresh air system Under conditions of good air quality (no salt spray or corrosive gas pollution exists), low temperature and humidity and strong regular maintenance capability of a user, a fresh air system can be selected as a direct heat exchange equipment.
  • another application scenario of the present disclosure is: a base station room adopting linkage control of the fresh air system and the air conditioner for cooling.
  • the cooling equipment control solution provided by the present disclosure, based on a big data technology and the neural network technology, by fully considering the data such as the current indoor/outdoor temperatures and humidity and system loads and calculating by the neural network models according to the load prediction, the weather forecast and the same-period historical sample data, the cooling equipment loads and the indoor temperatures can be predicted in advance, and the optimal plan for the linkage control of the cooling equipment on the current day can be output and then be used together with the traditional control strategy, so that the predictable active control of the air conditioner and the heat exchange equipment in the machine room can be realized, thereby optimizing the control, saving the energy, and reducing the power consumption.
  • the predictable active linkage control of the heat exchange equipment and the air conditioner is realized, the operation time and the startup times of the air conditioner are significantly reduced; meanwhile, the operation temperatures of the devices in the machine room can be increased to controllable safety ranges of 30°C to 40 °C, so that the energy consumption of the cooling equipment is further reduced.
  • the predictable active linkage control of the air conditioner and the heat exchange equipment can save nearly 10,000kwh for a communication base station every year and reduce average electricity consumption by 40%, that is, if calculated by taking 10% of 5 million base stations as an example, an electricity expense of 5 billion yuan and 1.35 million tons of carbon emissions can be saved every year, thus producing remarkable economic and social benefits.
  • the present disclosure further provides a cooling equipment control device.
  • FIG. 10 and FIG. 11 are schematic structural diagrams of a cooling equipment control device according to the present disclosure.
  • the cooling equipment control device includes a first processing module 101, a second processing module 102, and a control module 103.
  • the first processing module 101 is configured to determine a current outdoor temperature.
  • the second processing module 102 is configured to: input same-period historical sample data of cooling equipment loads and a preset impact factor into a first neural network model as a first input parameter to obtain a predicted load of cooling equipment on a current day; input same-period historical sample data of outdoor temperatures and the predicted load of the cooling equipment on the current day into a second neural network model as a second input parameter to obtain a predicted indoor temperature on the current day; and input the predicted indoor temperature on the current day and a preset cooling efficiency factor into a third neural network model as a third input parameter to obtain optimal control parameters of the cooling equipment on the current day.
  • the control module 103 is configured to control the cooling equipment to operate according to the optimal control parameters.
  • the cooling equipment control device may further include a model building module 104.
  • the model building module 104 is configured to build the first neural network model, the second neural network model, and the third neural network model in an initialization stage.
  • the model building module 104 may be configured to: acquire historical sample data which includes outdoor temperatures, indoor temperatures, and cooling equipment loads; subject the historical sample data to simulation, and obtain daily optimal control parameters of the cooling equipment by calculation; and build the first neural network model, the second neural network model, and the third neural network model according to the historical sample data and the daily optimal control parameters of the cooling equipment.
  • the model building module 104 may be further configured to: after subjecting the historical sample data to the simulation and obtaining the daily optimal control parameters of the cooling equipment by calculation, and before building the first neural network model, the second neural network model, and the third neural network model according to the historical sample data and the daily optimal control parameters of the cooling equipment, normalize the historical sample data and the daily optimal control parameters of the cooling equipment; and establish training sample data set according to the normalized data, where the training sample data set includes a training set, a verification set, and a test set.
  • the model building module 104 may be configured to build the first neural network model, the second neural network model, and the third neural network model according to the training sample data set.
  • the model building module 104 may be configured to: build the first neural network model by taking the same-period historical sample data of the cooling equipment loads and the preset impact factor as the first input parameter and taking historical sample data of the cooling equipment load on the current day as a first output parameter; build the second neural network model by taking the same-period historical sample data of the outdoor temperatures and the historical sample data of the cooling equipment load on the current day as the second input parameter and taking historical sample data of an indoor temperature on the current day as a second output parameter; and build the third neural network model by taking the historical sample data of the indoor temperature on the current day and the preset cooling efficiency factor as the third input parameter and taking historical sample data of the optimal control parameters of the cooling equipment on the current day as a third output parameter.
  • the sample data may include simulation data and sampled data.
  • the simulation data is the data obtained by simulating operation of the cooling equipment when an indoor temperature is greater than a preset third threshold.
  • the sampled data is the data sampled when an indoor temperature is less than a preset sixth threshold and actual shutdown duration of the cooling equipment is greater than a preset seventh threshold.
  • the first processing module 101 may be configured to determine an outdoor temperature within a preset time period before a current moment; and determine the current outdoor temperature according to the outdoor temperature within the preset time period before the current moment, a predicted temperature on the current day, a preset first weight and a preset second weight.
  • the optimal control parameters may include startup moment and operation duration.
  • the impact factor may include one of the following factors or any combination of the following factors: a holiday impact factor, a tide impact factor, and a regional event factor.
