Optimization method and device for control parameters of cooling side of water chilling unit and storage medium
By obtaining the power model of the chiller unit and determining the optimization target, and using the optimization seeker to optimize the cooling side control parameters, the problem of insufficient optimization of the cooling side control parameters of the chiller unit in the prior art is solved, and more effective energy-saving control is achieved.
Patent Information
- Application Number
- CN202311438133.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-31
- Publication Date
- 2025-05-02
AI Technical Summary
The prior art is difficult to effectively optimize the control parameters on the cooling side of the chiller unit, making it difficult to tap energy saving potential, and only stays in single-factor feedback and the experience of property personnel.
By obtaining the host power model, cooling tower power model and cooling pump power model, the optimization target of the cooling side control parameters of the chiller unit is determined and inputted to the optimization seeker to obtain the optimization results and optimize the control parameters.
It has achieved a more reasonable optimization of the cooling side control parameters of the chiller unit in combination with the historical operating conditions and the physical mechanism of the cooling side of the chiller unit, effectively meeting the energy-saving control needs of the chiller unit.
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Figure CN119914974A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of control parameter optimization, for example, to a method, device and storage medium for optimizing control parameters of a cooling side of a chiller. Background Art
[0002] At present, chillers are the largest energy consumers in large buildings such as buildings, parks, and data centers, and their comprehensive energy consumption accounts for more than 50% of the total energy consumption of large buildings. Therefore, how to more effectively control energy conservation in large buildings has become the focus of the industry.
[0003] At present, group control is usually used for energy-saving control of chillers. Specifically, the start and stop of the chiller, the outlet water temperature, the pressure difference on the freezing side, the proximity and temperature difference on the cooling side are controlled. Although this control method has liberated the property staff to a certain extent, it is difficult to tap the energy-saving potential, and it only stays on single-factor feedback and the experience of the property staff. Therefore, how to optimize the control parameters of the cooling side of the chiller to meet the energy-saving control needs of the chiller has become a technical problem that needs to be solved urgently.
[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention
[0005] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.
[0006] The embodiments of the present disclosure provide a method, device and storage medium for optimizing the cooling side control parameters of a chiller, which can effectively optimize the cooling side control parameters of the chiller to meet the energy-saving control requirements of the chiller.
[0007] In some embodiments, the optimization method for the cooling side control parameters of the chiller includes: obtaining a main engine power model, a cooling tower power model, and a cooling pump power model; determining the optimization target of the cooling side control parameters of the chiller according to the main engine power model, the cooling tower power model, and the cooling pump power model; inputting the optimization target of the cooling side control parameters of the chiller and the optimization range of the cooling side control parameters of the chiller into the optimizer to obtain the optimization results of the cooling side control parameters of the chiller output by the optimizer; and controlling the chiller to optimize the control parameters according to the optimization results.
[0008] In some embodiments, the optimization method for the cooling side control parameters of the chiller includes: obtaining the output value of the host power model, the output value of the cooling tower power model, and the output value of the cooling pump power model; adding the output value of the host power model, the output value of the cooling tower power model, and the output value of the cooling pump power model to obtain the target total power; and minimizing the target total power as the optimization target of the cooling side control parameters of the chiller.
[0009] In some embodiments, the optimization method for the control parameters of the cooling side of the chiller includes: obtaining the cooling water outlet temperature of the host, the set chilled water outlet temperature of the host and the current instantaneous cooling capacity; inputting the cooling water outlet temperature of the host, the set chilled water outlet temperature of the host and the current instantaneous cooling capacity into the host power model to obtain the output value of the host power model.
[0010] In some embodiments, the optimization method for the cooling side control parameters of the chiller includes: obtaining the heat dissipation efficiency of the cooling tower, the frequency of the cooling tower, the cooling water flow rate, the cooling water supply pressure difference and the instantaneous cooling capacity; inputting the heat dissipation efficiency of the cooling tower, the frequency of the cooling tower, the cooling water flow rate, the cooling water supply pressure difference and the instantaneous cooling capacity into the cooling tower power model to obtain the output value of the cooling tower power model.
[0011] In some embodiments, the optimization method for the cooling side control parameters of the chiller includes: obtaining the startup data of the cooling pump and the operating power of the cooling pump; inputting the startup data of the cooling pump and the operating power of the cooling pump into the cooling pump power model to obtain the output value of the cooling pump power model.
[0012] In some embodiments, the optimization method for the cooling side control parameters of the chiller includes: determining the target constraint conditions; inputting the optimization target of the cooling side control parameters of the chiller, the optimization range of the cooling side control parameters of the chiller and the target constraint conditions into the optimizer to obtain the optimization result of the cooling side control parameters of the chiller output by the optimizer.
