Intelligent energy management system and energy-saving control method thereof

Abstract

The invention provides an intelligent energy management system and an energy-saving control method thereof, relates to the technical field of energy-saving control, and aims to monitor the running process of air conditioner fan equipment in real time. Energy efficiency temperature difference data of the air conditioner fan equipment is extracted from the monitored operation state data, an energy efficiency deviation threshold value of the energy efficiency temperature difference data is determined, and the energy efficiency temperature difference data is converted into an energy efficiency temperature difference sequence based on the energy efficiency deviation threshold value; the energy efficiency temperature difference of the air conditioner fan equipment is predicted according to the energy efficiency temperature difference sequence, energy efficiency temperature difference prediction data are obtained, and energy efficiency temperature difference stable entropy is determined through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; the energy efficiency temperature difference prediction data is compensated according to the energy efficiency temperature difference stable entropy, then the power output of the air conditioner fan equipment is adjusted based on the compensated energy efficiency temperature difference prediction data, the power output of the air conditioner fan equipment can be adjusted in advance, power dynamic regulation and control of the air conditioner fan equipment are achieved, and energy waste is avoided.

Description

Technical Field

[0001] The present application relates to the field of energy-saving control technology, and more specifically, to an intelligent energy management system and an energy-saving control method thereof. Background Art

[0002] The intelligent energy management system is an integrated solution that aims to help enterprises, buildings or industrial facilities reduce energy consumption, reduce costs and achieve sustainable development by real-time monitoring, analysis and optimization of energy efficiency. The system achieves refined management of energy use by connecting with various energy equipment (such as air conditioners, electrical equipment, lighting systems, fans, etc.). Its core functions include data collection, energy consumption analysis, equipment operation optimization, energy-saving control and decision support. By using advanced sensors, Internet of Things technology, data analysis tools and artificial intelligence algorithms, the intelligent energy management system can provide real-time data feedback and predictive analysis, thereby achieving accurate scheduling and efficient use of energy.

[0003] However, in the energy-saving control of the intelligent energy management system, the intelligent energy management system relies on a large amount of real-time data to make accurate decisions. The data sources include sensors, intelligent metering instruments, and the operating status data of various equipment. In addition, the existing air-conditioning fan equipment usually adopts fixed power control and cannot be adaptively adjusted according to actual needs, resulting in serious energy waste. Therefore, how to pre-adjust the power output of the air-conditioning fan equipment to achieve dynamic power regulation of the air-conditioning fan equipment and avoid energy waste is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides an intelligent energy management system and an energy-saving control method thereof, which can pre-adjust the power output of air-conditioning fan equipment to achieve dynamic power regulation of the air-conditioning fan equipment and avoid energy waste.

[0005] In a first aspect, the present application provides an energy-saving control method for an intelligent energy management system, the control method comprising the following steps: Real-time monitoring of the operation process of air-conditioning fan equipment; Extracting energy efficiency temperature difference data of the air-conditioning fan device from the monitored operating status data, determining an energy efficiency deviation threshold of the energy efficiency temperature difference data, and converting the energy efficiency temperature difference data into an energy efficiency temperature difference sequence of the air-conditioning fan device based on the energy efficiency deviation threshold; Predicting the energy efficiency temperature difference of the air-conditioning fan equipment according to the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and determining the energy efficiency temperature difference stability entropy of the air-conditioning fan equipment operation process through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; The energy efficiency temperature difference prediction data is compensated according to the energy efficiency temperature difference stable entropy, and then the power output of the air-conditioning fan equipment is adjusted based on the compensated energy efficiency temperature difference prediction data.

[0006] In this embodiment, the operation process of the air-conditioning fan equipment is monitored in real time by means of intelligent sensors.

[0007] In this embodiment, extracting the energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operating status data specifically includes: Extracting the air inlet temperature data and the air outlet temperature data of the air conditioning fan equipment from the monitored operating status data; The energy efficiency temperature difference data of the air conditioning fan equipment is determined by the air inlet temperature data and the air outlet temperature data.

[0008] In this embodiment, determining the energy efficiency deviation threshold of the energy efficiency temperature difference data specifically includes: Determining the temperature difference concentration degree of the energy efficiency temperature difference data; An energy efficiency deviation threshold of the energy efficiency temperature difference data is determined according to the temperature difference concentration degree.

