An intelligent energy management system and its energy-saving control method

Abstract

The present application provides an intelligent energy management system and its energy-saving control method, which relates to the technical field of energy-saving control, and monitors the operation process of air-conditioning fan equipment in real time; extracts the energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operation state data, determines the energy efficiency deviation threshold of the energy efficiency temperature difference data, and converts the energy efficiency temperature difference data into an energy efficiency temperature difference sequence based on the energy efficiency deviation threshold; predicts 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 determines the energy efficiency temperature difference stable entropy through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; compensates the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stable entropy, and then adjusts the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data. The present application can pre-adjust the power output of the air-conditioning fan equipment to achieve dynamic power control of the air-conditioning fan equipment and avoid energy waste.

Description

Technical Field

[0001] This application relates to the field of energy-saving control technologies. More specifically, this application relates to an intelligent energy management system and its energy-saving control method. Background Art

[0002] An intelligent energy management system is an integrated solution designed to help enterprises, buildings, or industrial facilities reduce energy consumption, cut costs, and achieve sustainable development by monitoring, analyzing, and optimizing the energy usage efficiency in real time. The system realizes refined management of energy usage by connecting to various energy-consuming devices (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-making support, etc. By using advanced sensors, Internet of Things technologies, data analysis tools, and artificial intelligence algorithms, the intelligent energy management system can provide real-time data feedback and predictive analysis, thus achieving precise scheduling and efficient utilization of energy.

[0003] However, in the energy-saving control of an intelligent energy management system, the intelligent energy management system relies on a large amount of real-time data to make precise decisions. The data sources include sensors, intelligent metering instruments, and the operation status data of various devices. And 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 air-conditioning fan equipment to achieve dynamic power regulation of air-conditioning fan equipment and avoid energy waste is a difficult problem faced by the industry. Summary of the Invention

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

[0005] In a first aspect, this application provides an energy-saving control method for an intelligent energy management system. The control method includes the following steps:

[0006] Monitor the operation process of the air-conditioning fan equipment in real time;

[0007] 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 an energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold;

[0008] 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;

[0009] Compensate the predicted energy efficiency temperature difference data according to the stable entropy of the energy efficiency temperature difference, and then adjust the power output of the air-conditioning fan equipment based on the compensated predicted energy efficiency temperature difference data.

[0010] In this embodiment, the operation process of the air-conditioning fan equipment is monitored in real time through an intelligent sensor.

[0011] In this embodiment, extracting the energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operation status data specifically includes:

[0012] Extract the inlet air temperature data and the outlet air temperature data of the air-conditioning fan equipment from the monitored operation status data;

[0013] Determine the energy efficiency temperature difference data of the air-conditioning fan equipment through the inlet air temperature data and the outlet air temperature data.

[0014] In this embodiment, determining the energy efficiency deviation threshold of the energy efficiency temperature difference data specifically includes:

[0015] Determine the temperature difference concentration degree of the energy efficiency temperature difference data;

[0016] Determine the energy efficiency deviation threshold of the energy efficiency temperature difference data according to the temperature difference concentration degree.

[0017] In this embodiment, converting 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 is to eliminate the data points in the energy efficiency temperature difference data that are greater than the energy efficiency deviation threshold, and then obtain the energy efficiency temperature difference sequence of the air-conditioning fan equipment.

[0018] In this embodiment, predicting the energy efficiency temperature difference of the air-conditioning fan equipment based on 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.

[0019] In this embodiment, determining the stable entropy of the energy efficiency temperature difference during the operation process of the air-conditioning fan equipment through the energy efficiency temperature difference sequence and the predicted energy efficiency temperature difference data specifically includes:

[0020] Extract the trend characteristics of the energy efficiency temperature difference sequence to obtain the energy efficiency temperature difference trend factor;

[0021] Determine the prediction deviation data based on the energy efficiency temperature difference sequence and the predicted energy efficiency temperature difference data;

[0022] Determine the prediction deviation entropy according to the prediction deviation data;

[0023] Determine the stable entropy of the energy efficiency temperature difference during the operation of the air-conditioning fan equipment based on the energy efficiency temperature difference trend factor and the prediction deviation entropy.