[0013] In some embodiments, the method for optimizing the control parameters of the cooling side of the chiller includes: establishing a cooling side limit heat dissipation capacity model of the chiller; and using the cooling side limit heat dissipation capacity output by the cooling side limit heat dissipation capacity model as a target constraint condition.
[0014] In some embodiments, the device for optimizing the cooling side control parameters of the chiller includes: an acquisition module, configured to obtain a host power model, a cooling tower power model and a cooling pump power model; a determination module, configured to determine the optimization target of the cooling side control parameters of the chiller according to the host power model, the cooling tower power model and the cooling pump power model; an input module, configured to input the optimization target of the cooling side control parameters of the chiller and the optimization range of the cooling side control parameters of the chiller into the optimizer to obtain the optimization result of the cooling side control parameters of the chiller output by the optimizer; and a control module, configured to control the chiller to optimize the control parameters according to the optimization result.
[0015] In some embodiments, the device for optimizing the control parameters of the cooling side of the chiller includes: a processor and a memory storing program instructions, and the processor is configured to execute the aforementioned method for optimizing the control parameters of the cooling side of the chiller when running the program instructions.
[0016] In some embodiments, the storage medium stores program instructions, and when the program instructions are run, the aforementioned method for optimizing the control parameters of the cooling side of the chiller is executed.
[0017] The optimization method, device and storage medium for the cooling side control parameters of the chiller provided by the embodiments of the present disclosure can achieve the following technical effects: by obtaining the host power model, cooling tower power model and cooling pump power model; and according to the host power model, cooling tower power model and cooling pump power model, determining the optimization target of the cooling side control parameters of the chiller; thereby inputting the optimization target of the cooling side control parameters of the chiller and the optimization range of the cooling side control parameters of the chiller into the optimizer to obtain the optimization result of the cooling side control parameters of the chiller output by the optimizer; and then controlling the chiller to optimize the control parameters according to the optimization result. In this way, the control parameters of the cooling side of the chiller can be optimized more reasonably in combination with the historical operating conditions of the chiller and the physical mechanism of the cooling side, effectively meeting the energy-saving control requirements of the chiller.
[0018] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:
[0020] Figure 1 is a schematic diagram of a method for optimizing control parameters of a cooling side of a chiller provided by an embodiment of the present disclosure;
[0021] Figure 2 is a schematic diagram of a method for determining an optimization target provided by an embodiment of the present disclosure;
[0022] Figure 3 is a schematic diagram of a method for obtaining a host power model output value provided by an embodiment of the present disclosure;
[0023] Figure 4 is a schematic diagram of a method for obtaining an output value of a cooling tower power model provided by an embodiment of the present disclosure;
[0024] Figure 5 is a schematic diagram of a method for obtaining a cooling pump power model output value provided by an embodiment of the present disclosure;
[0025] Figure 6 is a schematic diagram of a device for optimizing control parameters of a cooling side of a chiller provided by an embodiment of the present disclosure;
[0026] Figure 7 It is a schematic diagram of another device for optimizing control parameters of the cooling side of a chiller provided in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0027] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.
[0028] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.
[0029] Unless otherwise stated, the term "plurality" means two or more.
[0030] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.
[0031] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.
[0032] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.
[0033] In the embodiments of the present disclosure, smart home appliances refer to home appliances that are formed by introducing microprocessors, sensor technology, and network communication technology into home appliances. They have the characteristics of intelligent control, intelligent perception, and intelligent application. The operation process of smart home appliances often relies on the application and processing of modern technologies such as the Internet of Things, the Internet, and electronic chips. For example, smart home appliances can be connected to electronic devices to enable users to remotely control and manage the smart home appliances.
[0034] In the embodiments of the present disclosure, the terminal device refers to an electronic device with a wireless connection function. The terminal device can communicate with the above-mentioned smart home appliances by connecting to the Internet, or can directly communicate with the above-mentioned smart home appliances through Bluetooth, Wi-Fi, etc. In some embodiments, the terminal device is, for example, a mobile device, a computer, or a vehicle-mounted device built into a hover car, or any combination thereof. Mobile devices may include, for example, mobile phones, smart home devices, wearable devices, smart mobile devices, virtual reality devices, etc., or any combination thereof, wherein wearable devices include, for example: smart watches, smart bracelets, pedometers, etc.