[0009] In this embodiment, the energy efficiency temperature difference data is converted into an energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold, and data points in the energy efficiency temperature difference data that are greater than the energy efficiency deviation threshold are eliminated, thereby obtaining the energy efficiency temperature difference sequence of the air-conditioning fan equipment.

[0010] In this embodiment, predicting the energy efficiency temperature difference of the air-conditioning fan device according to the energy efficiency temperature difference sequence is to input the energy efficiency temperature difference sequence into a pre-trained prediction model to predict the energy efficiency temperature difference of the air-conditioning fan device.

[0011] In this embodiment, determining the energy efficiency temperature difference stable entropy of the air-conditioning fan equipment operation process through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data specifically includes: Extracting trend features of the energy efficiency temperature difference sequence to obtain an energy efficiency temperature difference trend factor; Determining prediction deviation data based on the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; determining a prediction deviation entropy based on the prediction deviation data; The energy efficiency temperature difference stable entropy of the air conditioning fan equipment operation process is determined according to the energy efficiency temperature difference trend factor and the predicted deviation entropy.

[0012] In this embodiment, compensating the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stable entropy specifically includes: Determine the compensation coefficient by using the energy efficiency temperature difference stabilization entropy; Each data point in the energy efficiency temperature difference prediction data is compensated according to the compensation coefficient.

[0013] In this embodiment, adjusting the power output of the air-conditioning fan device based on the compensated energy efficiency temperature difference prediction data specifically includes: Determine the energy efficiency temperature difference fluctuation degree according to the energy efficiency temperature difference prediction data after compensation; When the energy efficiency temperature difference fluctuation is greater than a preset fluctuation threshold, reducing the power output of the air conditioning fan equipment; When the energy efficiency temperature difference fluctuation is not greater than a preset fluctuation threshold, the current power output of the air conditioning fan device is maintained.

[0014] In a second aspect, the present application provides an intelligent energy management system for executing an energy-saving control method of an intelligent energy management system, the management system comprising: Real-time monitoring module, used to monitor the operation process of air-conditioning fan equipment in real time; A data processing module, used to extract energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operating status data, determine an energy efficiency deviation threshold of the energy efficiency temperature difference data, and convert the energy efficiency temperature difference data into an energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold; The data processing module is further used to predict the energy efficiency temperature difference of the air-conditioning fan equipment according to the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and determine the energy efficiency temperature difference stability entropy of the air-conditioning fan equipment operation process through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; The power control module is used to compensate the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stable entropy, and then adjust the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: The operation process of the air-conditioning fan equipment is monitored in real time; the energy efficiency temperature difference data of the air-conditioning fan equipment is extracted from the monitored operation status data, the energy efficiency deviation threshold of the energy efficiency temperature difference data is determined, and the energy efficiency temperature difference data is converted into an energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold; the energy efficiency temperature difference of the air-conditioning fan equipment is predicted according to the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and the energy efficiency temperature difference stable entropy of the operation process of the air-conditioning fan equipment is determined through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; the energy efficiency temperature difference prediction data is compensated according to the energy efficiency temperature difference stable entropy, and then the power output of the air-conditioning fan equipment is adjusted based on the compensated energy efficiency temperature difference prediction data.

[0016] It can be seen that in the present application, firstly, by extracting the energy efficiency temperature difference data from the operating status data and filtering the abnormal data through the energy efficiency deviation threshold, it can be ensured that the processed data is more accurate and within the normal working range, and based on this, the energy efficiency temperature difference data is converted into an energy efficiency temperature difference sequence, and the intelligent energy management system can accurately and dynamically adjust the equipment to avoid unnecessary power waste; then, through the prediction of the energy efficiency temperature difference sequence and the calculation of the energy efficiency temperature difference stability entropy, it can provide strong support for the dynamic power regulation of the air-conditioning fan equipment, which is helpful to adjust the power output of the equipment in real time; finally, the energy efficiency temperature difference prediction data is compensated by the energy efficiency temperature difference stability entropy, and the air-conditioning fan power output is pre-adjusted based on the compensated energy efficiency temperature difference prediction data, which can effectively improve the system operation efficiency and reduce energy waste.

