An operating energy efficiency test method, tester and storage medium for an air conditioner unit

Abstract

The present application relates to a method for testing the operating energy efficiency of an air-conditioning unit, a tester, and a storage medium, belonging to the technical field of air conditioners. The testing method includes: obtaining environmental monitoring data and usage data of the building where the air-conditioning unit is located at the current time period; predicting the heating and cooling load requirements of the building based on a machine learning algorithm according to the environmental monitoring data and usage data; obtaining the heat supply / cooling capacity and real-time power of the air-conditioning unit at the current time period and determining whether they meet the heating and cooling load requirements. If not, calculate the heating performance coefficient / cooling energy efficiency ratio of the air-conditioning unit; determine whether the heating performance coefficient / cooling energy efficiency ratio meets a preset standard value; if so, send a start control signal to the standby air-conditioning unit; if not, send a configuration adjustment prompt signal to the management terminal. The present application not only improves the operating efficiency of the air-conditioning unit, but also ensures the environmental comfort in the building.

Description

Technical Field

[0001] This application relates to the technical field of air conditioners, and particularly to a method for testing the operating energy efficiency of an air conditioner unit, a tester, and a storage medium. Background Art

[0002] In today's society, with the acceleration of the urbanization process and the increasingly severe challenges of climate change, building energy management, especially the energy efficiency optimization of air conditioner units, has become a crucial issue. As key equipment for regulating the indoor environmental temperature, air conditioner units are widely used in commercial, industrial, and residential buildings. By providing cooling or heating, they ensure the thermal comfort and air quality of indoor spaces, and their operating efficiency directly affects the economy of energy utilization and the environmental sustainability.

[0003] Currently, common air conditioner energy efficiency testing technologies often lack in-depth analysis of complex environmental changes and usage situations within buildings, and are unable to accurately predict and real-time evaluate the changes in the operating energy efficiency of air conditioner units under different environmental conditions. It is difficult to timely detect problems such as low operating efficiency or insufficient energy supply of air conditioner units, resulting in low energy usage efficiency and difficulty in ensuring the environmental comfort within buildings. Summary of the Invention

[0004] In order to improve energy usage efficiency while ensuring the environmental comfort within buildings, this application provides a method for testing the operating energy efficiency of an air conditioner unit, a tester, and a storage medium.

[0005] In a first aspect, this application provides a method for testing the operating energy efficiency of an air conditioner unit, adopting the following technical solution:

[0006] A method for testing the operating energy efficiency of an air conditioner unit, the method comprising:

[0007] Obtain the environmental monitoring data and usage situation data of the current period of the building where the air conditioner unit is located;

[0008] Based on a machine learning algorithm, predict the heating and cooling load requirements of the building according to the environmental monitoring data and usage situation data;

[0009] Obtain the heat supply of the unit / cooling capacity of the unit and the real-time power of the air conditioner unit in the current period;

[0010] Judge whether the heat supply of the unit / cooling capacity of the unit meets the heating and cooling load requirements. If not, calculate the heating performance coefficient / cooling energy efficiency ratio of the air conditioner unit according to the real-time power of the air conditioner unit and the heat supply of the unit / cooling capacity of the unit;

[0011] Judge whether the heating performance coefficient / cooling energy efficiency ratio meets a preset standard value;

[0012] If so, send a start control signal to the standby air conditioning unit;

[0013] If not, send a configuration adjustment prompt signal to the management terminal.

[0014] By adopting the above technical solution, environmental monitoring data and usage data in the building where the air conditioning unit is located are collected, the cooling and heating load demands are predicted based on machine learning algorithms, the heat supply / cooling capacity and real-time power of the air conditioning unit are obtained in real time, and directly compared with the predicted cooling and heating load demands. When it is found that the supply and demand do not match, the energy efficiency evaluation process is immediately entered, and the operating efficiency of the unit is evaluated by calculating COP or EER. According to the evaluation results, the standby unit is automatically triggered to start or a configuration adjustment prompt is sent, realizing the intelligence and automation level of the system. It not only improves the operating efficiency of the air conditioning unit, reduces energy consumption, but also enhances the comfort and environmental sustainability of the building.

[0015] Optionally, based on the machine learning algorithm, the steps of predicting the cooling and heating load demands of the building according to the environmental monitoring data and usage data include:

[0016] Input the environmental monitoring data and usage data into the cooling and heating load demand prediction model to obtain the prediction result of the cooling and heating load demands of the building; wherein, the cooling and heating load demand prediction model is pre-trained according to the historical environmental monitoring data, historical usage data and corresponding cooling and heating load demand labels of the building.

[0017] By adopting the above technical solution, a machine learning model is trained using historical data, the relationship between environmental monitoring data and usage data and the cooling and heating load demands is established, and the model is applied to analyze the current data to predict the immediate cooling and heating load demands of the building, facilitating the early understanding of the cooling and heating demands in future periods, providing a scientific basis for adjusting the operating strategy of the air conditioning unit, reducing energy waste, and improving the overall energy efficiency.