[0035] Figure 1 is a schematic diagram of a method for optimizing control parameters of the cooling side of a chiller provided by an embodiment of the present disclosure; Figure 1 As shown, the embodiment of the present disclosure provides a method for optimizing control parameters of the cooling side of a chiller, comprising:
[0036] S11, the chiller obtains a host power model, a cooling tower power model and a cooling pump power model.
[0037] S12, the chiller determines the optimization target of the control parameters of the cooling side of the chiller according to the host power model, the cooling tower power model and the cooling pump power model.
[0038] S13, the chiller inputs the optimization target of the chiller cooling side control parameters and the optimization range of the chiller cooling side control parameters into the optimizer to obtain the optimization result of the chiller cooling side control parameters output by the optimizer.
[0039] S14, the chiller controls the chiller to optimize control parameters according to the optimization result.
[0040] In this solution, the chiller can obtain a host power model. Specifically, obtaining the host power model includes: establishing an initial host power model; iteratively training the initial host power model to obtain the host power model. As an example, the establishment of the initial host power model can refer to the industry standard formula. That is, P = a0 + a1 (T cws / out -T chsup )+a2(T cws / out -T chsup ) 2 +a3Q+a4Q 2 +a5Q(T cws / out -T chsup ), where P is the power of the host, a0 to a5 are the coefficients of the regression term, T cws / out is the cooling water outlet temperature of the host, T chsup The outlet water temperature is set for the host's chilled water, and Q is the current instantaneous cooling capacity. In this way, the initial host power model can be accurately established. Furthermore, the initial host power model can be iteratively trained to obtain the host power model. Specifically, the chiller iteratively trains the initial host power model, including: selecting recent data of a user-set duration as the data source; processing the data source, including but not limited to processing duplicate values, missing values, abnormal fluctuation values, and aligning the time dimension of input and output; selecting an error criterion formula, and rolling training with a new data source to achieve iterative update of the initial host power model; using the iteratively updated initial host power model as the host power model.
[0041] In this embodiment, the chiller can obtain a cooling tower power model. Specifically, obtaining the cooling tower power model includes: establishing an initial cooling tower power model; iteratively training the initial cooling tower power model to obtain the cooling tower power model. As an example, a machine learning model can be used for modeling, and appropriate parameters can be selected for model establishment. As a preferred solution, the parameters used for initial cooling tower power modeling include but are not limited to the total power of the host, the heat dissipation efficiency of the cooling tower, the frequency of the cooling tower, the cooling water flow, the cooling water supply pressure difference, and the instantaneous cooling capacity, and the output parameter is the power of the cooling tower. In this way, the initial cooling tower power model can be accurately established. Further, the initial cooling tower power model can be iteratively trained to obtain the cooling tower power model. Specifically, the chiller iteratively trains the initial cooling tower power model, including: selecting recent data of a user-set duration as a data source; processing the data source, including but not limited to processing repeated values, missing values, abnormal fluctuation values, and aligning the time dimension of input and output; selecting an error criterion formula, and rolling training through a new data source to achieve iterative update of the initial cooling tower power model; using the iteratively updated initial cooling tower power model as the cooling tower power model.
[0042] In this embodiment, the chiller can obtain a cooling pump power model. Specifically, obtaining the cooling pump power model includes: establishing an initial cooling pump power model; iteratively training the initial cooling pump power model to obtain the cooling pump power model. As an example, a tree model can be used for modeling, and appropriate parameters can be selected for modeling. It can be understood that the parameters used for initial cooling pump power modeling include the operating frequency of the cooling pump and the startup data of the cooling pump, and the output parameter is the total power of the cooling pump. Here, the startup data of the cooling pump refers to the coding combination of the startup states of different cooling pumps. For example, there are three cooling pumps, two of which are in the on state, and 1 in the coding represents the on state, and 0 represents the off state, then the startup data of the cooling pump can be 110, 101 or 011. In another example, modeling can also be performed in a similar manner to the host power model, that is, a polynomial formula is used to establish the initial cooling pump power model. In this way, the initial cooling pump power model can be accurately established. Further, the initial cooling pump power model can be iteratively trained to obtain the cooling pump power model. Specifically, the chiller iteratively trains the initial cooling pump power model, including: selecting recent data of a user-set duration as a data source; processing the data source, including but not limited to processing duplicate values, missing values, abnormal fluctuation values, and aligning the time dimension of input and output; selecting an error criterion formula, and performing rolling training with a new data source to achieve iterative update of the initial cooling pump power model; and using the iteratively updated initial cooling pump power model as the cooling pump power model.
