Energy consumption adjustment method and device of device, electronic device and storage medium

Abstract

The application discloses an energy consumption adjustment method and device of equipment, electronic equipment and storage medium, belongs to the field of energy consumption adjustment, and solves the problem that real-time dynamic adjustment of energy consumption of a data center infrastructure cannot be realized in related technologies, and optimal energy saving cannot be realized. The method comprises the following steps: collecting working parameters of at least one refrigeration device, indoor and outdoor wet bulb temperatures and internet IT loads in a preset time period; inputting the changes of the indoor and outdoor wet bulb temperatures, the IT loads and the working parameters of the refrigeration device into an energy consumption model of the refrigeration device which is obtained through pre-training, to obtain a change curve of power of the refrigeration device; determining minimum values of the power of each refrigeration device based on the change curve; and adjusting the working parameters of the refrigeration device to target working parameters corresponding to the minimum values, so that the target working parameters are used to adjust the energy consumption of the refrigeration device.

Description

Technical Field

[0001] This application belongs to the field of energy consumption adjustment, specifically relating to a method, apparatus, electronic device and storage medium for adjusting the energy consumption of a device. Background Technology

[0002] The most important purpose of data center infrastructure is to provide a continuous and stable power and cooling supply to ensure the secure operation of data. However, the large size and high equipment density of data centers result in enormous power consumption for both equipment and cooling. For example, a medium-sized data center consumes nearly 10 million kilowatt-hours of electricity per month, with cooling equipment accounting for over 35% of the energy consumption. This mainly includes high-pressure chillers, plate heat exchangers, cooling towers, cooling water pumps, chilled water pumps, and air conditioning terminals. Achieving the optimal cooling mode based on the load of the computer room and changes in indoor and outdoor temperatures, while ensuring cooling safety, is a pressing problem that needs to be solved in related technical fields.

[0003] To address the aforementioned issues, Building Automation (BA) systems exist as a relevant technology. BA systems are quite common in data centers, but they can only monitor, view, and manually control high-pressure chillers, water pumps, and cooling towers. They cannot achieve automated or intelligent adjustments, nor can they achieve intelligent linkage with internal and external environmental parameters.

[0004] In summary, BA systems are extensive support systems with a high degree of human intervention, which cannot achieve real-time dynamic adjustment of energy consumption for data center infrastructure, and thus cannot achieve the goal of optimal energy saving. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for adjusting the energy consumption of a device, which can solve the problem in related technologies that it is impossible to dynamically adjust the energy consumption of data center infrastructure in real time, thus making it impossible to achieve optimal energy saving.

[0006] In a first aspect, embodiments of this application provide a method for adjusting the energy consumption of a device. The method includes: collecting the operating parameters of at least one refrigeration device, indoor and outdoor wet-bulb temperatures, and Internet IT load within a preset time period; inputting the changes in the indoor and outdoor wet-bulb temperatures, the IT load, and the operating parameters of the refrigeration device into a pre-trained energy consumption model of the refrigeration device to obtain a power change curve of the refrigeration device; determining the minimum power of each refrigeration device based on the change curve; and adjusting the operating parameters of the refrigeration device to a target operating parameter corresponding to the minimum value, wherein the target operating parameter is used to adjust the energy consumption of the refrigeration device.

[0007] Secondly, embodiments of this application provide an energy consumption adjustment device for a device, comprising: a data acquisition module for acquiring the operating parameters, indoor and outdoor wet-bulb temperatures, and Internet IT load of at least one refrigeration device within a preset time period; an input module for inputting the changes in the indoor and outdoor wet-bulb temperatures, the IT load, and the operating parameters of the refrigeration device into a pre-trained energy consumption model of the refrigeration device to obtain a power change curve of the refrigeration device; a determination module for determining the minimum power of each of the refrigeration devices based on the change curve; and an adjustment module for adjusting the operating parameters of the refrigeration device to a target operating parameter corresponding to the minimum value, wherein the target operating parameter is used to adjust the energy consumption of the refrigeration device.

[0008] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0009] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0010] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0011] In this embodiment, the operating parameters of at least one cooling device, indoor and outdoor wet-bulb temperatures, and internet IT load are collected within a preset time period. The changes in the indoor and outdoor wet-bulb temperatures, the IT load, and the operating parameters of the cooling devices are input into a pre-trained energy consumption model of the cooling devices to obtain a power variation curve for each cooling device. Based on the variation curve, the minimum power of each cooling device is determined. The operating parameters of the cooling devices are then adjusted to target operating parameters corresponding to the minimum value. These target operating parameters are used to adjust the energy consumption of the cooling devices. The power variation curve of each cooling device within the preset time period can be determined using the pre-trained energy consumption model of each cooling device. Furthermore, the minimum power of each cooling device within the preset time period can be determined using the power variation curve of each cooling device. Based on this, the target operating parameters of each cooling device corresponding to the minimum power can be determined. In this way, adjusting the operating parameters of each cooling device to the target operating parameters solves the problem in related technologies where real-time dynamic adjustment of energy consumption for data center infrastructure (cooling devices) is not possible, thus hindering optimal energy saving. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating a method for adjusting the energy consumption of a device according to an embodiment of this application;