[0018] Optionally, the steps of obtaining the heat supply of the air conditioning unit in the current period include:

[0019] Collect the flow rate G on the user side of the unit, the hot water density ρ1, the constant pressure specific heat C1 of the hot water, the supply water temperature Tg and the return water temperature Th of the unit in the current period of the air conditioning unit, and obtain the heat supply Qr of the unit = G×ρ1×C1×(Tg - Th) / 3600;

[0020] The steps of obtaining the cooling capacity of the air conditioning unit in the current period include:

[0021] Collect the flow rate G of the user side of the air conditioner unit, the cold water density ρ2, the constant pressure specific heat C2 of the cold water, the supply water temperature Tg and the return water temperature Th of the unit at the current time period, and obtain the cooling capacity Q1 of the unit = G×ρ2×C2×(Th - Tg) / 3600.

[0022] By adopting the above technical solution, the total amount of heat energy / cooling energy provided by the air conditioner unit within a given time period can be accurately calculated, providing key data for evaluating the energy efficiency and performance of the system in the cooling mode.

[0023] Optionally, the formulas for calculating the heating performance coefficient COP / cooling energy efficiency ratio EER of the air conditioner unit are respectively: COP = Qr / N, EER = Q1 / N; where Qr is the heating capacity of the unit, Q1 is the cooling capacity of the unit, and N is the real-time power of the air conditioner unit.

[0024] By adopting the above technical solution, based on the accurate calculation of the formula, a quantitative index is provided for evaluating the energy utilization efficiency of the air conditioner unit in the cooling / heating mode.

[0025] Optionally, the test method further includes the training step of the heating and cooling load demand prediction model, and the training step includes:

[0026] Obtain a sample data set and perform data preprocessing: the sample data set includes the historical environmental monitoring data, historical usage data of the building, and the corresponding heating and cooling load demand labels;

[0027] Extract features from the preprocessed sample data set;

[0028] Divide the sample data set after feature extraction into a training set, a validation set and a test set;

[0029] Based on the training set, perform initial training on a pre-constructed neural network model, and based on the validation set and the test set, perform validation and model parameter optimization and adjustment on the initially trained neural network model to obtain the trained heating and cooling load demand prediction model.

[0030] By adopting the above technical solution, a heating and cooling load demand prediction model with accurate prediction ability is established, ensuring the generalization performance and practical application value of the model, and providing strong technical support for building energy management.

[0031] Optionally, the steps of performing initial training on a pre-constructed neural network model based on the training set, and performing validation and model parameter optimization and adjustment on the initially trained neural network model based on the validation set and the test set include:

[0032] Input the training set into a pre - constructed neural network model for training, optimize the model parameters and calculate the loss function of the neural network model until the loss function meets the preset conditions or the model iteration times reach the preset number of times, and obtain the trained prediction model for heating and cooling load demands;

[0033] Validate the prediction model for heating and cooling load demands based on the validation set, evaluate the performance of the prediction model for heating and cooling load demands and adjust the hyperparameters of the model;

[0034] Test the prediction ability of the adjusted prediction model for heating and cooling load demands based on the test set.

[0035] By adopting the above - mentioned technical solution, the model is trained using the training set, model selection and hyperparameter fine - tuning are carried out through the validation set, the loss function and prediction error are monitored to avoid overfitting; the test set is used to evaluate the final model, the prediction error is analyzed, and the model or features are adjusted according to the error type, thereby further improving the generalization ability of the model.

[0036] Optionally, after the step of judging whether the heat supply / cooling capacity of the unit meets the heating and cooling load demands, the following steps are further included:

[0037] If the heat supply / cooling capacity of the unit meets the heating and cooling load demands, obtain the over - supply amount of the unit according to the heat supply / cooling capacity of the unit and the heating and cooling load demands;

[0038] Determine the over - supply percentage according to the ratio of the over - supply amount of the unit to the heating and cooling load demands;

[0039] Judge whether the over - supply percentage is higher than the preset threshold. If so, determine the corresponding over - supply level according to the over - supply percentage;

[0040] Based on the preset policy library, match the corresponding energy - saving strategy according to the over - supply level and send it to the management terminal.

[0041] By adopting the above - mentioned technical solution, the over - supply amount is accurately quantified and classified for management, and different degrees of energy - saving measures are taken according to the severity of over - supply. By adjusting the operating state of the unit in a timely manner, unnecessary energy consumption is avoided while ensuring the comfort of the building.

[0042] Optionally, after the step of sending a configuration adjustment prompt signal to the management terminal, the following steps are further included:

[0043] Monitor the heating / cooling performance of the air - conditioning unit, and obtain the change amount of the heating performance coefficient / cooling energy efficiency ratio of the air - conditioning unit after a preset time period;

[0044] Judge whether the change amount meets the preset change threshold;

[0045] If so, obtain the configuration adjustment policy of the management terminal and store it in the preset configuration policy library;

[0046] If not, send a fault prompt signal to the management terminal according to the change amount.

[0047] By adopting the above technical solution, the energy efficiency improvement brought by real-time tracking and quantification of adjustment measures is ensured, and the energy efficiency improvement is judged by setting a threshold, ensuring that further actions are triggered only when the adjustment effect significantly deviates from the expectation, avoiding unnecessary frequent interventions; at the same time, when the energy efficiency improvement is significant, the effective configuration adjustment policy is stored, thus accumulating data assets for long-term performance optimization.