[0043] Furthermore, after the chiller obtains the host power model, cooling tower power model and cooling pump power model, the optimization target of the chiller cooling side control parameters is determined by combining the host power model, cooling tower power model and cooling pump power model. Specifically, the chiller determines the optimization target of the chiller cooling side control parameters according to the host power model, cooling tower power model and cooling pump power model, including: the chiller obtains the output value of the host power model, the output value of the cooling tower power model and the output value of the cooling pump power model; the chiller adds the output value of the host power model, the output value of the cooling tower power model and the output value of the cooling pump power model to obtain the target total power; the chiller takes the minimum target total power as the optimization target of the chiller cooling side control parameters. With this solution, the accurate determination of the optimization target of the chiller cooling side control parameters can be achieved.
[0044] Furthermore, the chiller inputs the optimization target of the chiller cooling side control parameters and the optimization range of the chiller cooling side control parameters into the optimizer. Among them, the optimization target of the chiller cooling side control parameters is to minimize the target total power, and the optimization range of the chiller cooling side control parameters can be input in advance by the business personnel; specifically, the optimization range of the chiller cooling side control parameters includes the chilled water outlet temperature range, the cooling pump number range, the cooling pump frequency range, the cooling tower number range and the cooling tower frequency range.
[0045] In this embodiment, if the optimizer algorithm adopts the particle swarm optimization algorithm, the optimization process of the optimizer includes:
[0046] The chiller obtains the cooling tower outlet temperature prediction model.
[0047] The chiller extracts the recent value of the outlet tower temperature, the characteristic matrix, the instantaneous cooling capacity and the current target total power from the real-time database.
[0048] The chiller inputs the optimization parameters, recent values of the outlet temperature, characteristic matrix, instantaneous cooling capacity and current target total power into the cooling tower outlet temperature prediction model to obtain the outlet temperature prediction value output by the cooling tower outlet temperature prediction model.
[0049] The chiller obtains the total cooling water flow determined by the cooling pump flow model.
[0050] The chiller calculates the main unit cooling water outlet temperature based on the predicted value of the outlet tower temperature and the total cooling water flow rate.
[0051] The chiller inputs the main engine cooling water outlet temperature into the cooling side limit heat dissipation capacity model to determine whether the cooling side limit heat dissipation capacity is sufficient;
[0052] The chiller determines the data with sufficient heat dissipation capacity judged by the cooling side limit heat dissipation capacity model as valid optimization data, and substitutes it into the group to continue to search for the best result; otherwise, it is determined as invalid optimization data and refused to enter the group.
[0053] Optionally, the host cooling water outlet temperature can be calculated based on the predicted value of the outlet tower temperature and the total cooling water flow rate. Specifically, the host cooling water outlet temperature is calculated in the following manner: host cooling water outlet temperature = cooling tower outlet temperature predicted value + (cooling capacity + host total power) / cooling water total flow rate. In this way, the host cooling water outlet temperature can be accurately calculated.
[0054] In this embodiment, it can be understood that, considering that there are non-steady-state data in the actual data, which is greatly affected by environmental changes, and it cannot be guaranteed that the heat generated by the host at the current moment can be dissipated during actual operation, the present embodiment adopts the generative cooling tower outlet temperature prediction to take the sequence value of the features arranged in time sequence as the feature input. Specifically, the feature matrix may include a feature sequence of t time steps, and the feature sequence includes a wet-bulb temperature sequence, an outdoor temperature sequence, an instantaneous cooling capacity sequence, a cooling pump frequency sequence, a cooling tower frequency sequence, a target total power sequence, and a start-stop combination dummy feature sequence, etc., so as to form a feature matrix. Further, the acquired feature matrix can be input into a simple generative network through an encoder, and high-dimensional features can be obtained by using the multi-layer encoder and attention mechanism in the simple generative network; and under the premise that the cooling tower outlet temperature at the first n moments is input into the multi-layer decoder of the simple generative network through a decoder, the generated predicted value of the cooling tower outlet temperature at the next n moments is obtained. Here, the generated predicted values of the cooling tower outlet temperature at the next n moments can be the same or different. In this way, a generative forecasting method is adopted to comprehensively consider environmental factors, control parameters, and their temporal variation trends, making the forecast results more accurate.