[0017] In summary, the technical solution adopted in the present application can pre-adjust the power output of the air-conditioning fan equipment to achieve dynamic power regulation of the air-conditioning fan equipment and avoid energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 is a flow chart of an energy-saving control method of an intelligent energy management system provided by the present application; Figure 2 It is a schematic diagram of a process for extracting energy efficiency temperature difference data of air-conditioning fan equipment according to the present application; Figure 3 It is a flow chart of determining the energy efficiency temperature difference stable entropy of the operation process of the air-conditioning fan equipment provided by the present application; Figure 4 It is a module structure diagram of the intelligent energy management system provided according to this application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0021] The embodiment of the present application provides an intelligent energy management system and an energy-saving control method thereof, the core of which is to monitor the operation process of the air-conditioning fan equipment in real time; extract the energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operation status data, determine the energy efficiency deviation threshold of the energy efficiency temperature difference data, and convert the energy efficiency temperature difference data into the energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold; predict the energy efficiency temperature difference of the air-conditioning fan equipment according to the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and determine the energy efficiency temperature difference stable entropy of the operation process of the air-conditioning fan equipment through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; compensate the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stable entropy, and then adjust the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data. The above scheme can be used to pre-adjust the power output of the air-conditioning fan equipment to realize dynamic power regulation of the air-conditioning fan equipment and avoid energy waste.

[0022] Embodiment 1: In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG. 1 , this figure is an exemplary flow chart of an energy-saving control method of an intelligent energy management system according to this embodiment of the present application, and the control method includes the following steps: In step S1, the operation process of the air-conditioning fan equipment is monitored in real time.

[0023] In specific implementation, the operation process of the air-conditioning fan equipment can be monitored in real time through the intelligent sensor; the intelligent sensor senses the various operating parameters of the air-conditioning fan equipment and transmits the data to the central monitoring system or the intelligent energy management system, thereby obtaining the operating status data. The operating status data includes the inlet and outlet temperature data, humidity data and pressure data in the fan channel during the operation of the air-conditioning fan equipment, thereby realizing real-time grasp and optimal regulation of the equipment operation status. The main intelligent sensor used in this application is the temperature sensor, which can be installed at the air inlet and outlet of the air-conditioning fan equipment to measure the temperature changes of the air in real time.

[0024] In step S2, energy efficiency temperature difference data of the air-conditioning fan equipment is extracted from the monitored operating status data, an energy efficiency deviation threshold of the energy efficiency temperature difference data is determined, and the energy efficiency temperature difference data is converted into an energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold.

[0025] Preferably, in this embodiment, the energy efficiency temperature difference data of the air-conditioning fan equipment is extracted from the monitored operating status data, and the reference Figure 2As shown, this figure is a schematic diagram of the process of extracting energy efficiency temperature difference data of air-conditioning fan equipment in some embodiments of the present application. In this embodiment, the energy efficiency temperature difference data of the air-conditioning fan equipment can be extracted by the following steps: In step S21, the air inlet temperature data and the air outlet temperature data of the air conditioner fan device are extracted from the monitored operating status data; In step S22, energy efficiency temperature difference data of the air conditioning fan equipment is determined by the air inlet temperature data and the air outlet temperature data.

[0026] In specific implementation, first, the inlet temperature data and the outlet temperature data of the air-conditioning fan device can be extracted from the monitored operating status data by traversal extraction. The inlet temperature data is a data set composed of the intake temperature of the air-conditioning fan device, and the outlet temperature data is a data set composed of the outlet temperature of the air-conditioning fan device; then, the energy efficiency temperature difference data of the air-conditioning fan device can be determined through the inlet temperature data and the outlet temperature data. The energy efficiency temperature difference data is a data set composed of the difference between the inlet temperature and the outlet temperature at each time point. The inlet temperature and the outlet temperature at each time point can be subtracted respectively to obtain the energy efficiency temperature difference data of the air-conditioning fan device.

[0027] In this embodiment, the energy efficiency deviation threshold of the energy efficiency temperature difference data may be determined in the following manner, namely: Determining the temperature difference concentration degree of the energy efficiency temperature difference data; An energy efficiency deviation threshold of the energy efficiency temperature difference data is determined according to the temperature difference concentration degree.