[0048] In a second aspect, the present application provides an air-conditioning unit operation energy efficiency tester, adopting the following technical solution:

[0049] An air-conditioning unit operation energy efficiency tester, the tester includes:

[0050] An environmental data acquisition module, configured to acquire the environmental monitoring data and usage data of the building where the air-conditioning unit is located at the current time;

[0051] A heating and cooling load demand prediction module, configured to predict the heating and cooling load demand of the building based on a machine learning algorithm according to the environmental monitoring data and usage data;

[0052] An air-conditioning data acquisition module, configured to acquire the heat supply / cooling capacity and real-time power of the air-conditioning unit at the current time;

[0053] A first judgment module, configured to judge whether the heat supply / cooling capacity of the unit meets the heating and cooling load demand, if not, output a first judgment result;

[0054] A calculation module, configured to respond to the first judgment result, and calculate the heating performance coefficient / cooling energy efficiency ratio of the air-conditioning unit according to the real-time power of the air-conditioning unit and the heat supply / cooling capacity of the unit;

[0055] A second judgment result, configured to judge whether the heating performance coefficient / cooling energy efficiency ratio meets a preset standard value; if so, output a second judgment result; if not, output a third judgment result;

[0056] A standby unit control module, configured to send a start control signal to the standby air-conditioning unit in response to the second judgment result;

[0057] A configuration adjustment prompt module, configured to send a configuration adjustment prompt signal to the management terminal in response to the third judgment result.

[0058] In a third aspect, the present application provides a computer-readable storage medium, adopting the following technical solution:

[0059] A computer-readable storage medium stores a computer program that can be loaded and executed by a processor to perform any of the methods in the first aspect.

[0060] In summary, the present application includes at least one of the following beneficial technical effects:

[0061] 1. By integrating multiple links such as environmental perception, data prediction, real-time monitoring, energy efficiency evaluation, and intelligent control, a closed-loop energy efficiency management system is formed. Compared with the existing technologies with single functions, it creatively realizes comprehensive energy efficiency optimization and management.

[0062] 2. Using machine learning for load prediction and combining real-time data analysis for intelligent decision-making significantly improves the intelligence level of the system and reduces manual intervention, which is a major innovation in the management method of traditional HVAC systems.

[0063] 3. Different countermeasures are taken according to the energy efficiency evaluation results, such as starting standby units or adjusting configurations. This flexible strategy design shows in-depth consideration and response to complex scenarios, reflects the intelligence and automation level of the system, and has high innovation value. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is the first process schematic diagram of an air conditioner unit operation energy efficiency test method in one embodiment of the present application.

[0065] Figure 2 is the second process schematic diagram of an air conditioner unit operation energy efficiency test method in one embodiment of the present application.

[0066] Figure 3 is the third process schematic diagram of an air conditioner unit operation energy efficiency test method in one embodiment of the present application.

[0067] Figure 4 is the fourth process schematic diagram of an air conditioner unit operation energy efficiency test method in one embodiment of the present application.

[0068] Figure 5 is the fifth process schematic diagram of an air conditioner unit operation energy efficiency test method in one embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] In order to make the objectives, technical solutions, and advantages of the present application clearer, the following further describes the present application in detail with reference to the accompanying Figures 1-5 drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0070] An embodiment of the present application discloses a method for testing the operating energy efficiency of an air-conditioning unit.

[0071] Referring to Figure 1 , a method for testing the operating energy efficiency of an air-conditioning unit, the testing method includes:

[0072] Step S101, obtaining environmental monitoring data and usage data of the current period of the building where the air-conditioning unit is located;

[0073] Among them, the environmental monitoring data and usage data can be collected in real time through a sensor network and a data acquisition system installed inside and outside the building;

[0074] Specifically, the environmental monitoring data includes indoor and outdoor temperature, humidity, sunlight intensity, wind speed, etc., which directly affect the heat exchange efficiency and load demand of the building; the usage data, such as the number of people in the building, the operating status of equipment, the occupancy of rooms, etc., reflects the dynamic changes of heat sources in the building and is crucial for accurately predicting the cooling and heating loads.

[0075] Step S102, based on a machine learning algorithm, predicting the cooling and heating load demands of the building according to the environmental monitoring data and usage data;

[0076] Among them, the machine learning algorithm can identify complex data patterns, effectively process non-linear relationships, and improve the prediction accuracy; in some embodiments, a machine learning algorithm, such as a neural network, a support vector machine or a random forest, can be used to train a model to predict the cooling and heating load demands of the building in the current period based on historical data and currently collected environmental monitoring data and usage data;

[0077] It can be understood that by understanding the cooling and heating demands in the future period in advance, it provides a scientific basis for adjusting the operating strategy of the air-conditioning unit, reduces energy waste, and improves the overall energy efficiency.

[0078] Step S103, obtaining the heat supply / cooling capacity of the unit and the real-time power of the air-conditioning unit in the current period;

[0079] Among them, the operating status of the air-conditioning unit is monitored in real time, including the heat supply / cooling capacity (i.e., the actual cooling and heating capacity provided) and the real-time power (i.e., the consumed electric energy). These data are usually directly provided by sensors built into the unit and are key parameters for evaluating the operating efficiency and energy efficiency of the unit.