[0013] Figure 2 This is a flowchart illustrating another energy consumption adjustment method for a device provided in an embodiment of this application;

[0014] Figure 3 This is an energy consumption variation graph provided in an embodiment of this application;

[0015] Figure 4 This is yet another energy consumption variation diagram provided in the embodiments of this application;

[0016] Figure 5 This is another energy consumption variation diagram provided in an embodiment of this application;

[0017] Figure 6 This is a schematic diagram of the structure of an energy consumption adjustment device according to an embodiment of this application;

[0018] Figure 7 This is a schematic diagram of the structure of an electronic device according to another embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The energy consumption adjustment method, apparatus, electronic device, and storage medium of this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0021] Specifically, the most important purpose of establishing data center infrastructure is to provide continuous and stable power and cooling to ensure the secure operation of data. However, the large size and high equipment density of data centers result in enormous power consumption for both equipment and cooling. For example, a medium-sized data center consumes nearly 10 million kilowatt-hours of electricity per month, with cooling equipment accounting for over 35% of the energy consumption. This mainly includes high-pressure chillers, plate exchangers, cooling towers, cooling water pumps, chilled water pumps, and air conditioning terminals. How to achieve the optimal cooling mode based on the load of the computer room and changes in indoor and outdoor temperatures, while ensuring cooling safety, is a pressing problem that needs to be solved in related technical fields.

[0022] To address the aforementioned issues, Building Automation (BA) systems exist as a relevant technology. BA systems are quite common in data centers, but they can only monitor, view, and manually control high-pressure chillers, water pumps, and cooling towers. They cannot achieve automated or intelligent adjustments, nor can they achieve intelligent linkage with internal and external environmental parameters.

[0023] In summary, BA systems are extensive support systems with a high degree of human intervention, which cannot achieve real-time dynamic adjustment of energy consumption for data center infrastructure, and thus cannot achieve the goal of optimal energy saving.

[0024] To address this issue, this application collects the operating parameters of at least one cooling device, indoor and outdoor wet-bulb temperatures, and Internet (IT) load over a preset time period. The changes in these parameters are input into a pre-trained energy consumption model of the cooling device to obtain a power variation curve for the cooling device. Based on this curve, a minimum power value for each cooling device is determined. The operating parameters of the cooling devices are then adjusted to target operating parameters corresponding to these minimum values. These target operating parameters are used to adjust the energy consumption of the cooling devices. The pre-trained energy consumption models for each cooling device can be used to determine the power variation curves of each cooling device over the preset time period. These curves can then be used to determine the minimum power of each cooling device within the preset time period. Based on this, the target operating parameters for each cooling device corresponding to the minimum power can be determined. By adjusting the operating parameters of each cooling device to target operating parameters, this solves the problem in related technologies where real-time dynamic adjustment of energy consumption for data center infrastructure (cooling devices) is not possible, thus hindering optimal energy saving.

[0025] Figure 1 This illustration shows an embodiment of the present invention providing a method for adjusting the energy consumption of a device. This method can be executed by an electronic device, which may include a server and / or a terminal device, wherein the terminal device may be, for example, a mobile phone terminal. In other words, the method can be executed by software or hardware installed in the electronic device, and the method includes the following steps:

[0026] Step 101: Collect the operating parameters of at least one refrigeration device, indoor and outdoor wet-bulb temperatures, and Internet IT load within a preset time period.

[0027] Optionally, the length of the preset time period can be set according to specific needs, and there is no specific limitation here.

[0028] Optionally, refrigeration equipment may include chillers, cooling water pumps, chilled water pumps, and cooling towers.

[0029] Specifically, cooling equipment can be the cooling equipment of a data center, and IT load can be the IT load undertaken by the data center.

[0030] Step 102: Input the changes in the indoor and outdoor wet-bulb temperatures, the IT load, and the operating parameters of the refrigeration equipment into the pre-trained energy consumption model of the refrigeration equipment to obtain the power change curve of the refrigeration equipment.

[0031] Specifically, by inputting the changes in the operating parameters of the refrigeration equipment, indoor and outdoor wet-bulb temperatures, and IT load into the energy consumption model of the pre-trained refrigeration equipment, the power change value of the refrigeration equipment can be output, and the power change curve of the refrigeration equipment can be obtained. Thus, the power change curve can be used to reflect the trend of the power of each refrigeration equipment with the changes in the operating parameters of each refrigeration equipment, indoor and outdoor wet-bulb temperatures, and IT load within a preset time period.

[0032] Step 103: Based on the change curve, determine the minimum power of each of the refrigeration devices.

[0033] Step 104: Adjust the operating parameters of the refrigeration equipment to the target operating parameters corresponding to the minimum value, wherein the target operating parameters are used to adjust the energy consumption of the refrigeration equipment.