[0024] In this embodiment, compensating the energy efficiency temperature difference prediction data according to the stable entropy of the energy efficiency temperature difference specifically includes:

[0025] Determine the compensation coefficient through the stable entropy of the energy efficiency temperature difference;

[0026] Compensate each data point in the energy efficiency temperature difference prediction data according to the compensation coefficient.

[0027] In this embodiment, adjusting the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data specifically includes:

[0028] Determine the fluctuation degree of the energy efficiency temperature difference according to the compensated energy efficiency temperature difference prediction data;

[0029] When the fluctuation degree of the energy efficiency temperature difference is greater than the preset fluctuation threshold, reduce the power output of the air-conditioning fan equipment;

[0030] When the fluctuation degree of the energy efficiency temperature difference is not greater than the preset fluctuation threshold, maintain the current power output of the air-conditioning fan equipment.

[0031] In a second aspect, the present application provides an intelligent energy management system for implementing an energy-saving control method of an intelligent energy management system. The management system includes:

[0032] A real-time monitoring module for real-time monitoring of the operation process of the air-conditioning fan equipment;

[0033] A data processing module for extracting the energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operation state data, determining the 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 equipment based on the energy efficiency deviation threshold;

[0034] The data processing module is further configured to predict the energy efficiency temperature difference of the air-conditioning fan equipment based on the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and determine the stable entropy of the energy efficiency temperature difference during the operation of the air-conditioning fan equipment through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data;

[0035] A power control module for compensating the energy efficiency temperature difference prediction data according to the stable entropy of the energy efficiency temperature difference, and then adjusting the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data.

[0036] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects:

[0037] By monitoring the operation process of the air-conditioning fan equipment in real time; extracting the energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operation status data, determining the 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 equipment 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, determining the energy efficiency temperature difference stable entropy in the operation process of the air-conditioning fan equipment through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; compensating the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stable entropy, and then adjusting the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data.

[0038] It can be seen that in this application, first of all, by extracting the energy efficiency temperature difference data from the operation status data and filtering abnormal data through the energy efficiency deviation threshold, it can ensure 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, so that the intelligent energy management system can perform precise dynamic adjustment on the equipment and avoid unnecessary power waste; then, through the prediction of the energy efficiency temperature difference sequence and the calculation of the energy efficiency temperature difference stable entropy, it can provide strong support for the dynamic power regulation of the air-conditioning fan equipment and help to adjust the power output of the equipment in real time; finally, by compensating the energy efficiency temperature difference prediction data with the energy efficiency temperature difference stable entropy and pre-adjusting the power output of the air-conditioning fan based on the compensated energy efficiency temperature difference prediction data, it can effectively improve the system operation efficiency and reduce energy waste.

[0039] In summary, the technical solution adopted in this application can pre-adjust the power output of the air-conditioning fan equipment to achieve the dynamic power regulation of the air-conditioning fan equipment and avoid energy waste. Brief Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a flowchart of the energy-saving control method for the intelligent energy management system provided by the present application;

[0042] Figure 2 It is a schematic flow diagram for extracting the energy efficiency temperature difference data of the air-conditioning fan equipment provided by the present application;

[0043] Figure 3It is a schematic flowchart for determining the stable entropy of the energy efficiency temperature difference in the operation process of an air-conditioning fan device according to the present application;

[0044] Figure 4 It is a module structure diagram of an intelligent energy management system according to the present application. Specific embodiments

[0045] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present application.

[0046] The embodiment of the present application provides an intelligent energy management system and its energy-saving control method. The core is to monitor the operation process of the air-conditioning fan device in real time; extract the energy efficiency temperature difference data of the air-conditioning fan device 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 an energy efficiency temperature difference sequence of the air-conditioning fan device based on the energy efficiency deviation threshold; predict the energy efficiency temperature difference of the air-conditioning fan device according to the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, determine the stable entropy of the energy efficiency temperature difference in the operation process of the air-conditioning fan device 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 stable entropy of the energy efficiency temperature difference, and then adjust the power output of the air-conditioning fan device based on the compensated energy efficiency temperature difference prediction data. By adopting the above solution, the power output of the air-conditioning fan device can be pre-adjusted to achieve dynamic power control of the air-conditioning fan device and avoid energy waste.