[0080] Step S104, determining whether the heat supply / cooling capacity of the unit meets the cooling and heating load demands. If not, jump to step S105;

[0081] Among them, the actual heat supply / cooling capacity is compared with the predicted heating and cooling load demands to determine whether the current demands of the building can be met, so as to ensure that the supply of the air conditioning system matches the actual demands and avoid the situation of insufficient supply and demand;

[0082] Step S105: Calculate the heating performance coefficient / cooling energy efficiency ratio of the air conditioning unit according to the real-time power of the air conditioning unit and the heat supply / cooling capacity of the unit;

[0083] It can be understood that if the heat supply / cooling capacity cannot meet the demand, the heating performance coefficient (COP) / cooling energy efficiency ratio (EER) is further calculated to facilitate determining the root cause of the problem; among them, COP and EER are important indicators for measuring the energy efficiency of the air conditioning system, representing the heat or cooling capacity that can be provided per 1 kilowatt of electricity consumed during the heating and cooling processes respectively;

[0084] Specifically, the formulas for calculating the heating performance coefficient COP / cooling energy efficiency ratio EER of the air conditioning unit are: COP = Qr / N, EER = Q1 / N; where, Qr is the heat supply of the unit, Q1 is the cooling capacity of the unit, and N is the real-time power of the air conditioning unit, with the unit of Kw.

[0085] Among them, the heating performance coefficient (COP) is an indicator for measuring the energy efficiency of the air conditioning unit in the heating mode, defined as the ratio of the thermal energy (Qr) provided by the air conditioning unit to the electrical energy (N) consumed; the cooling energy efficiency ratio (EER) is an indicator for measuring the energy efficiency of the air conditioning unit in the cooling mode, defined as the ratio of the cooling energy (Q1) provided by the air conditioning unit to the electrical energy (N) consumed.

[0086] It should be noted that the heating performance coefficient (COP) and the cooling energy efficiency ratio (EER) are dimensionless ratios, which represent the cooling or heating capacity (measured in kilowatts or kilojoules per hour) that the air conditioning system can provide per 1 kilowatt-hour of electrical energy consumed.

[0087] Step S106: Determine whether the heating performance coefficient / cooling energy efficiency ratio meets the preset standard value; if so, jump to step S107; if not, jump to step S108;

[0088] Among them, the preset standard value can be preset according to the actual situation or historical experience;

[0089] Step S107: Send a start control signal to the standby air conditioning unit;

[0090] Among them, if the heating performance coefficient (COP) / cooling energy efficiency ratio (EER) reaches the preset standard value, it indicates that the problem may lie in the insufficient unit capacity rather than the low operating efficiency. At this time, it is necessary to start the standby unit to enhance the energy supply;

[0091] Step S108, send a configuration adjustment prompt signal to the management terminal.

[0092] It can be understood that if the heating performance coefficient (COP) / cooling energy efficiency ratio (EER) does not reach the preset standard, the configuration needs to be adjusted to prompt the management terminal to perform optimization settings or maintenance.

[0093] In some embodiments, the configuration adjustment strategy specifically includes optimizing operating parameters, such as adjusting the evaporation temperature and condensation temperature (for a refrigeration system), increasing the flow rate of chilled water or hot water, optimizing the operating frequency of the fan or pump, etc., to reduce energy consumption and improve COP / EER; it can also include reconfiguring or optimizing the entire air-conditioning unit, including but not limited to adjusting the system capacity, improving the pipeline layout, increasing or decreasing the refrigerant circulation volume, etc., to match the actual load demand and improve the overall system efficiency.

[0094] In the above embodiments, environmental monitoring data and usage data in the building where the air-conditioning unit is located are collected, the cooling and heating load demands are predicted based on machine learning algorithms, the heating / cooling capacity and real-time power of the air-conditioning unit are obtained in real time, and directly compared with the predicted cooling and heating load demands. When it is found that the supply and demand do not match, the energy efficiency evaluation process is immediately entered, and the operating efficiency of the unit is evaluated by calculating COP or EER. According to the evaluation results, the standby unit is automatically triggered to start or a configuration adjustment prompt is sent, realizing the intelligence and automation level of the system, not only improving the operating efficiency of the air-conditioning unit, reducing energy consumption, but also enhancing the comfort and environmental sustainability of the building.

[0095] As an implementation manner of step S102, the step of predicting the cooling and heating load demands of the building based on machine learning algorithms according to environmental monitoring data and usage data includes:

[0096] Input the environmental monitoring data and usage data into the cooling and heating load demand prediction model to obtain the prediction result of the cooling and heating load demands of the building; among them, the cooling and heating load demand prediction model is pre-trained according to the historical environmental monitoring data, historical usage data and corresponding cooling and heating load demand labels of the building.

[0097] In the above embodiments, the machine learning model is trained using historical data to establish the relationship between environmental monitoring data and usage data and the cooling and heating load demands, and this model is applied to analyze the current data to predict the immediate cooling and heating load demands of the building, facilitating the early understanding of the cooling and heating demands in the future period, providing a scientific basis for adjusting the operating strategy of the air-conditioning unit, reducing energy waste, and enhancing the overall energy efficiency.

[0098] As an implementation manner of step S103, the step of obtaining the heating capacity of the air-conditioning unit in the current period includes:

[0099] Collect the flow rate G on the user side of the air-conditioning unit, the hot water density ρ1, the constant pressure specific heat C1 of the hot water, the supply water temperature Tg and the return water temperature Th of the unit at the current time period, and obtain the heat supply Qr of the unit = G×ρ1×C1×(Tg - Th) / 3600;

[0100] The steps for obtaining the cooling capacity of the air-conditioning unit at the current time period include:

[0101] Collect the flow rate G on the user side of the air-conditioning unit, the cold water density ρ2, the constant pressure specific heat C2 of the cold water, the supply water temperature Tg and the return water temperature Th of the unit at the current time period, and obtain the cooling capacity Q1 of the unit = G×ρ2×C2×(Th - Tg) / 3600.