[0034] Optionally, the operating parameters of each refrigeration device can be adjusted using proportional-integral-differential (PID) control. Since PID control technology is a relatively mature technology in this field, it will not be described in detail here.

[0035] This application collects operating parameters of at least one cooling device, indoor and outdoor wet-bulb temperatures, and internet IT load within a preset time period. The changes in these parameters are input into a pre-trained energy consumption model of the cooling device to obtain a power variation curve. Based on this curve, a minimum power value for each cooling device is determined. The operating parameters of the cooling devices are then adjusted to target operating parameters corresponding to these minimum values. These target operating parameters are used to adjust the energy consumption of the cooling devices. The pre-trained energy consumption models of each cooling device can be used to determine the power variation curve of each cooling device within the preset time period. Furthermore, the power variation curves of each cooling device can be used to determine the minimum power of each cooling device within the preset time period. Based on this, the target operating parameters corresponding to the minimum power of each cooling device can be determined. By adjusting the operating parameters of each cooling device to the target operating parameters, the problem in related technologies of being unable to achieve real-time dynamic adjustment of energy consumption for data center infrastructure (cooling devices), thus hindering optimal energy saving, can be solved.

[0036] In one optional implementation, before inputting the changes in the indoor and outdoor wet-bulb temperatures, the IT load, and the operating parameters of the refrigeration equipment into a pre-trained energy consumption model of the refrigeration equipment to obtain the power change curve of the refrigeration equipment, the method further includes:

[0037] A first training sample set is obtained, and an artificial neural network is trained using the first training sample set to obtain an energy consumption model of the cooling water pump. The samples in the first training sample set include first sample data and first sample labels. The first sample data includes the changes in the operating parameters of the cooling water pump and the IT load during a historical time period. The first sample label includes the changes in the power of the cooling water pump during a historical time period.

[0038] A second training sample set is obtained, and the artificial neural network is trained using the second training sample set to obtain the energy consumption model of the chilled water pump. The samples in the second training sample set include second sample data and second sample labels. The second sample data includes the changes in the operating parameters of the chilled water pump and the IT load during the historical time period. The second sample label includes the power changes of the chilled water pump during the historical time period.

[0039] A third training sample set is obtained, and the artificial neural network is trained using the third training sample set to obtain the energy consumption model of the cooling tower fan. The samples in the third training sample set include third sample data and third sample labels. The third sample data includes the changes in the operating parameters of the cooling tower fan and the IT load during the historical time period. The third sample labels include the changes in the power of the cooling tower fan.

[0040] A fourth training sample set is obtained, and the artificial neural network is trained using the fourth training sample set to obtain a cooling tower model. The samples in the fourth training sample set include fourth sample data and fourth sample labels. The fourth sample data includes the changes in the inlet temperature of the cooling water, the changes in the operating parameters of the cooling water pump, the changes in the operating parameters of the cooling tower fan, the indoor and outdoor wet-bulb temperatures, and the IT load during the historical time period. The fourth sample labels include the changes in the outlet temperature of the cooling water during the historical time period.

[0041] A fifth training sample set is obtained, and the artificial neural network is trained using the fifth training sample set to obtain the energy consumption model of the chiller unit. The samples in the fifth training sample set include fifth sample data and fifth sample labels. The fifth sample data includes the changes in the operating parameters of the chilled water pump, the changes in the operating parameters of the cooling water pump, the changes in the outlet temperature of the cooling water, and the IT load during the historical time period. The fifth sample label includes the changes in the power of the chiller unit during the historical time period.

[0042] Specifically, the operating parameters of the cooling water pumps in the first training sample can include the cooling water flow rate and the head of the cooling water pump; the operating parameters of the chilled water pumps in the second training sample can include the chilled water flow rate and the head of the chilled water pump; the operating parameters of the fans in the third training sample can include the air flow rate; the operating parameters of the cooling water pumps in the fourth training sample can include the cooling water flow rate, and the operating parameters of the cooling tower fans can include the air flow rate; the operating parameters of the chilled water pumps in the fifth training sample can include the chilled water flow rate and the chilled water inlet and outlet temperatures, and the operating parameters of the cooling water pumps can include the cooling water flow rate.

[0043] Optionally, before training each energy consumption model, historical parameter data of each refrigeration device can be collected. The historical parameter data constitutes a historical training sample set. The historical training sample set is used for training to obtain the basic logical relationship between the input and output parameters of the energy consumption model of each refrigeration device. This basic logical relationship can be used as the template for the energy consumption model of each refrigeration device. Based on this, the template can be continuously trained using historical data.

[0044] Furthermore, real-time parameter data of each refrigeration device can be collected within a certain time period. Then, self-learning algorithms such as annealing, traversal, or particle swarm optimization can be used to train the real-time parameter data to obtain the real-time logical relationship between the input and output parameters of the energy consumption model of each refrigeration device. This real-time logical relationship is automatically compared and corrected with the original energy consumption model of each refrigeration device to generate a better energy consumption model for each refrigeration device compared to the original model.