[0055] In one example, the training and iteration process of the cooling tower outlet temperature prediction model includes: the user defines the length of historical data, the feature matrix time step t and the number of prediction steps n; the scrolling data frame is used to obtain the historical data of the time series length according to the user definition; the data is subjected to missing values, abnormal values, time series interpolation, smoothing and steady-state segmentation; it is determined whether the processed data is sufficient; when the processed data is sufficient, the model training preparation is carried out, and the preparation process includes but is not limited to model building, data standardization, training set test set validation set batching and error function determination; the algorithm business personnel autonomously control the model iteration training, and set the iteration end standard under the premise of ensuring accuracy; it is determined whether the model verification error has reached the output standard; when the output standard is reached, the training iteration of the cooling tower outlet temperature prediction model is output. In this way, the training iteration of the cooling tower outlet temperature prediction model can be accurately obtained. It can be understood that in the actual model training iteration process, it is necessary to first determine the length of the historical data used for training, such as using the data of the previous month or the data of the previous year for training. Secondly, it is necessary to determine the time series length of the feature matrix. This dimension will be shorter than the length of the previous training time, generally tens of minutes or hours. The specific number of groups of data depends on the frequency of the data source. Finally, it is necessary to determine the step size to be predicted, such as predicting the next ten minutes or half an hour.
[0056] Further, after obtaining the optimization result of the control parameter of the cooling side of the chiller output by the optimizer, the chiller is controlled to optimize the control parameters according to the optimization result. Here, controlling the chiller to optimize the control parameters according to the optimization result includes: determining the parameter optimization time; controlling the chiller to optimize the control parameters according to the optimization result and the parameter optimization time. Optionally, determining the parameter optimization time includes: determining the parameter trigger interval; taking the sum of the current moment and the parameter trigger interval as the parameter optimization time. It can be understood that the parameter trigger interval cannot exceed the maximum prediction time step of the cooling tower outlet temperature prediction model. For example, if the cooling tower outlet temperature prediction model predicts the cooling tower outlet temperature prediction value every 5 minutes in the next 30 minutes, then the trigger interval of the predictive control can only be selected in 5 to 30 minutes, with a step size of 5 minutes. In this way, the parameter trigger interval can be accurately determined to achieve accurate acquisition of the parameter optimization time.
[0057] The optimization method for the cooling side control parameters of the chiller provided by the embodiment of the present disclosure is adopted, by obtaining the host power model, cooling tower power model and cooling pump power model; and according to the host power model, cooling tower power model and cooling pump power model, the optimization target of the cooling side control parameters of the chiller is determined; thereby the optimization target of the cooling side control parameters of the chiller and the optimization range of the cooling side control parameters of the chiller are input into the optimizer to obtain the optimization result of the cooling side control parameters of the chiller output by the optimizer; and then the chiller is controlled to optimize the control parameters according to the optimization result. In this way, the control parameters of the cooling side of the chiller can be optimized more reasonably in combination with the historical operating conditions of the chiller and the physical mechanism of the cooling side, effectively meeting the energy-saving control requirements of the chiller.
[0058] Figure 2 is a schematic diagram of a method for determining an optimization target provided by an embodiment of the present disclosure; Figure 2 As shown, optionally, in S12, the chiller determines the optimization target of the cooling side control parameters of the chiller according to the host power model, the cooling tower power model and the cooling pump power model, including:
[0059] S21, the chiller obtains the output value of the host power model, the output value of the cooling tower power model and the output value of the cooling pump power model.
[0060] S22, the chiller adds the output value of the host power model, the output value of the cooling tower power model and the output value of the cooling pump power model to obtain the target total power.
[0061] S23, the chiller takes the minimum target total power as the optimization target of the control parameters of the cooling side of the chiller.
[0062] In this solution, after obtaining the host power model, cooling tower power model and cooling pump power model, the chiller can obtain the output value of the host power model, the output value of the cooling tower power model and the output value of the cooling pump power model respectively; thus, the output value of the host power model, the output value of the cooling tower power model and the output value of the cooling pump power model are added together to calculate the target total power, so as to minimize the target total power as the optimization target of the control parameters on the cooling side of the chiller. With this solution, the optimal total energy consumption of the cooling side and the host can be taken as the goal, providing an accurate data basis for the optimization process of the control parameters, and effectively meeting the energy-saving control needs of users.
[0063] Figure 3 is a schematic diagram of a method for obtaining a host power model output value provided by an embodiment of the present disclosure; Figure 3 As shown, optionally, in S21, the chiller obtains an output value of the host power model, including:
[0064] S31, the chiller obtains the cooling water outlet temperature of the host, the set chilled water outlet temperature of the host and the current instantaneous cooling capacity.
[0065] S32, the chiller inputs the cooling water outlet temperature of the host, the set chilled water outlet temperature of the host and the current instantaneous cooling capacity into the host power model to obtain the output value of the host power model.