[0102] Among them, by installing a flow meter and supply and return water temperature sensors on the user side of the air-conditioning unit, parameters such as the flow rate G, the density ρ of hot (cold) water, the constant pressure specific heat C of hot (cold) water, the supply water temperature Tg and the return water temperature Th of the unit can be collected in real time; among them, the unit of the flow rate G is m 3 / h, the unit of the density ρ of hot (cold) water is kg / m 3 , the unit of the constant pressure specific heat C of hot (cold) water is KJ / (kg*℃), and the units of the supply water temperature Tg and the return water temperature Th of the unit are ℃.

[0103] In the above implementation, by accurately calculating the total amount of thermal energy / cooling energy provided by the air-conditioning unit within a given time period, key data is provided for evaluating the energy efficiency and performance of the system in the cooling mode.

[0104] Refer to Figure 2 , as a further implementation of the test method, it also includes the training steps of the cooling and heating load demand prediction model, and the training steps include:

[0105] Step S201, obtain the sample data set and perform data preprocessing:

[0106] Among them, the sample data set includes the historical environmental monitoring data, historical usage data of the building and the corresponding cooling and heating load demand labels; specifically, environmental monitoring data (such as temperature, humidity, light, etc.) and usage data (such as personnel flow, equipment operation status, etc.) can be collected from historical records, and at the same time, the actual cooling and heating load demands corresponding to the corresponding periods are matched as labels.

[0107] In some embodiments, the data preprocessing steps include cleaning the data set, processing missing values and outliers, and performing data standardization or normalization to facilitate model processing and improve model performance, thereby ensuring the data quality of the model training and providing accurate inputs for feature extraction and model learning.

[0108] Step S202: Extract features from the preprocessed sample dataset;

[0109] Among them, extract features valuable for predicting cooling and heating loads from the preprocessed dataset, such as time features (seasons, time periods of the day), environmental factors (relationship between average temperature and load), and usage features (correlation between occupancy density and load). Additionally, feature engineering techniques may be applied to create new features, such as moving averages of historical loads, combined features of weather conditions, etc.

[0110] Step S203: Divide the sample dataset after feature extraction into a training set, a validation set, and a test set;

[0111] Among them, divide the dataset after feature extraction into a training set, a validation set, and a test set, with typical ratios such as 70%, 15%, 15%. The training set is used for model learning, the validation set is used to evaluate the model performance during training for adjusting model parameters, and the test set is used to evaluate its generalization ability after the final model is determined.

[0112] Step S204: Conduct initial training on the pre - constructed neural network model based on the training set, and verify the initially trained neural network model and optimize and adjust the model parameters based on the validation set and the test set to obtain a trained cooling and heating load demand prediction model.

[0113] In one embodiment of the present application, a long short - term memory network (LSTM) in deep learning can be used to construct a cooling and heating load demand prediction model. Design the LSTM model architecture, including an input layer, an LSTM hidden layer, an output layer, etc., and define a loss function and an optimizer.

[0114] Specifically, during the process of constructing the LSTM model, consider a multi - layer LSTM structure to capture long - term dependencies. A dropout layer can be connected after each layer of LSTM units to reduce the risk of overfitting, and finally a fully connected layer is connected to output the predicted load value. By adjusting hyperparameters such as the number of LSTM units, learning rate, batch size, etc., to balance model complexity and training efficiency.

[0115] In the above - mentioned implementation manner, a cooling and heating load demand prediction model with accurate prediction ability is established, ensuring the generalization performance and practical application value of the model, and providing strong technical support for building energy management.

[0116] Refer to Figure 3 , as an implementation manner of step S204, the steps of conducting initial training on the pre - constructed neural network model based on the training set, and verifying the initially trained neural network model and optimizing and adjusting the model parameters based on the validation set and the test set include:

[0117] Step S301: Input the training set into a pre - constructed neural network model for training, optimize the model parameters, and calculate the loss function of the neural network model until the loss function meets the preset conditions or the number of model iterations reaches the preset number, obtaining a trained prediction model for heating and cooling load demands.

[0118] Among them, through initial training, the model can capture the basic features and trends of the data set, laying a foundation for further optimization and adjustment.

[0119] Step S302: Validate the prediction model for heating and cooling load demands based on the validation set, evaluate the performance of the prediction model for heating and cooling load demands, and adjust the hyperparameters of the model.

[0120] Among them, through the validation set, the model is optimized and the hyperparameters are finely tuned, monitoring the loss function and prediction error to avoid overfitting; in some embodiments, K - fold cross - validation can be used to further ensure the generalization ability of the model.

[0121] Step S303: Test the prediction ability of the adjusted prediction model for heating and cooling load demands based on the test set.

[0122] Among them, performance indicators such as mean squared error (MSE) and mean absolute error (MAE) can be calculated to evaluate the prediction accuracy, ensuring the robustness and prediction effect of the model.