[0047] Embodiment 1. To better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners. Refer to Figure 1 As shown, this figure is an exemplary flowchart of an energy-saving control method for an intelligent energy management system according to this embodiment of the present application. The control method includes the following steps:

[0048] In step S1, the operation process of the air-conditioning fan device is monitored in real time.

[0049] In specific implementation, the operation process of the air-conditioning fan equipment can be monitored in real time through intelligent sensors; the intelligent sensors sense various operation parameters of the air-conditioning fan equipment and transmit the data to the central monitoring system or the intelligent energy management system, and then the operation status data can be obtained. The operation status data includes the temperature data of the air inlet and outlet during the operation of the air-conditioning fan equipment, the humidity data, and the pressure data in the fan channel, so as to realize the real-time grasp and optimized control of the equipment operation status. The main intelligent sensor used in this application is a temperature sensor, which can be installed at the air inlet and outlet of the air-conditioning fan equipment to measure the temperature change of the air in real time.

[0050] In step S2, 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 an energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold.

[0051] Preferably, in this embodiment, extract the energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operation status data, refer to Figure 2 As shown in the figure, which is a schematic flow chart for extracting the energy efficiency temperature difference data of the air-conditioning fan equipment in some embodiments of this application. The extraction of the energy efficiency temperature difference data of the air-conditioning fan equipment in this embodiment can be realized by the following steps:

[0052] In step S21, extract the air inlet temperature data and the air outlet temperature data of the air-conditioning fan equipment from the monitored operation status data;

[0053] In step S22, determine the energy efficiency temperature difference data of the air-conditioning fan equipment through the air inlet temperature data and the air outlet temperature data.

[0054] In specific implementation, first, the air inlet temperature data and the air outlet temperature data of the air-conditioning fan equipment can be extracted from the monitored operation status data by means of traversal extraction. The air inlet temperature data is a data set composed of the intake air temperatures of the air-conditioning fan equipment, and the air outlet temperature data is a data set composed of the outlet air temperatures of the air-conditioning fan equipment; then, the energy efficiency temperature difference data of the air-conditioning fan equipment can be determined through the air inlet temperature data and the air outlet temperature data. The energy efficiency temperature difference data is a data set composed of the differences between the air inlet temperature and the air outlet temperature at each time point. The differences between the air inlet temperature and the air outlet temperature at each time point can be calculated respectively, so as to obtain the energy efficiency temperature difference data of the air-conditioning fan equipment.

[0055] In this embodiment, the energy efficiency deviation threshold of the energy efficiency temperature difference data can be determined specifically by the following method, that is:

[0056] Determine the temperature difference aggregation degree of the energy efficiency temperature difference data;

[0057] Determine the energy efficiency deviation threshold of the energy efficiency temperature difference data according to the temperature difference concentration degree.

[0058] In specific implementation, first, the temperature difference concentration degree of the energy efficiency temperature difference data can be determined. Here, the temperature difference concentration degree is an index indicating the degree of concentration of the distribution of the energy efficiency temperature difference data. The mean value and kurtosis of the energy efficiency temperature difference data can be calculated, and thus the product of the mean value and the kurtosis can be used as the temperature difference concentration 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 concentration degree. Here, the energy efficiency deviation threshold is a threshold for excluding deviated data points in the energy efficiency temperature difference data. In actual implementation, the energy efficiency deviation threshold can be determined by the following formula:

[0059]

[0060] Wherein, represents the energy efficiency deviation threshold, represents the temperature difference concentration degree, 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.

[0061] In this embodiment, 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 exclude the data points in the energy efficiency temperature difference data that are greater than the energy efficiency deviation threshold, and then obtain the energy efficiency temperature difference sequence of the air-conditioning fan device.

[0062] In specific implementation, each data point in the energy efficiency temperature difference data can be compared with the energy efficiency deviation threshold respectively, so as to exclude the data points that are greater than the energy efficiency deviation threshold, and arrange all the remaining data points in chronological order, then the energy efficiency temperature difference sequence of the air-conditioning fan device can be obtained. By excluding the data points that are greater than the energy efficiency deviation threshold, those data points that deviate from the normal operating range can be excluded, and the valid data points that meet the energy efficiency standard can be retained, so as to more accurately evaluate the operating state and energy efficiency performance of the air-conditioning fan device.