[0045] Furthermore, the first to fifth training sample sets can be obtained. These sets can include the changes in parameter data of each refrigeration device. This allows the energy consumption model of each refrigeration device trained based on these sets to reflect the power change trend. The minimum power of the refrigeration device within a preset time period can be determined based on the power change trend. Naturally, the operating parameters of each refrigeration device corresponding to the minimum power can also be determined, thus obtaining the optimal energy consumption adjustment method for each refrigeration device.

[0046] In one optional implementation, determining the minimum power of each of the refrigeration devices based on the variation curves includes: determining the minimum power of the chilled water pump based on the power variation curve of the chilled water pump; determining a first target operating parameter of the chilled water pump corresponding to the minimum power; and, with the first target operating parameter fixed, determining the minimum power of the cooling water pump, the cooling tower fan, and the chiller unit based on the power variation curves of the cooling water pump, the cooling tower fan, and the chiller unit.

[0047] The step of adjusting the operating parameters of the refrigeration equipment to the target operating parameters corresponding to the minimum value includes: determining the second target operating parameters of the cooling water pump corresponding to the minimum power value and the third target operating parameters corresponding to the cooling tower fan; adjusting the operating parameters of each of the refrigeration equipment to their corresponding target operating parameters, wherein the target operating parameters include the first target operating parameter, the second target operating parameter and the third target operating parameter.

[0048] In this way, by first determining the target operating parameters corresponding to the minimum power of the cooling water pump, and then keeping the target operating parameters of the cooling water pump unchanged, the target operating parameters corresponding to the minimum power of other refrigeration equipment are determined, so that each refrigeration equipment can operate according to the determined target operating parameters, thereby making the output power sufficiently small.

[0049] In one optional implementation, the collection of operating parameters of the refrigeration equipment within a preset time interval includes: collecting operating parameters of the refrigeration equipment within a preset time period based on a pre-established data reading priority of the refrigeration equipment.

[0050] Optionally, the data reading priority of each refrigeration device can be set by engineers according to the specific application scenario, and there is no specific limitation here.

[0051] Optionally, the collected working parameters can be stored in an intermediate database for preservation.

[0052] In this way, by pre-establishing the data reading priority of the refrigeration equipment, the real-time data acquisition response and transmission of the refrigeration equipment can be accelerated during the collection of the operating parameters of the refrigeration equipment within a preset time period, thereby improving the timeliness of the data response.

[0053] Optionally, during the process of collecting the operating parameters of the refrigeration equipment within a preset time period, the sampling period of the operating parameters can be made smaller than a preset threshold, thereby increasing the throughput of the collected operating parameters and making the power change curves of each refrigeration equipment obtained based on the data more accurate.

[0054] In one optional implementation, after collecting the operating parameters of at least one refrigeration device, indoor and outdoor wet-bulb temperatures, and Internet IT load within the preset time period, the method further includes: generating a serial number for the operating parameters, wherein the serial number includes the priority of the operating parameters.

[0055] In this way, by generating serial numbers for the working parameters, the timeliness of each collected working parameter can be determined based on the serial number. The serial number of the working parameter includes the priority of the working parameter, and the energy consumption data of each refrigeration device can be compared in real time.

[0056] Optional, see below Figure 2 An embodiment of this application will be described, which includes the following steps:

[0057] This embodiment is implemented based on an energy-saving platform architecture, which includes a physical layer, a data layer, a business layer, and an application layer.

[0058] Step 201: Based on the pre-established data reading priority of the refrigeration equipment, collect the operating parameters of the refrigeration equipment within a preset time period.

[0059] Optionally, the data reading priority of each refrigeration device can be set by engineers according to the specific application scenario, and there is no specific limitation here.

[0060] Optionally, the collected working parameters can be stored in an intermediate database for preservation.

[0061] Optionally, when addressing the electricity meters of each refrigeration device, the meter's identification (ID) and number can be sorted out, and the meter's address can be formed using a combination of Internet Protocol (IP) + port number + hardware encoding to address the electricity meter.

[0062] Optionally, the collected data can be stored in the physical layer of the energy-saving platform architecture, and the physical layer can also transmit the collected data.

[0063] Optionally, the data layer of the energy-saving platform architecture can be used to classify and store the data collected and transmitted from the physical layer according to standards.

[0064] Step 202: Generate the serial number of the working parameters.

[0065] The serial number includes the priority of the working parameters.

[0066] Step 203: Obtain at least one training sample set, and train the artificial neural network using the training sample set to obtain the energy consumption model of each refrigeration device.

[0067] Specifically, such as Figure 3 As shown, when the IT load and water flow rate remain constant, the lower the cooling water temperature, the lower the energy consumption of the chiller unit, but the higher the energy consumption of the cooling tower fan. Therefore, for each load requirement, there exists an optimal cooling tower fan airflow, i.e., an optimal cooling water temperature, to minimize the total energy consumption of the chiller unit (cooler) and the cooling tower fan.

[0068] Specifically, such as Figure 4 As shown, when the IT load and cooling water outlet temperature remain constant, a higher water flow rate results in lower chiller energy consumption, but higher cooling water pump energy consumption. Therefore, for each load requirement, there exists an optimal cooling water flow rate that minimizes the total energy consumption of the chiller and cooling water pump.