[0123] In the above - mentioned embodiments, the model is trained using the training set, the model is selected and the hyperparameters are finely tuned through the validation set, monitoring the loss function and prediction error to avoid overfitting; the final model is evaluated using the test set, analyzing the prediction error, and adjusting the model or features according to the error type, thereby further improving the generalization ability of the model.

[0124] Refer to Figure 4 As a further implementation of the test method, after the step of judging whether the heat supply of the unit / cooling supply of the unit meets the heating and cooling load demands in step S106, the following steps are further included:

[0125] Step S401: If the heat supply of the unit / cooling supply of the unit meets the heating and cooling load demands, obtain the over - supply of the unit according to the heat supply of the unit / cooling supply of the unit and the heating and cooling load demands.

[0126] Among them, the difference between the heat supply or cooling supply of the unit and the heating and cooling load demands is the over - supply. By quantifying the energy supply exceeding the demand, it provides quantitative data support for subsequent refined management, helping to identify and locate the source of energy waste.

[0127] Step S402: Determine the over - supply percentage according to the ratio of the over - supply of the unit and the heating and cooling load demands.

[0128] Among them, the over-supply percentage intuitively shows the proportion of the over-supply volume to the demand volume, which is convenient for further classification and assessment of the severity of over-supply, facilitating the quick identification of the severity of the over-supply problem, providing an intuitive basis for taking measures, and at the same time facilitating comparison with the preset threshold to achieve automated response;

[0129] Step S403, determine whether the over-supply percentage is higher than the preset threshold. If so, jump to step S404; if not, do not perform any operation;

[0130] Step S404, determine the corresponding over-supply level according to the over-supply percentage;

[0131] In some embodiments, the over-supply levels may include slight over-supply, moderate over-supply, or severe over-supply. The preset threshold can be set as the over-supply percentage corresponding to slight over-supply to distinguish normal energy supply fluctuations from excessive energy supply situations that need to be optimized, avoid unnecessary adjustments, and at the same time ensure the optimization of energy efficiency.

[0132] Step S405, based on the preset policy library, match the corresponding energy-saving strategy according to the over-supply level and send it to the management terminal.

[0133] Among them, the determined energy-saving strategy is automatically sent to the management system terminal through the network or communication protocol so that the operator or the automated system can directly execute it, thus ensuring the rapid execution of the decision and reducing human delays.

[0134] In some embodiments, the preset policy library contains a variety of energy-saving solutions, from mild adjustments (such as fine-tuning device operation parameters) to severe interventions (such as temporarily shutting down some devices or enabling standby systems), which not only avoids waste of resources but also ensures that effective measures can be taken in a timely manner when necessary, maintaining the economy and efficiency of energy use;

[0135] Specifically, the energy-saving strategy corresponding to slight over-supply can be: slightly adjusting the output power of the unit, using the gradient descent method to gradually approach the actual demand, and reducing the impact of mutations on the system stability; the energy-saving strategy corresponding to moderate over-supply is: introducing a PID controller and dynamically adjusting the PID parameters (proportional P, integral I, differential D) according to the over-supply volume to achieve fast response while maintaining stability; the energy-saving strategy corresponding to severe over-supply is: directly adopting an emergency energy-saving mode, significantly reducing the output power to the safety threshold, starting the standby unit or adjusting other auxiliary systems to maintain the basic demand, and at the same time sending an alarm to notify the maintenance team for inspection.

[0136] In the above embodiments, the over-supply volume is accurately quantified and classified for management. Different degrees of energy-saving measures are taken according to the severity of over-supply. By timely adjusting the operating state of the unit, unnecessary energy consumption is avoided while ensuring the comfort of the building.

[0137] Refer to Figure 5, as a further implementation of the test method, after the step of sending a configuration adjustment prompt signal to the management terminal, the following steps are also included:

[0138] Step S501, monitor the heating / cooling performance of the air conditioner unit, and obtain the change amount of the heating performance coefficient / cooling energy efficiency ratio of the air conditioner unit after a preset time period;

[0139] In one embodiment of the present application, after sending a configuration adjustment prompt signal to the management terminal, the system enters the performance monitoring stage. By continuously collecting the heating performance coefficient COP or cooling energy efficiency ratio EER data of the air conditioner unit after a preset time period (such as 24 hours) and comparing it with the value before adjustment, the change amount can be calculated;

[0140] It can be understood that this step can evaluate the actual change in the energy efficiency of the air conditioner unit after configuration adjustment, providing data support for further decision-making.

[0141] Step S502, determine whether the change amount meets a preset change threshold; if so, jump to step S503; if not, jump to step S504;

[0142] Among them, the system sets a preset change threshold, and compares the obtained change amount with the preset change threshold to determine whether the configuration adjustment has achieved the expected effect. If the threshold is met, it indicates that the configuration adjustment is effective; if not, it may mean that there is a fault in the air conditioner unit, and further troubleshooting and maintenance may be required.

[0143] Step S503, obtain the configuration adjustment strategy of the management terminal and store it in the preset configuration strategy library;

[0144] Among them, when the change amount meets the preset threshold, the system automatically obtains the current effective configuration adjustment strategy from the management terminal and stores it in the preset configuration strategy library. By accumulating successful configuration adjustment cases, the resources of the strategy library are enriched, thus providing the possibility of quick call in future similar situations.

[0145] Step S504, send a fault prompt signal to the management terminal according to the change amount.