[0028] In specific implementation, first, the temperature difference aggregation degree of the energy efficiency temperature difference data can be determined, wherein the temperature difference aggregation degree is an indicator indicating the degree of concentration of the distribution of the energy efficiency temperature difference data, and the mean and kurtosis of the energy efficiency temperature difference data can be calculated, so that the product of the mean and the kurtosis can be used as the temperature difference aggregation degree of the energy efficiency temperature difference data; then, the energy efficiency deviation threshold of the energy efficiency temperature difference data can be determined according to the temperature difference aggregation degree, wherein the energy efficiency deviation threshold is a threshold for eliminating deviation data points in the energy efficiency temperature difference data. In actual implementation, the energy efficiency deviation threshold can be determined by the following formula:

[0029] in, represents the energy efficiency deviation threshold, represents the temperature difference concentration, n represents the total number of data points in the energy efficiency temperature difference data, Represents the i-th data point in the energy efficiency temperature difference data.

[0030] In this embodiment, the energy efficiency temperature difference data is converted into an energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold, and data points in the energy efficiency temperature difference data that are greater than the energy efficiency deviation threshold are eliminated, thereby obtaining the energy efficiency temperature difference sequence of the air-conditioning fan equipment.

[0031] In specific implementation, each data point in the energy efficiency temperature difference data can be compared with the energy efficiency deviation threshold, so as to eliminate the data points that are greater than the energy efficiency deviation threshold, and arrange all the remaining data points in chronological order to obtain the energy efficiency temperature difference sequence of the air-conditioning fan equipment. By eliminating the data points that are greater than the energy efficiency deviation threshold, those data points that deviate from the normal operating range can be eliminated, and valid data points that meet the energy efficiency standards can be retained, so as to more accurately evaluate the operating status and energy efficiency performance of the air-conditioning fan equipment.

[0032] It should be noted that by extracting energy efficiency temperature difference data from the operating status data and filtering abnormal data (such as excessive temperature difference) through the energy efficiency deviation threshold, it can ensure that the processed data is more accurate and in line with the normal working range, and based on this, the energy efficiency temperature difference data is converted into an energy efficiency temperature difference sequence. The intelligent energy management system can accurately and dynamically adjust the equipment to avoid unnecessary power waste. This method can not only improve the operating efficiency and energy-saving effect of the air-conditioning system, but also help extend the life of the equipment, reduce maintenance costs, and enable the system to respond to load changes in real time, thereby achieving more intelligent and energy-saving air-conditioning system management.

[0033] In step S3, the energy efficiency temperature difference of the air-conditioning fan equipment is predicted according to the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and the energy efficiency temperature difference stable entropy of the air-conditioning fan equipment operation process is determined by the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data.

[0034] In this embodiment, predicting the energy efficiency temperature difference of the air-conditioning fan equipment according to the energy efficiency temperature difference sequence is to input the energy efficiency temperature difference sequence into a pre-trained prediction model to predict the energy efficiency temperature difference of the air-conditioning fan equipment, thereby obtaining energy efficiency temperature difference prediction data.

[0035] In the specific implementation, first, a suitable prediction model can be selected. The prediction model selected in this application is ARIMA (autoregressive integrated moving average model). In actual implementation, models such as exponential smoothing, support vector machine, decision tree, long short-term memory network (LSTM) or convolutional neural network can also be selected as prediction models; then, after selecting the model, the historical energy efficiency temperature difference sequence can be used to train the model. The goal of the training process is to adjust the parameters of the model so that it can accurately predict the energy efficiency temperature difference at a certain point in the future when historical data is input; finally, the energy efficiency temperature difference sequence can be input into the pre-trained prediction model to predict the energy efficiency temperature difference of the air-conditioning fan equipment, so that the energy efficiency temperature difference prediction data can be obtained. It should be noted that inputting the energy efficiency temperature difference sequence into the pre-trained prediction model for prediction can not only accurately grasp the energy efficiency change trend of the air-conditioning fan equipment, but also provide accurate data support for dynamically adjusting the power output of the equipment and optimizing the operation strategy.

[0036] Preferably, in this embodiment, the energy efficiency temperature difference stable entropy of the air-conditioning fan equipment operation process is determined by the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data, referring to Figure 3 As shown, this figure is a flow chart of determining the energy efficiency temperature difference stable entropy of the air-conditioning fan equipment operation process in some embodiments of the present application. In this embodiment, determining the energy efficiency temperature difference stable entropy of the air-conditioning fan equipment operation process can be achieved by the following steps: In step S31, trend features are extracted from the energy efficiency temperature difference sequence to obtain an energy efficiency temperature difference trend factor; In step S32, prediction deviation data is determined based on the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; In step S33, a prediction deviation entropy is determined according to the prediction deviation data; In step S34, the energy efficiency temperature difference stable entropy of the air-conditioning fan equipment operation process is determined according to the energy efficiency temperature difference trend factor and the predicted deviation entropy.