[0069] Specifically, such as Figure 5 As shown, when the IT load and chilled water inlet temperature remain constant, a higher water flow rate results in a lower chilled water outlet temperature and lower energy consumption for the chiller unit, but also higher energy consumption for the chilled water pump. Therefore, for a given load requirement, there exists an optimal chilled water flow rate that minimizes the total energy consumption of the chiller unit and chilled water pump.

[0070] Specifically, a first training sample set is obtained, and an artificial neural network is trained using the first training sample set to obtain an energy consumption model of the cooling water pump. The samples in the first training sample set include first sample data and first sample labels. The first sample data includes the changes in the operating parameters of the cooling water pump and the IT load during a historical time period. The first sample label includes the changes in the power of the cooling water pump during a historical time period.

[0071] A second training sample set is obtained, and the artificial neural network is trained using the second training sample set to obtain the energy consumption model of the chilled water pump. The samples in the second training sample set include second sample data and second sample labels. The second sample data includes the changes in the operating parameters of the chilled water pump and the IT load during the historical time period. The second sample label includes the power changes of the chilled water pump during the historical time period.

[0072] A third training sample set is obtained, and the artificial neural network is trained using the third training sample set to obtain the energy consumption model of the cooling tower fan. The samples in the third training sample set include third sample data and third sample labels. The third sample data includes the changes in the operating parameters of the cooling tower fan and the IT load during the historical time period. The third sample labels include the changes in the power of the cooling tower fan.

[0073] A fourth training sample set is obtained, and the artificial neural network is trained using the fourth training sample set to obtain a cooling tower model. The samples in the fourth training sample set include fourth sample data and fourth sample labels. The fourth sample data includes the changes in the inlet temperature of the cooling water, the changes in the operating parameters of the cooling water pump, the changes in the operating parameters of the cooling tower fan, the indoor and outdoor wet-bulb temperatures, and the IT load during the historical time period. The fourth sample labels include the changes in the outlet temperature of the cooling water during the historical time period.

[0074] A fifth training sample set is obtained, and the artificial neural network is trained using the fifth training sample set to obtain the energy consumption model of the chiller unit. The samples in the fifth training sample set include fifth sample data and fifth sample labels. The fifth sample data includes the changes in the operating parameters of the chilled water pump, the changes in the operating parameters of the cooling water pump, the changes in the outlet temperature of the cooling water, and the IT load during the historical time period. The fifth sample label includes the changes in the power of the chiller unit during the historical time period.

[0075] Specifically, the parameters of the energy consumption model for cooling water pumps, chilled water pumps, and cooling tower fans may include rated power, impeller diameter, rated speed, rated power, kinetic energy of the pump impeller at different speeds, pump impeller vibration velocity, and pump current; the parameters of the cooling tower model may include the number of heat transfer units in the cooling tower; the parameters of the energy consumption model for chiller units may include rated cooling capacity, rated chilled water flow rate, rated cooling water flow rate, rated chilled water inlet and outlet temperatures, rated cooling water inlet and outlet temperatures, and changes in various currents of the unit.

[0076] Step 204: Input the changes in the indoor and outdoor wet-bulb temperatures, the IT load, and the operating parameters of the refrigeration equipment into the pre-trained energy consumption model of the refrigeration equipment to obtain the power change curve of the refrigeration equipment.

[0077] Specifically, by inputting the changes in the operating parameters of the refrigeration equipment, indoor and outdoor wet-bulb temperatures, and IT load into the energy consumption model of the pre-trained refrigeration equipment, the power change value of the refrigeration equipment can be output, and the power change curve of the refrigeration equipment can be obtained. Thus, the power change curve can be used to reflect the trend of the power of each refrigeration equipment with the changes in the operating parameters of each refrigeration equipment, indoor and outdoor wet-bulb temperatures, and IT load within a preset time period.

[0078] Step 205: Based on the change curve, determine the minimum power of each of the refrigeration devices.

[0079] Specifically, the minimum power of the chilled water pump can be determined based on the power variation curve of the chilled water pump; the first target operating parameter of the chilled water pump corresponding to the minimum power can be determined; and when the first target operating parameter is fixed, the minimum power of the cooling water pump, the cooling tower fan, and the chiller unit can be determined based on the power variation curves of the cooling water pump, the cooling tower fan, and the chiller unit.

[0080] Optionally, this step can be performed at the business layer within the energy-saving platform architecture.

[0081] Step 206: Adjust the operating parameters of the refrigeration equipment to the target operating parameters corresponding to the minimum value.

[0082] Specifically, the second target operating parameter of the cooling water pump corresponding to the minimum power value and the third target operating parameter of the cooling tower fan can be determined; the operating parameters of each of the refrigeration devices are adjusted to their corresponding target operating parameters, wherein the target operating parameters include the first target operating parameter, the second target operating parameter and the third target operating parameter.