[0146] Among them, if the change amount fails to reach the preset threshold, the system automatically sends a fault prompt message to the management terminal. This message includes the specific value of the change amount, the comparison of performance parameters before and after adjustment, and suggestions for possible cause analysis, which is convenient for management personnel to quickly locate the problem and take corresponding measures; through the timely fault feedback mechanism, the time difference from problem discovery to solution is shortened, which helps to prevent potential energy waste or equipment damage and protects the stable operation and economy of the system.

[0147] In the above embodiments, the real-time tracking and quantification of the energy efficiency improvement brought about by the adjustment measures, and the judgment of the energy efficiency improvement by setting thresholds ensure that further actions are only triggered when the adjustment effect significantly deviates from the expectation, avoiding unnecessary frequent interventions. At the same time, when the energy efficiency improvement is significant, the effective configuration adjustment strategy is stored, thus accumulating data assets for long-term performance optimization.

[0148] The embodiment of the present application also discloses an operating energy efficiency tester for an air-conditioning unit.

[0149] An operating energy efficiency tester for an air-conditioning unit includes:

[0150] An environmental data acquisition module, configured to acquire the environmental monitoring data and usage data of the building where the air-conditioning unit is located at the current time;

[0151] A heating and cooling load demand prediction module, configured to predict the heating and cooling load demand of the building based on a machine learning algorithm according to the environmental monitoring data and usage data;

[0152] An air-conditioning data acquisition module, configured to acquire the heat supply / cooling capacity and real-time power of the air-conditioning unit at the current time;

[0153] A first judgment module, configured to judge whether the heat supply / cooling capacity of the unit meets the heating and cooling load demand. If not, output a first judgment result;

[0154] A calculation module, configured to respond to the first judgment result, and calculate the heating performance coefficient / cooling energy efficiency ratio of the air-conditioning unit according to the real-time power of the air-conditioning unit and the heat supply / cooling capacity of the unit;

[0155] A second judgment result, configured to judge whether the heating performance coefficient / cooling energy efficiency ratio meets a preset standard value. If so, output a second judgment result; if not, output a third judgment result;

[0156] A standby unit control module, configured to respond to the second judgment result, and send a start control signal to the standby air-conditioning unit;

[0157] A configuration adjustment prompt module, configured to respond to the third judgment result, and send a configuration adjustment prompt signal to the management terminal.

[0158] It should be noted that the tester in the embodiment of the present application integrates functions such as data testing, calculation, storage, and display. The real-time cooling and heating energy efficiency of the unit is measured and obtained through the real-time power of the air-conditioning unit and the heat supply / cooling capacity of the unit, and a remote transmission interface is reserved. The display content may include real-time flow, supply and return water temperatures, and real-time energy efficiency, demonstrating broad market value and economic practicality.

[0159] The air conditioner unit operation energy efficiency tester according to the embodiments of the present application can implement any one of the above-mentioned test methods, and the specific working processes of each module in the tester can refer to the corresponding processes in the above-mentioned method embodiments.

[0160] In several embodiments provided by the present application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0161] The embodiments of the present application also disclose a computer-readable storage medium.

[0162] A computer-readable storage medium stores a computer program that can be loaded and executed by a processor to perform any one of the above-mentioned air conditioner unit operation energy efficiency test methods.

[0163] Among them, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or device; the program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0164] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0165] The above are all the preferred embodiments of the present application. Without restricting the protection scope of the present application in accordance with this, any feature disclosed in this specification (including the abstract and drawings), unless specifically described, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically described, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for testing the operating energy efficiency of an air conditioning unit, characterized in that, The method includes: Obtaining the environmental monitoring data and usage data of the building where the air-conditioning unit is located at the current time period; among them, the environmental monitoring data includes indoor and outdoor temperature, humidity, sunlight intensity, and wind speed; the usage data includes the number of people in the building, the operating status of equipment, and the room occupancy situation; Based on a machine learning algorithm, predicting the heating and cooling load requirements of the building according to the environmental monitoring data and usage data; Obtaining the heat supply / cooling capacity and real-time power of the unit of the air-conditioning unit at the current time period; Judging whether the heat supply / cooling capacity of the unit meets the heating and cooling load requirements. If not, then calculate the heating performance coefficient / cooling energy efficiency ratio of the air-conditioning unit according to the real-time power of the air-conditioning unit and the heat supply / cooling capacity of the unit; Judging whether the heating performance coefficient / cooling energy efficiency ratio meets a preset standard value; If so, sending a start control signal to the standby air-conditioning unit; If not, sending a configuration adjustment prompt signal to the management terminal; among them, the configuration adjustment strategy includes optimizing the operating parameters or reconfiguring and optimizing the entire air-conditioning unit; After the step of sending the configuration adjustment prompt signal to the management terminal, it further includes: Monitoring the heating / cooling performance of the air-conditioning unit, and obtaining the change amount of the heating performance coefficient / cooling energy efficiency ratio of the air-conditioning unit after a preset time period; Judging whether the change amount meets a preset change threshold; If so, obtaining the configuration adjustment strategy of the management terminal and storing it in a preset configuration strategy library; If not, sending a fault prompt signal to the management terminal according to the change amount.