  • the control module 103 may be configured to: if a current indoor temperature is less than or equal to a preset first threshold and greater than or equal to a preset second threshold and a first high temperature prestart condition is met, set maximum operation duration of an air conditioner to be the smaller one of operation duration of the air conditioner and a preset maximum continuous operation time of the air conditioner, and turn on the air conditioner, with the second threshold being less than the first threshold; and if actual operation duration of the air conditioner is greater than or equal to the maximum operation duration of the air conditioner, turn off the air conditioner.
  • the first high temperature prestart condition may include that a startup moment of the air conditioner is reached, the current indoor temperature is greater than the preset third threshold, and actual shutdown duration of the air conditioner is greater than a preset minimum continuous shutdown time of the air conditioner.
  • the control module 103 may be further configured to: during a process of controlling the cooling equipment to operate according to the optimal control parameters, if the current indoor temperature is greater than the first threshold and the actual shutdown duration of the air conditioner is greater than the minimum continuous shutdown time of the air conditioner, set the maximum operation duration of the air conditioner to be the maximum continuous operation time of the air conditioner, and turn on the air conditioner; and/or, if the current indoor temperature is less than the second threshold, turn off the air conditioner.
  • the control module 103 may be further configured to: if a second high temperature prestart condition is met, turn on heat exchange equipment; and if actual operation duration of the heat exchange equipment is greater than or equal to operation duration of the heat exchange equipment, turn off the heat exchange equipment.
  • the second high temperature prestart condition may include that a startup moment of the heat exchange equipment is reached, the current indoor temperature is greater than a preset fourth threshold, and a difference between the current indoor temperature and the current outdoor temperature is greater than a preset fifth threshold.
  • the second high temperature prestart condition may include one of the following conditions: the startup moment of the heat exchange equipment is reached, the current indoor temperature is greater than the preset fourth threshold, the difference between the current indoor temperature and the current outdoor temperature is greater than a preset eighth threshold, and the eighth threshold is greater than the fifth threshold; and the startup moment of the heat exchange equipment is reached, the current indoor temperature is greater than the preset fourth threshold, the difference between the current indoor temperature and the current outdoor temperature is greater than the preset eighth threshold, and a current indoor humidity is less than or equal to a preset ninth threshold.
  • the startup moment of the air conditioner is different from that of the heat exchange equipment; and the control module 103 may be further configured to: if the air conditioner is turned on, turn off the heat exchange equipment; and if the heat exchange equipment is turned on, turn off the air conditioner.
  • the control module 103 may be further configured to: after controlling the cooling equipment to operate according to the optimal control parameters, if an error between actual operation parameters of the air conditioner on the current day and the optimal control parameters of the air conditioner on the current day exceeds a preset tenth threshold, instruct the second processing module 102 to re-determine the optimal control parameters of the air conditioner on the current day; and update the training sample data set according to the re-determined optimal control parameters of the air conditioner on the current day.
  • the control module 103 may be further configured to: if one type of the cooling equipment that is operating currently fails and the other type of the cooling equipment is normal, turn off the failed cooling equipment, and turn on the normal cooling equipment; and if both of the two types of the cooling equipment that are operating currently fail, turn on the cooling equipment whose failure is eliminated during elimination of failures
  • the second processing module 102 may be further configured to: if the current indoor temperature is less than the preset sixth threshold and the actual shutdown duration of the cooling equipment is greater than the preset seventh threshold, train the second neural network model according to currently acquired sample data which includes outdoor temperatures, indoor temperatures and cooling equipment loads.
  • the present disclosure further provides a computer device, including one or more processors, and a storage device having stored thereon one or more programs, which, when executed by the one or more processors, cause the one or more processors to carry out the cooling equipment control method provided by the present disclosure.
  • the present disclosure further provides a computer-readable medium having stored thereon a computer program, which, when executed by a processor, causes the processor to carry out the cooling equipment control method provided by the present disclosure.
  • the functional modules/units in all or some of the operations and devices disclosed in the above method may be implemented as software, firmware, hardware, or suitable combinations thereof. If implemented as hardware, the division between the functional modules/units stated above is not necessarily corresponding to the division of physical components; and for example, one physical component may have a plurality of functions, or one function or operation may be performed through cooperation of several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit.
  • a processor such as a central processing unit, a digital signal processor or a microprocessor
  • Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a transitory medium).
  • a computer storage medium or a non-transitory medium
  • a communication medium or a transitory medium
  • computer storage medium includes volatile/nonvolatile and removable/non-removable media used in any method or technology for storing information (such as computer-readable instructions, data structures, program modules and other data).
  • the computer storage medium includes, but is not limited to, a Random Access Memory, a Read-Only Memory, an Electrically Erasable Programmable Read-Only Memory (EEPROM), a flash memory or other storage technology, a Compact Disc Read Only Memory (CD-ROM), a Digital Versatile Disc (DVD) or other optical discs, a magnetic cassette, a magnetic tape, a magnetic disk or other magnetic storage devices, or any other medium which can be configured to store desired information and can be accessed by a computer.
  • the communication media generally include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier wave or other transmission mechanism, and may include any information delivery medium.

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EP21821753.7A 2020-06-10 2021-06-10 Method and apparatus for controlling refrigeration device, computer device, and computer readable medium Pending EP4166862A1 (en)

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