[0083] Optionally, this step can be performed at the business layer within the energy-saving platform architecture.

[0084] Step 207: When the refrigeration equipment is operating at the target operating parameters, perform energy efficiency index analysis on the refrigeration equipment.

[0085] Specifically, the following formula can be used to analyze the energy efficiency of refrigeration equipment.

[0086]

[0087] Where δ(k) represents the energy efficiency evaluation of various cooling equipment in the computer room, X0(k) represents the energy efficiency value of various equipment in the standard computer room, X(k) represents the energy efficiency value of various equipment in the actual computer room, and ±(X(k)-X0(k)) represents the benchmark deviation factor. When the index value is larger, it is better and takes a positive sign; when the index value is smaller, it is better and takes a negative sign.

[0088] Optionally, when the refrigeration equipment is operating at the target parameters, the total energy saving and energy saving rate of the refrigeration equipment can be calculated.

[0089] Specifically, the energy-saving rate of refrigeration equipment can be calculated using the following formula.

[0090]

[0091] ΔE=E 改造前 -E 改造后 +E 调整量 ;

[0092] Where ε represents the energy saving rate of the refrigeration equipment, E 改造前 E represents the energy consumption before the renovation. 改造后 E represents the energy consumption after the modification. 调整量 This indicates the adjustment value for energy saving after changes in IT load.

[0093] Optionally, the results of energy efficiency analysis of the refrigeration equipment and the energy saving rate of the refrigeration equipment can be displayed to relevant technical personnel.

[0094] Optionally, this step can be implemented in the application layer of the energy-saving platform architecture, where a human-computer interactive visual interface is used to present the running results to the user in a clear and intuitive way.

[0095] Specifically, the BA system can output data on data center cooling equipment energy consumption, energy saving rate, and energy saving solutions based on real-time collected data center IT load and cooling equipment energy consumption data. It also sends control commands to each cooling device, ensuring that decision commands are sent to the BA system efficiently and stably, and monitors the command execution results. It continuously optimizes the energy saving model based on site conditions and sends control logic to the air conditioning system to maximize energy saving without affecting the healthy operation of the data center.

[0096] It should be noted that the energy consumption adjustment method for a device provided in this application embodiment can be executed by an energy consumption adjustment device for a device, or a control module in the energy consumption adjustment device for executing the energy consumption adjustment method for a device. This application embodiment uses an energy consumption adjustment device executing a device energy consumption adjustment method as an example to illustrate the energy consumption adjustment device provided in this application embodiment.

[0097] Figure 6 This is a schematic diagram of the structure of an energy consumption adjustment device according to an embodiment of the present invention. Figure 6 As shown, an energy consumption adjustment device 600 for a device includes: a data acquisition module 610, an input module 620, a determination module 630, and an adjustment module 640.

[0098] The data acquisition module 610 is used to acquire the operating parameters, indoor and outdoor wet-bulb temperatures, and internet IT load of at least one refrigeration device within a preset time period; the input module 620 is used to input the changes in the indoor and outdoor wet-bulb temperatures, the IT load, and the operating parameters of the refrigeration device into a pre-trained energy consumption model of the refrigeration device to obtain the power change curve of the refrigeration device; the determination module 630 is used to determine the minimum power of each of the refrigeration devices based on the change curve; and the adjustment module 640 is used to adjust the operating parameters of the refrigeration device to the target operating parameters corresponding to the minimum value, wherein the target operating parameters are used to adjust the energy consumption of the refrigeration device.

[0099] In one implementation, the acquisition module 610 is further configured to: acquire a first training sample set, train an artificial neural network using the first training sample set, and obtain an energy consumption model of the cooling water pump, wherein the samples in the first training sample set include first sample data and first sample labels, the first sample data includes the changes in the operating parameters of the cooling water pump and the IT load during a historical time period, and the first sample labels include the power changes of the cooling water pump during a historical time period.

[0100] A second training sample set is obtained, and the artificial neural network is trained using the second training sample set to obtain the energy consumption model of the chilled water pump. The samples in the second training sample set include second sample data and second sample labels. The second sample data includes the changes in the operating parameters of the chilled water pump and the IT load during the historical time period. The second sample label includes the power changes of the chilled water pump during the historical time period.

[0101] A third training sample set is obtained, and the artificial neural network is trained using the third training sample set to obtain the energy consumption model of the cooling tower fan. The samples in the third training sample set include third sample data and third sample labels. The third sample data includes the changes in the operating parameters of the cooling tower fan and the IT load during the historical time period. The third sample labels include the changes in the power of the cooling tower fan.

[0102] A fourth training sample set is obtained, and the artificial neural network is trained using the fourth training sample set to obtain a cooling tower model. The samples in the fourth training sample set include fourth sample data and fourth sample labels. The fourth sample data includes the changes in the inlet temperature of the cooling water, the changes in the operating parameters of the cooling tower fan, the changes in the operating parameters of the cooling water pump, the indoor and outdoor wet-bulb temperatures, and the IT load during the historical time period. The fourth sample labels include the changes in the outlet temperature of the cooling water during the historical time period.