[0037] In specific implementation, first, trend characteristics of the energy efficiency temperature difference sequence can be extracted to obtain an energy efficiency temperature difference trend factor, wherein the energy efficiency temperature difference trend factor is a characteristic factor representing the short-term change trend of the temperature difference of the air-conditioning fan equipment, and the linear regression method in the prior art can be used to fit the internal and external temperature difference sequence into a regression line, and then the slope of the regression line is used as the trend characteristic factor; then, the prediction deviation data can be determined based on the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data, that is, an energy efficiency temperature difference value can be selected from the energy efficiency temperature difference sequence, and the energy efficiency temperature difference prediction value corresponding to the energy efficiency temperature difference value can be obtained from the energy efficiency temperature difference prediction data, and the energy efficiency temperature difference value can be subtracted from the energy efficiency temperature difference prediction value, and the difference obtained by the subtraction is used as the prediction deviation value, and the prediction deviation values ​​corresponding to the remaining energy efficiency temperature difference values ​​are further determined, and a set of all prediction deviation values ​​is used as the prediction deviation data.

[0038] In addition, in specific implementation, the prediction deviation entropy can be determined according to the prediction deviation data, wherein the prediction deviation entropy is an indicator of the degree of information confusion in the prediction deviation data, and the Shannon entropy of the prediction deviation data can be calculated, so that the result is used as the prediction deviation entropy; then, the energy efficiency temperature difference stability entropy of the air-conditioning fan equipment operation process can be determined according to the energy efficiency temperature difference trend factor and the prediction deviation entropy, wherein the energy efficiency temperature difference stability entropy is a feature used to evaluate the residual stability between the energy efficiency temperature difference prediction data and the energy efficiency temperature difference sequence. In actual implementation, the energy efficiency temperature difference stability entropy can be determined according to the following formula:

[0039] in, represents the energy efficiency temperature difference stable entropy, represents the energy efficiency temperature difference trend factor, Represents the prediction deviation entropy; it should be noted that the energy efficiency temperature difference stability entropy of the air-conditioning fan equipment is calculated through the energy efficiency temperature difference series and the energy efficiency temperature difference prediction data, which provides a more in-depth operation analysis for the intelligent energy management system, and can help evaluate the energy efficiency fluctuations and prediction errors of the equipment, thereby achieving energy saving and improving system reliability.

[0040] It should be noted that the prediction of energy efficiency temperature difference series and the calculation of energy efficiency temperature difference stable entropy can provide strong support for the dynamic power control of air-conditioning fan equipment, help to adjust the power output of the equipment in real time, avoid energy waste, and improve the stability and efficiency of system operation. In addition, through prediction and analysis, the system can adapt to load changes, extend equipment life, optimize equipment management, and ultimately achieve the goal of energy saving and consumption reduction and improving energy utilization.

[0041] In step S4, the energy efficiency temperature difference prediction data is compensated according to the energy efficiency temperature difference stable entropy, and then the power output of the air-conditioning fan equipment is adjusted based on the compensated energy efficiency temperature difference prediction data.

[0042] In this embodiment, the energy efficiency temperature difference prediction data is compensated according to the energy efficiency temperature difference stable entropy in the following manner, namely: Determine the compensation coefficient by using the energy efficiency temperature difference stabilization entropy; Each data point in the energy efficiency temperature difference prediction data is compensated according to the compensation coefficient.

[0043] In specific implementation, first, the compensation coefficient can be determined by the stable entropy of the energy efficiency temperature difference, where the compensation coefficient represents the degree of compensation for the data points in the energy efficiency temperature difference prediction data. In actual implementation, the value of the compensation coefficient can be ,in, Represents the stable entropy of the energy efficiency temperature difference; then, each data point in the energy efficiency temperature difference prediction data can be compensated according to the compensation coefficient, that is, the compensation coefficient is multiplied by each data point in the energy efficiency temperature difference prediction data, so as to obtain the compensated energy efficiency temperature difference prediction data; it should be noted that, by determining the compensation coefficient through the stable entropy of the energy efficiency temperature difference, and adjusting each data point according to the compensation coefficient, the deviation in the energy efficiency temperature difference prediction data can be effectively eliminated, ensuring that the air-conditioning fan equipment can perform power adjustment according to more accurate energy efficiency temperature difference prediction data during operation.