[0063] It should be noted that by extracting the energy efficiency temperature difference data from the operating state 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 within 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 perform precise dynamic adjustment on the device, avoiding 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 device life, reduce the maintenance cost, and enable the system to respond to load changes in real time, thereby realizing more intelligent and energy-saving management of the air-conditioning system.

[0064] In step S3, the energy efficiency temperature difference of the air-conditioning fan equipment is predicted based on the energy efficiency temperature difference sequence to obtain energy efficiency temperature difference prediction data, and the energy efficiency temperature difference stable entropy during the operation of the air-conditioning fan equipment is determined through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data.

[0065] In this embodiment, predicting the energy efficiency temperature difference of the air-conditioning fan equipment based on 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.

[0066] Specifically, 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 the prediction model. Then, after the model is selected, 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 future time point when inputting historical data. 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, thereby obtaining energy efficiency temperature difference prediction data. 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.

[0067] Preferably, in this embodiment, the energy efficiency temperature difference stable entropy during the operation of the air-conditioning fan equipment is determined through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data. Refer to Figure 3 As shown, this figure is a schematic flowchart of determining the energy efficiency temperature difference stable entropy during the operation of the air-conditioning fan equipment in some embodiments of this application. The energy efficiency temperature difference stable entropy during the operation of the air-conditioning fan equipment in this embodiment can be implemented by the following steps:

[0068] In step S31, the trend characteristics of the energy efficiency temperature difference sequence are extracted to obtain the energy efficiency temperature difference trend factor;

[0069] In step S32, prediction deviation data is determined based on the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data;

[0070] In step S33, the prediction deviation entropy is determined according to the prediction deviation data;

[0071] In step S34, the energy efficiency temperature difference stable entropy during the operation of the air-conditioning fan equipment is determined based on the energy efficiency temperature difference trend factor and the prediction deviation entropy.

[0072] In specific implementation, first, the trend features of the energy efficiency temperature difference sequence can be extracted to obtain the energy efficiency temperature difference trend factor. Here, 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. The linear regression method in the existing technology can be used to fit the internal and external temperature difference sequence into a regression line, and then the slope of this regression line is used as the trend feature 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 corresponding energy efficiency temperature difference prediction value can be obtained from the energy efficiency temperature difference prediction data. The difference between this energy efficiency temperature difference value and this energy efficiency temperature difference prediction value is calculated, and the obtained difference is used as the prediction deviation value. The prediction deviation values corresponding to the remaining energy efficiency temperature difference values are continuously determined, and the set composed of all prediction deviation values is used as the prediction deviation data.

[0073] In addition, in specific implementation, the prediction deviation entropy can be determined according to the prediction deviation data. Here, the prediction deviation entropy is an index representing the degree of information chaos in the prediction deviation data. The Shannon entropy of this prediction deviation data can be calculated, and the result is used as the prediction deviation entropy. Then, the energy efficiency temperature difference stability entropy of the operation process of the air-conditioning fan equipment can be determined based on the energy efficiency temperature difference trend factor and the prediction deviation entropy. Here, the energy efficiency temperature difference stability entropy is a characteristic for evaluating 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:

[0074]

[0075] Wherein, represents the energy efficiency temperature difference stability entropy, represents the energy efficiency temperature difference trend factor, represents the prediction deviation entropy; it should be noted that by calculating the energy efficiency temperature difference stability entropy of the air-conditioning fan equipment through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data, more in-depth operation analysis can be provided for the intelligent energy management system, which can help evaluate the energy efficiency fluctuation and prediction error of the equipment, so as to achieve energy conservation and improve the system reliability.

[0076] It should be noted that through the prediction of the energy efficiency temperature difference sequence and the calculation of the energy efficiency temperature difference stability entropy, strong support can be provided for the power dynamic regulation of the air-conditioning fan equipment, which helps to adjust the power output of the equipment in real time, avoid energy waste, and can also improve the stability and efficiency of the system operation. In addition, through prediction and analysis, the system can adapt to load changes, extend the equipment life, optimize equipment management, and finally achieve the goal of energy conservation and consumption reduction and improvement of energy utilization rate.