[0066] In this embodiment, the chiller can obtain the cooling water outlet temperature of the host, the chilled water set outlet temperature of the host, and the current instantaneous cooling capacity; the chiller can input the cooling water outlet temperature of the host, the chilled water set outlet temperature of the host, and the current instantaneous cooling capacity into the host power model to obtain the output value of the host power model. As an example, in this way, the output value of the host power model can be accurately obtained, providing an accurate data basis for the calculation of the target total power.
[0067] Figure 4 is a schematic diagram of a method for obtaining a cooling tower power model output value provided by an embodiment of the present disclosure; Figure 4 As shown, optionally, in S21, the chiller obtains an output value of a cooling tower power model, including:
[0068] S41, the chiller obtains the heat dissipation efficiency of the cooling tower, the frequency of the cooling tower, the cooling water flow rate, the cooling water supply pressure difference and the instantaneous cooling capacity.
[0069] S42, the chiller inputs the heat dissipation efficiency of the cooling tower, the frequency of the cooling tower, the cooling water flow rate, the cooling water supply pressure difference and the instantaneous cooling capacity into the cooling tower power model to obtain the output value of the cooling tower power model.
[0070] In this solution, the chiller can obtain the heat dissipation efficiency of the cooling tower, the frequency of the cooling tower, the cooling water flow rate, the cooling water supply pressure difference and the instantaneous cooling capacity; the chiller can input the heat dissipation efficiency of the cooling tower, the frequency of the cooling tower, the cooling water flow rate, the cooling water supply pressure difference and the instantaneous cooling capacity into the cooling tower power model to obtain the output value of the cooling tower power model. Here, the cooling tower power model can be modeled using a machine learning model, and appropriate parameters are selected for model establishment. As a preferred solution, the parameters used for cooling tower power modeling include but are not limited to the total power of the host, the heat dissipation efficiency of the cooling tower, the frequency of the cooling tower, the cooling water flow rate, the cooling water supply pressure difference and the instantaneous cooling capacity. With this solution, after obtaining the cooling tower power model, the output value of the cooling tower power model can be accurately obtained, providing an accurate data basis for the calculation of the target total power.
[0071] Figure 5 is a schematic diagram of a method for obtaining a cooling pump power model output value provided by an embodiment of the present disclosure; Figure 5 As shown, optionally, in S21, the chiller obtains a cooling pump power model, including:
[0072] S51, the chiller obtains the startup data of the cooling pump and the operating power of the cooling pump.
[0073] S52, the chiller inputs the startup data of the cooling pump and the operating power of the cooling pump into the cooling pump power model to obtain an output value of the cooling pump power model.
[0074] In this solution, the chiller can obtain the startup data of the cooling pump and the operating power of the cooling pump. Here, the startup data of the cooling pump refers to the coding combination of the startup status of different cooling pumps. For example, there are three cooling pumps, two of which are in the on state, 1 in the code represents the on state, and 0 represents the off state, then the startup data of the cooling pump can be 110, 101 or 011. Furthermore, the chiller can input the startup data of the cooling pump and the operating power of the cooling pump into the cooling pump power model to obtain the output value of the cooling pump power model. Here, the cooling pump power model can be modeled in a similar way to the host power model, that is, a polynomial formula is used to establish the cooling pump power model. With this solution, after obtaining the cooling pump power model, the output value of the cooling pump power model can be accurately obtained, providing an accurate data basis for the calculation of the target total power.
[0075] Optionally, in S13, the chiller inputs the optimization target of the chiller cooling side control parameter and the optimization range of the chiller cooling side control parameter into the optimizer to obtain the optimization result of the chiller cooling side control parameter output by the optimizer, including:
[0076] Chiller determines the target constraints.
[0077] The chiller inputs the optimization target of the chiller cooling side control parameters, the optimization range of the chiller cooling side control parameters and the target constraints into the optimizer to obtain the optimization results of the chiller cooling side control parameters output by the optimizer.
[0078] In this solution, the chiller can determine the target constraint conditions. Specifically, the chiller determines the target constraint conditions, including: the chiller establishes a cooling side limit heat dissipation capacity model of the chiller. The chiller uses the cooling side limit heat dissipation capacity output by the cooling side limit heat dissipation capacity model as the target constraint condition. With this solution, the target constraint conditions can be accurately determined. Furthermore, the chiller can input the optimization target of the chiller cooling side control parameters, the optimization range of the chiller cooling side control parameters and the target constraint conditions into the optimizer to obtain the optimization results of the chiller cooling side control parameters output by the optimizer. With this solution, the optimization can be limited by the cooling side limit heat dissipation capacity model to effectively avoid returning unreasonable control parameters.