[0044] In this embodiment, the power output of the air-conditioning fan device is adjusted based on the compensated energy efficiency temperature difference prediction data in the following manner, namely: Determine the energy efficiency temperature difference fluctuation degree according to the energy efficiency temperature difference prediction data after compensation; When the energy efficiency temperature difference fluctuation is greater than a preset fluctuation threshold, reducing the power output of the air conditioning fan equipment; When the energy efficiency temperature difference fluctuation is not greater than a preset fluctuation threshold, the current power output of the air conditioning fan device is maintained.

[0045] In specific implementation, the energy efficiency temperature difference fluctuation degree can be determined according to the compensated energy efficiency temperature difference prediction data, wherein the energy efficiency temperature difference fluctuation degree represents the degree of fluctuation of the data points in the energy efficiency temperature difference prediction data, and the standard deviation of all data points in the compensated energy efficiency temperature difference prediction data can be used as the energy efficiency temperature difference fluctuation degree; then, the energy efficiency temperature difference fluctuation degree can be compared with the preset fluctuation threshold value, wherein the fluctuation threshold value can be set according to historical experiments and data analysis, which will not be described in detail here; when the energy efficiency temperature difference fluctuation degree is greater than the preset fluctuation threshold value, it means that the air-conditioning fan equipment may have load fluctuations and unstable energy efficiency, and the air-conditioning system may be When experiencing uneven load or performance degradation, the power output of the air-conditioning fan equipment can be reduced to alleviate system load fluctuations and prevent excessive energy consumption. In actual implementation, a dynamic adjustment algorithm, such as PID control or fuzzy control, can be used to dynamically adjust the fan power output according to the energy efficiency temperature difference fluctuation, so as to avoid energy waste when the load fluctuates greatly. When the energy efficiency temperature difference fluctuation is not greater than the preset fluctuation threshold, it means that the equipment is running stably and the load changes little. At this time, there is no need to make large adjustments, and the current power output of the fan can be maintained to avoid excessive adjustment and unnecessary energy efficiency loss, that is, to maintain the current power output of the air-conditioning fan equipment.

[0046] It should be noted that by compensating the energy efficiency temperature difference prediction data through the energy efficiency temperature difference stable entropy, and pre-adjusting the air conditioner fan power output based on the compensated energy efficiency temperature difference prediction data, the system operation efficiency can be effectively improved, energy waste can be reduced, and the equipment can always be in an efficient and stable working state. This intelligent control method is more accurate and flexible than traditional passive control, and can achieve more efficient energy management goals.

[0047] It can be seen that in the present application, firstly, by extracting the energy efficiency temperature difference data from the operating status data and filtering the abnormal data through the energy efficiency deviation threshold, it can be ensured that the processed data is more accurate and within the normal working range, and based on this, the energy efficiency temperature difference data is converted into an energy efficiency temperature difference sequence, and the intelligent energy management system can accurately and dynamically adjust the equipment to avoid unnecessary power waste; then, through the prediction of the energy efficiency temperature difference sequence and the calculation of the energy efficiency temperature difference stability entropy, it can provide strong support for the dynamic power regulation of the air-conditioning fan equipment, which is helpful to adjust the power output of the equipment in real time; finally, the energy efficiency temperature difference prediction data is compensated by the energy efficiency temperature difference stability entropy, and the air-conditioning fan power output is pre-adjusted based on the compensated energy efficiency temperature difference prediction data, which can effectively improve the system operation efficiency and reduce energy waste.

[0048] In summary, the technical solution adopted in the present application can pre-adjust the power output of the air-conditioning fan equipment to achieve dynamic power regulation of the air-conditioning fan equipment and avoid energy waste.

[0049] Embodiment 2: This application provides an intelligent energy management system, referring to Figure 4 As shown, this figure is a schematic diagram of an intelligent energy management system according to this embodiment of the present application, and the management system includes: The real-time monitoring module 100 is used to monitor the operation process of the air-conditioning fan equipment in real time; The data processing module 200 is used to extract the energy efficiency temperature difference data of the air-conditioning fan device from the monitored operating status data, determine the energy efficiency deviation threshold of the energy efficiency temperature difference data, and convert the energy efficiency temperature difference data into an energy efficiency temperature difference sequence of the air-conditioning fan device based on the energy efficiency deviation threshold; The data processing module 200 is further used to predict the energy efficiency temperature difference of the air-conditioning fan equipment according to the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and determine the energy efficiency temperature difference stability entropy of the air-conditioning fan equipment operation process through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; The power control module 300 is used to compensate the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stable entropy, and then adjust the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data.