[0103] A fifth training sample set is obtained, and the artificial neural network is trained using the fifth training sample set to obtain the energy consumption model of the chiller unit. The samples in the fifth training sample set include fifth sample data and fifth sample labels. The fifth sample data includes the changes in the operating parameters of the chilled water pump, the changes in the operating parameters of the cooling water pump, the changes in the outlet temperature of the cooling water, and the IT load during the historical time period. The fifth sample label includes the changes in the power of the chiller unit during the historical time period.

[0104] In one implementation, the determining module 630 is specifically used to: determine the minimum power of the chilled water pump based on the power variation curve of the chilled water pump; determine the first target operating parameter of the chilled water pump corresponding to the minimum power; and, with the first target operating parameter fixed, determine the minimum power of the cooling water pump, the cooling tower fan, and the chiller unit based on the power variation curves of the cooling water pump, the cooling tower fan, and the chiller unit.

[0105] In one implementation, the adjustment module 640 is specifically used to: determine the second target operating parameter of the cooling water pump corresponding to the minimum power and the third target operating parameter corresponding to the cooling tower fan; adjust the operating parameters of each of the refrigeration devices to their corresponding target operating parameters, wherein the target operating parameters include the first target operating parameter, the second target operating parameter and the third target operating parameter.

[0106] In one implementation, the acquisition module 610 is specifically used to: acquire the operating parameters of the refrigeration equipment within a preset time period based on a pre-established data reading priority of the refrigeration equipment.

[0107] In one implementation, the acquisition module 610 is further configured to: generate a serial number for the working parameter, wherein the serial number includes the priority of the working parameter.

[0108] The energy consumption adjustment device of a device in this application embodiment can be a device, or it can be a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc., and this application embodiment does not specifically limit the scope.

[0109] The energy consumption adjustment device of one embodiment of this application can be a device with an operating system. The operating system can be Android, iOS, or other possible operating systems, and this embodiment of the application does not specifically limit it.

[0110] The energy consumption adjustment device for a device provided in this application embodiment can achieve... Figure 1 and Figure 2 The various processes implemented in the method embodiments are not described in detail here to avoid repetition.

[0111] Optional, such as Figure 7 As shown, this application embodiment also provides an electronic device 700, including a processor 701, a memory 702, and a program or instructions stored in the memory 702 and executable on the processor 701. When the program or instructions are executed by the processor 701, they implement the various processes of the above method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0112] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0113] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described embodiment of the energy consumption adjustment method for a device and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0114] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0115] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiment of the energy consumption adjustment method for a device, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0116] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0117] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0118] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0119] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method of adjusting power consumption of a device, the method comprising: include: Collect operating parameters, indoor and outdoor wet-bulb temperatures, and internet IT load of at least one refrigeration device within a preset time period; The changes in the indoor and outdoor wet-bulb temperatures, the IT load, and the operating parameters of the refrigeration equipment are input into the energy consumption model of the refrigeration equipment obtained through pre-training to obtain the power change curve of the refrigeration equipment. Based on the aforementioned variation curves, the minimum power of each of the refrigeration devices is determined; The operating parameters of the refrigeration equipment are adjusted to the target operating parameters corresponding to the minimum value, and the target operating parameters are used to adjust the energy consumption of the refrigeration equipment; The indoor and outdoor wet-bulb temperatures, the IT load, and the refrigeration equipment are mentioned. Before inputting the changes in operating parameters into the pre-trained energy consumption model of the refrigeration equipment to obtain the power change curve of the refrigeration equipment, the process also includes: A first training sample set is obtained, and an artificial neural network is trained using the first training sample set to obtain an energy consumption model of the cooling water pump. The samples in the first training sample set include first sample data and first sample labels. The first sample data includes the changes in the operating parameters of the cooling water pump and the IT load during a historical time period. The first sample label includes the changes in the power of the cooling water pump during a historical time period. A second training sample set is obtained, and the artificial neural network is trained using the second training sample set to obtain the energy consumption model of the chilled water pump. The samples in the second training sample set include second sample data and second sample labels. The second sample data includes the changes in the operating parameters of the chilled water pump and the IT load during the historical time period. The second sample label includes the power changes of the chilled water pump during the historical time period. A third training sample set is obtained, and the artificial neural network is trained using the third training sample set to obtain the energy consumption model of the cooling tower fan. The samples in the third training sample set include third sample data and third sample labels. The third sample data includes the changes in the operating parameters of the cooling tower fan and the IT load during the historical time period. The third sample labels include the changes in the power of the cooling tower fan. A fourth training sample set is obtained, and the artificial neural network is trained using the fourth training sample set to obtain a cooling tower model. The samples in the fourth training sample set include fourth sample data and fourth sample labels. The fourth sample data includes the changes in the inlet temperature of the cooling water, the changes in the operating parameters of the cooling tower fan, the changes in the operating parameters of the cooling water pump, the indoor and outdoor wet-bulb temperatures, and the IT load during the historical time period. The fourth sample labels include the changes in the outlet temperature of the cooling water during the historical time period. A fifth training sample set is obtained, and the artificial neural network is trained using the fifth training sample set to obtain the energy consumption model of the chiller unit. The samples in the fifth training sample set include fifth sample data and fifth sample labels. The fifth sample data includes the changes in the operating parameters of the chilled water pump, the changes in the operating parameters of the cooling water pump, the changes in the outlet temperature of the cooling water, and the IT load during the historical time period. The fifth sample label includes the changes in the power of the chiller unit during the historical time period.