[0079] Optionally, the chiller determines target constraints including:
[0080] The chiller establishes the ultimate heat dissipation capacity model of the cooling side of the chiller.
[0081] The chiller takes the cooling side maximum heat dissipation capacity output by the cooling side maximum heat dissipation capacity model as the target constraint.
[0082] Understandably, the cooling side limit heat dissipation capacity model is used to determine whether the cooling side can cool the cooling water under the current situation. The main function of this model is to limit the scope of optimization and prevent the cooling water outlet temperature recommended by the optimization result from being a temperature that cannot be reached by the system limit. In one example, the establishment of the model mainly relies on test conditions. By adjusting the cooling pump and cooling tower to full frequency, the outlet water temperature of the chiller side is adjusted under different weather conditions, and then the inlet tower temperature of the cooling tower is obtained as the data source.
[0083] Specifically, the chiller establishes a cooling side extreme heat dissipation capacity model, including: the chiller tests and obtains data sources; the chiller processes data; the chiller trains and iterates the cooling side extreme heat dissipation capacity model; and the chiller outputs the trained cooling side extreme heat dissipation capacity model. Among them, the chiller tests and obtains data sources, including: testing under different environmental parameters, making the cooling side fully loaded, and testing and obtaining the difference between the cooling tower inlet temperature and the wet bulb under different cooling capacities and host loads. The chiller processes data, including: aligning inputs and stabilized outputs, processing abnormal values, duplicate values, invalid outliers and null values, and grouping them according to load conditions. The chiller trains and iterates the cooling side extreme heat dissipation capacity model, including: training the model in groups so that there is an extreme heat dissipation capacity model for the cooling side in each load range. It should be noted that during the model iteration process, the updated data sources will be sent to their respective groups for separate training and iteration. The chiller outputs the trained cooling side extreme heat dissipation capacity model, including outputting files for each group and storing them in the cloud server. In this way, the cooling side limit heat dissipation capacity model can be accurately established. Furthermore, the chiller can use the cooling side limit heat dissipation capacity output by the cooling side limit heat dissipation capacity model as a target constraint. In this way, the optimization can be restricted by the cooling side limit heat dissipation capacity model, effectively avoiding the return of unreasonable control parameters.
[0084] Figure 6 is a schematic diagram of a device for optimizing control parameters of the cooling side of a chiller provided by an embodiment of the present disclosure; Figure 6 As shown, the embodiment of the present disclosure provides an optimization device 200 for the control parameters of the cooling side of the chiller, including an acquisition module 61, a determination module 62, an input module 63 and a control module 64. The acquisition module 61 is configured to obtain a host power model, a cooling tower power model and a cooling pump power model; the determination module 62 is configured to determine the optimization target of the control parameters of the cooling side of the chiller according to the host power model, the cooling tower power model and the cooling pump power model; the input module 63 is configured to input the optimization target of the control parameters of the cooling side of the chiller and the optimization range of the control parameters of the cooling side of the chiller into the optimizer to obtain the optimization result of the control parameters of the cooling side of the chiller output by the optimizer; the control module 64 is configured to control the chiller to optimize the control parameters according to the optimization result.
[0085] The optimization device 200 for the control parameters of the cooling side of the chiller provided by the embodiment of the present disclosure is adopted, by obtaining the host power model, the cooling tower power model and the cooling pump power model; and according to the host power model, the cooling tower power model and the cooling pump power model, the optimization target of the control parameters of the cooling side of the chiller is determined; thereby the optimization target of the control parameters of the cooling side of the chiller and the optimization range of the control parameters of the cooling side of the chiller are input into the optimizer to obtain the optimization result of the control parameters of the cooling side of the chiller output by the optimizer; and then the chiller is controlled to optimize the control parameters according to the optimization result. In this way, the control parameters of the cooling side of the chiller can be optimized more reasonably in combination with the historical operating conditions of the chiller and the physical mechanism of the cooling side, effectively meeting the energy-saving control requirements of the chiller.
[0086] Figure 7 is a schematic diagram of another device for optimizing control parameters of the cooling side of a chiller provided by an embodiment of the present disclosure; Figure 7 As shown, an embodiment of the present disclosure provides an optimization device 200 for the control parameters of the cooling side of a chiller, including a processor 201 and a memory 202. Optionally, the device may also include a communication interface 203 and a bus 204. The processor 201, the communication interface 203, and the memory 202 may communicate with each other through the bus 204. The communication interface 203 may be used for information transmission. The processor 201 may call the logic instructions in the memory 202 to execute the optimization method for the control parameters of the cooling side of the chiller of the above embodiment.