[0050] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0051] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0052] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. An energy-saving control method for an intelligent energy management system, characterized in that: The control method comprises the following steps: Real-time monitoring of the operation process of air-conditioning fan equipment; Extracting energy efficiency temperature difference data of the air-conditioning fan device from the monitored operating status data, determining an energy efficiency deviation threshold of the energy efficiency temperature difference data, and converting the energy efficiency temperature difference data into an energy efficiency temperature difference sequence of the air-conditioning fan device based on the energy efficiency deviation threshold; Predicting the energy efficiency temperature difference of the air-conditioning fan equipment according to the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and determining the energy efficiency temperature difference stability entropy of the air-conditioning fan equipment operation process through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; The energy efficiency temperature difference prediction data is compensated according to the energy efficiency temperature difference stable entropy, and then the power output of the air-conditioning fan equipment is adjusted based on the compensated energy efficiency temperature difference prediction data.

2. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that: The operation process of air-conditioning fan equipment is monitored in real time through intelligent sensors.

3. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that: The energy efficiency temperature difference data of the air-conditioning fan equipment extracted from the monitored operating status data specifically include: Extracting the air inlet temperature data and the air outlet temperature data of the air conditioning fan equipment from the monitored operating status data; The energy efficiency temperature difference data of the air conditioning fan equipment is determined by the air inlet temperature data and the air outlet temperature data.

4. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that: Determining the energy efficiency deviation threshold of the energy efficiency temperature difference data specifically includes: Determining the temperature difference concentration degree of the energy efficiency temperature difference data; An energy efficiency deviation threshold of the energy efficiency temperature difference data is determined according to the temperature difference concentration degree.

5. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that: Converting the energy efficiency temperature difference data into an energy efficiency temperature difference sequence of the air-conditioning fan device based on the energy efficiency deviation threshold is to remove data points in the energy efficiency temperature difference data that are greater than the energy efficiency deviation threshold, thereby obtaining the energy efficiency temperature difference sequence of the air-conditioning fan device.

6. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that: Predicting the energy efficiency temperature difference of the air-conditioning fan device according to the energy efficiency temperature difference sequence is to input the energy efficiency temperature difference sequence into a pre-trained prediction model to predict the energy efficiency temperature difference of the air-conditioning fan device.

7. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that: Determining the energy efficiency temperature difference stable entropy of the air-conditioning fan equipment operation process through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data specifically includes: Extracting trend features of the energy efficiency temperature difference sequence to obtain an energy efficiency temperature difference trend factor; Determining prediction deviation data based on the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; determining a prediction deviation entropy based on the prediction deviation data; The energy efficiency temperature difference stable entropy of the air conditioning fan equipment operation process is determined according to the energy efficiency temperature difference trend factor and the predicted deviation entropy.

8. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that: Compensating the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stable entropy specifically includes: Determine the compensation coefficient by using the energy efficiency temperature difference stabilization entropy; Each data point in the energy efficiency temperature difference prediction data is compensated according to the compensation coefficient.

9. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that: The power output of the air-conditioning fan equipment is adjusted based on the compensated energy efficiency temperature difference prediction data, specifically including: Determine the energy efficiency temperature difference fluctuation degree according to the energy efficiency temperature difference prediction data after compensation; When the energy efficiency temperature difference fluctuation is greater than a preset fluctuation threshold, reducing the power output of the air conditioning fan equipment; When the energy efficiency temperature difference fluctuation is not greater than a preset fluctuation threshold, the current power output of the air conditioning fan device is maintained.

10. An intelligent energy management system, used to execute the energy-saving control method of an intelligent energy management system according to any one of claims 1 to 9, characterized in that: The management system comprises: Real-time monitoring module, used to monitor the operation process of air-conditioning fan equipment in real time; A data processing module, used to extract energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operating status data, determine an energy efficiency deviation threshold of the energy efficiency temperature difference data, and convert the energy efficiency temperature difference data into an energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold; The data processing module is further used to predict the energy efficiency temperature difference of the air-conditioning fan equipment according to the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and determine the energy efficiency temperature difference stability entropy of the air-conditioning fan equipment operation process through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; The power control module is used to compensate the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stable entropy, and then adjust the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data.

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