2. The method of claim 1, wherein, Determining the minimum power of each of the refrigeration devices based on the change curve includes: Based on the power variation curve of the chilled water pump, the minimum power of the chilled water pump is determined; Determine the first target operating parameters of the chilled water pump corresponding to the minimum power value; With the first target operating parameters fixed, the minimum power of the cooling water pump, cooling tower fan, and chiller unit is determined based on the power variation curves of the cooling water pump, the cooling tower fan, and the chiller unit.

3. The method of claim 2, wherein, The step of adjusting the operating parameters of the refrigeration equipment to the target operating parameters corresponding to the minimum value includes: Determine the second target operating parameters of the cooling water pump corresponding to the minimum power and the third target operating parameters corresponding to the cooling tower fan; The operating parameters of each of the refrigeration devices are adjusted to their corresponding target operating parameters, wherein the target operating parameters include a first target operating parameter, a second target operating parameter, and a third target operating parameter.

4. The method of claim 1, wherein, The collection of operating parameters of at least one refrigeration device within a preset time period includes: Based on the pre-established data reading priority of the refrigeration equipment, the operating parameters of the refrigeration equipment are collected within a preset time period.

5. The method of claim 1, wherein, After collecting the operating parameters of at least one refrigeration device, indoor and outdoor wet-bulb temperatures, and internet IT load within the preset time period, the method further includes: Generate a serial number for the working parameter, wherein the serial number includes the priority of the working parameter.

6. An energy consumption adjustment device for an equipment, characterized in that, include: The data acquisition module is used to collect the operating parameters, indoor and outdoor wet-bulb temperatures, and Internet IT load of at least one refrigeration device within a preset time period. The input module is used to input the changes in the indoor and outdoor wet-bulb temperatures, the IT load, and the operating parameters of the refrigeration equipment into the energy consumption model of the refrigeration equipment that has been pre-trained, so as to obtain the power change curve of the refrigeration equipment. A determining module is used to determine the minimum power of each of the refrigeration devices based on the change curve; An adjustment module is used to adjust the operating parameters of the refrigeration equipment to the target operating parameters corresponding to the minimum value, wherein the target operating parameters are used to adjust the energy consumption of the refrigeration equipment; The acquisition module is also used for: A first training sample set is obtained, and an artificial neural network is trained using the first training sample set to obtain an energy consumption model of the cooling water pump. The samples in the first training sample set include first sample data and first sample labels. The first sample data includes the changes in the operating parameters of the cooling water pump and the IT load during a historical time period. The first sample label includes the changes in the power of the cooling water pump during a historical time period. A second training sample set is obtained, and the artificial neural network is trained using the second training sample set to obtain the energy consumption model of the chilled water pump. The samples in the second training sample set include second sample data and second sample labels. The second sample data includes the changes in the operating parameters of the chilled water pump and the IT load during the historical time period. The second sample label includes the power changes of the chilled water pump during the historical time period. A third training sample set is obtained, and the artificial neural network is trained using the third training sample set to obtain the energy consumption model of the cooling tower fan. The samples in the third training sample set include third sample data and third sample labels. The third sample data includes the changes in the operating parameters of the cooling tower fan and the IT load during the historical time period. The third sample labels include the changes in the power of the cooling tower fan. A fourth training sample set is obtained, and the artificial neural network is trained using the fourth training sample set to obtain a cooling tower model. The samples in the fourth training sample set include fourth sample data and fourth sample labels. The fourth sample data includes the changes in the inlet temperature of the cooling water, the changes in the operating parameters of the cooling tower fan, the changes in the operating parameters of the cooling water pump, the indoor and outdoor wet-bulb temperatures, and the IT load during the historical time period. The fourth sample labels include the changes in the outlet temperature of the cooling water during the historical time period. A fifth training sample set is obtained, and the artificial neural network is trained using the fifth training sample set to obtain the energy consumption model of the chiller unit. The samples in the fifth training sample set include fifth sample data and fifth sample labels. The fifth sample data includes the changes in the operating parameters of the chilled water pump, the changes in the operating parameters of the cooling water pump, the changes in the outlet temperature of the cooling water, and the IT load during the historical time period. The fifth sample label includes the changes in the power of the chiller unit during the historical time period.

7. An electronic device, comprising: It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein when the program or instructions are executed by the processor, they implement the steps of the energy consumption adjustment method of the device as described in any one of claims 1-5.

8. A readable storage medium, characterized by, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the power consumption adjustment method of the device as described in any one of claims 1-5.

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