2. The method for testing the operating energy efficiency of an air-conditioning unit according to claim 1, wherein, The step of predicting the heating and cooling load requirements of the building based on a machine learning algorithm according to the environmental monitoring data and usage data includes: Inputting the environmental monitoring data and usage data into a heating and cooling load demand prediction model to obtain a heating and cooling load demand prediction result of the building; among them, the heating and cooling load demand prediction model is pre-trained according to the historical environmental monitoring data, historical usage data, and corresponding heating and cooling load demand labels of the building.

3. A method for testing the operating energy efficiency of an air conditioning unit according to claim 1, characterized in that, The step of obtaining the heat supply of the unit of the air-conditioning unit at the current time period includes: Collecting the unit user-side flow rate G, hot water density ρ1, constant pressure specific heat C1 of hot water, unit supply water temperature Tg, and unit return water temperature Th of the air-conditioning unit at the current time period, and obtaining the heat supply of the unit Qr = G×ρ1×C1×(Tg - Th) / 3600; The step of obtaining the cooling capacity of the unit of the air-conditioning unit at the current time period includes: Collecting the unit user-side flow rate G, cold water density ρ2, constant pressure specific heat C2 of cold water, unit supply water temperature Tg, and unit return water temperature Th of the air-conditioning unit at the current time period, and obtaining the cooling capacity of the unit Q1 = G×ρ2×C2×(Th - Tg) / 3600.

4. A method for testing the operating energy efficiency of an air-conditioning unit according to claim 3, characterized in that, The formulas for calculating the heating performance coefficient COP / cooling energy efficiency ratio EER of the air-conditioning unit are respectively: COP = Qr / N, EER = Q1 / N; where Qr is the heat supply of the unit, Q1 is the cooling capacity of the unit, and N is the real-time power of the air-conditioning unit.

5. A method for testing the operating energy efficiency of an air conditioning unit according to claim 2, characterized in that, The described testing method further includes a training step for the heating and cooling load demand prediction model, and the training step includes: Obtaining a sample data set and performing data preprocessing: The sample data set includes the historical environmental monitoring data, historical usage data of the building, and the corresponding heating and cooling load demand labels; Performing feature extraction on the preprocessed sample data set; Dividing the sample data set after feature extraction into a training set, a validation set, and a test set; Based on the training set, initially training a pre-constructed neural network model, and based on the validation set and the test set, validating the initially trained neural network model and optimizing and adjusting the model parameters to obtain the trained heating and cooling load demand prediction model.

6. The method for testing the operating energy efficiency of an air-conditioning unit according to claim 5, wherein The step of initially training a pre-constructed neural network model based on the training set and validating the initially trained neural network model and optimizing and adjusting the model parameters based on the validation set and the test set includes: Inputting the training set into the pre-constructed neural network model for training, optimizing the model parameters and calculating the loss function of the neural network model until the loss function meets the preset conditions or the model iteration times reach the preset number of times to obtain the trained heating and cooling load demand prediction model; Validating the heating and cooling load demand prediction model based on the validation set, evaluating the performance of the heating and cooling load demand prediction model and adjusting the hyperparameters of the model; Testing the prediction ability of the adjusted heating and cooling load demand prediction model based on the test set.

7. A method for testing the operating energy efficiency of an air conditioning unit according to any one of claims 1 to 6, characterized in that, After the step of judging whether the heat supply / cooling capacity of the unit meets the heating and cooling load demand, it further includes: If the heat supply / cooling capacity of the unit meets the heating and cooling load demand, obtaining the over-supply amount of the unit according to the heat supply / cooling capacity of the unit and the heating and cooling load demand; Determining the over-supply percentage according to the ratio of the over-supply amount of the unit to the heating and cooling load demand; Judging whether the over-supply percentage is higher than a preset threshold, if so, determining the corresponding over-supply level according to the over-supply percentage; Based on a preset policy library, matching the corresponding energy-saving strategy according to the over-supply level and sending it to the management terminal.

8. An energy efficiency tester for the operation of an air conditioning unit, characterized in that, For implementing an air-conditioning unit operation energy efficiency testing method according to any one of claims 1 to 7, the testing instrument includes: An environmental data acquisition module for acquiring the environmental monitoring data and usage data of the building where the air-conditioning unit is located at the current time; A heating and cooling load demand prediction module for predicting the heating and cooling load demand of the building based on a machine learning algorithm according to the environmental monitoring data and usage data; An air-conditioning data acquisition module for acquiring the heat supply / cooling capacity and real-time power of the air-conditioning unit at the current time; A first judgment module for judging whether the heat supply / cooling capacity of the unit meets the heating and cooling load demand, if not, outputting a first judgment result; A calculation module for, in response to the first judgment result, calculating the heating performance coefficient / cooling energy efficiency ratio of the air-conditioning unit according to the real-time power of the air-conditioning unit and the heat supply / cooling capacity of the unit; The second judgment result is used to judge whether the heating performance coefficient / cooling energy efficiency ratio meets the preset standard value; if so, the second judgment result is output; if not, the third judgment result is output; The standby unit control module is used to send a start control signal to the standby air conditioner unit in response to the second judgment result; The configuration adjustment prompt module is used to send a configuration adjustment prompt signal to the management terminal in response to the third judgment result.

9. A computer-readable storage medium, characterized in that: A computer program is stored that can be loaded and executed by a processor, such as the method according to any one of claims 1 to 7.

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