[0087] In addition, the logic instructions in the memory 202 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.
[0088] The memory 202 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 201 executes the function application and data processing by running the program instructions / modules stored in the memory 202, that is, the optimization method for the control parameters of the cooling side of the chiller in the above embodiment is implemented.
[0089] The memory 202 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 202 may include a high-speed random access memory and may also include a non-volatile memory.
[0090] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned method for optimizing control parameters of a cooling side of a chiller.
[0091] The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.
[0092] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes, or a transient storage medium.
[0093] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.
[0094] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.
[0095] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.
[0096] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.
Claims
1. A method for optimizing control parameters of the cooling side of a chiller, characterized in that: include: Obtaining the main engine power model, cooling tower power model and cooling pump power model; Determine the optimization target of the control parameters on the cooling side of the chiller according to the host power model, cooling tower power model and cooling pump power model; Inputting the optimization target of the cooling side control parameter of the chiller and the optimization range of the cooling side control parameter of the chiller into the optimizer to obtain the optimization result of the cooling side control parameter of the chiller output by the optimizer; Control the chiller to optimize control parameters according to the optimization results.
2. The method according to claim 1, characterized in that According to the host power model, cooling tower power model and cooling pump power model, the optimization target of the control parameters of the cooling side of the chiller is determined, including: Obtaining output values of the host power model, the cooling tower power model, and the cooling pump power model; Adding the output value of the main engine power model, the output value of the cooling tower power model and the output value of the cooling pump power model to obtain a target total power; The minimum target total power is used as the optimization target of the control parameters of the cooling side of the chiller.
3. The method according to claim 2, characterized in that Obtain the output values of the host power model, including: Obtain the cooling water outlet temperature of the host, the set chilled water outlet temperature of the host and the current instantaneous cooling capacity; The cooling water outlet temperature of the host, the chilled water set water outlet temperature of the host and the current instantaneous cooling capacity are input into the host power model to obtain the output value of the host power model.
4. The method according to claim 2, characterized in that: Obtain output values from the cooling tower power model, including: Obtain the cooling tower's heat dissipation efficiency, cooling tower frequency, cooling water flow, cooling water supply pressure difference and instantaneous cooling capacity; The heat dissipation efficiency of the cooling tower, the frequency of the cooling tower, the cooling water flow rate, the cooling water supply pressure difference and the instantaneous cooling capacity are input into the cooling tower power model to obtain the output value of the cooling tower power model.
5. The method according to claim 2, characterized in that: Obtain output values from the cooling pump power model, including: Obtaining the startup data of the cooling pump and the operating power of the cooling pump; The startup data of the cooling pump and the operating power of the cooling pump are input into the cooling pump power model to obtain an output value of the cooling pump power model.
6. The method according to claim 1, characterized in that The optimization target of the cooling side control parameter of the chiller and the optimization range of the cooling side control parameter of the chiller are input into the optimizer to obtain the optimization result of the cooling side control parameter of the chiller output by the optimizer, including: Determine target constraints; The optimization target of the cooling side control parameters of the chiller, the optimization range of the cooling side control parameters of the chiller and the target constraint conditions are input into the optimizer to obtain the optimization result of the cooling side control parameters of the chiller output by the optimizer.
7. The method according to claim 6, characterized in that Identify target constraints, including: Establish the cooling side ultimate heat dissipation capacity model of the chiller; The ultimate heat dissipation capacity of the cooling side output by the ultimate heat dissipation capacity model of the cooling side is used as the target constraint condition.
8. A device for optimizing control parameters of the cooling side of a chiller, characterized in that: include: An acquisition module is configured to obtain a host power model, a cooling tower power model, and a cooling pump power model; A determination module is configured to determine an optimization target of a control parameter of a cooling side of a chiller according to a host power model, a cooling tower power model, and a cooling pump power model; An input module is configured to input the optimization target of the cooling side control parameter of the chiller and the optimization range of the cooling side control parameter of the chiller into the optimizer to obtain the optimization result of the cooling side control parameter of the chiller output by the optimizer; The control module is configured to control the chiller to optimize control parameters according to the optimization result.
9. A device for optimizing control parameters of the cooling side of a chiller, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for optimizing the control parameters of the cooling side of a chiller according to any one of claims 1 to 7 when running the program instructions.
10. A storage medium storing program instructions, characterized in that: When the program instructions are executed, the method for optimizing the control parameters of the cooling side of the chiller according to any one of claims 1 to 7 is executed.