[0077] In step S4, the energy efficiency temperature difference prediction data is compensated according to the stable entropy of the energy efficiency temperature difference, and then the power output of the air-conditioning fan equipment is adjusted based on the compensated energy efficiency temperature difference prediction data.

[0078] In this embodiment, compensating the energy efficiency temperature difference prediction data according to the stable entropy of the energy efficiency temperature difference can be specifically implemented in the following manner, that is:

[0079] Determine the compensation coefficient through the stable entropy of the energy efficiency temperature difference;

[0080] Compensate each data point in the energy efficiency temperature difference prediction data according to the compensation coefficient.

[0081] Specifically, first, the compensation coefficient can be determined through the stable entropy of the energy efficiency temperature difference. Here, the compensation coefficient represents the compensation degree for the data points in the energy efficiency temperature difference prediction data. In actual implementation, the value of this compensation coefficient can be , where 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, multiplying the compensation coefficient by each data point in the energy efficiency temperature difference prediction data respectively, 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 this 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 based on more accurate energy efficiency temperature difference prediction data during operation.

[0082] In this embodiment, adjusting the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data can be specifically implemented in the following manner, that is:

[0083] Determine the energy efficiency temperature difference fluctuation degree according to the compensated energy efficiency temperature difference prediction data;

[0084] When the energy efficiency temperature difference fluctuation degree is greater than the preset fluctuation threshold, reduce the power output of the air-conditioning fan equipment;

[0085] When the energy efficiency temperature difference fluctuation degree is not greater than the preset fluctuation threshold, maintain the current power output of the air-conditioning fan equipment.

[0086] In specific implementation, the energy efficiency temperature difference fluctuation degree can be determined based on the compensated predicted data of the energy efficiency temperature difference. Herein, the energy efficiency temperature difference fluctuation degree represents the fluctuation degree of the data points in the predicted data of the energy efficiency temperature difference, and the standard deviation of all data points in the compensated predicted data of the energy efficiency temperature difference can be used as the energy efficiency temperature difference fluctuation degree. Then, the energy efficiency temperature difference fluctuation degree can be compared with a preset fluctuation threshold, where the fluctuation threshold can be set based on historical experiments and data analysis and will not be elaborated here. When the energy efficiency temperature difference fluctuation degree is greater than the preset fluctuation threshold, it indicates that the air-conditioning fan equipment may have load fluctuations and unstable energy efficiency, and the air-conditioning system may be experiencing uneven load or performance degradation. The power output of the air-conditioning fan equipment can be reduced to alleviate the system load fluctuation and prevent excessive energy consumption. In actual implementation, a dynamic adjustment algorithm such as PID control or fuzzy control can be adopted to dynamically adjust the fan power output according to the energy efficiency temperature difference fluctuation degree to avoid energy waste in the case of large load fluctuations. When the energy efficiency temperature difference fluctuation degree is not greater than the preset fluctuation threshold, it indicates that the equipment is operating stably and the load change is small. At this time, no major adjustment is required, 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.

[0087] It should be noted that compensating the predicted data of the energy efficiency temperature difference through the energy efficiency temperature difference stability entropy and pre-adjusting the power output of the air-conditioning fan based on the compensated predicted data of the energy efficiency temperature difference can effectively improve the system operation efficiency, reduce energy waste, and ensure that the equipment is always in a highly 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.

[0088] Thus, in this application, firstly, by extracting the energy efficiency temperature difference data from the operation state data and filtering 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. 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 perform precise dynamic adjustment on 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 control of the air-conditioning fan equipment and help to adjust the power output of the equipment in real time. Finally, compensating the predicted data of the energy efficiency temperature difference through the energy efficiency temperature difference stability entropy and pre-adjusting the power output of the air-conditioning fan based on the compensated predicted data of the energy efficiency temperature difference can effectively improve the system operation efficiency and reduce energy waste.

[0089] In summary, the technical solution adopted in this application can pre-adjust the power output of the air-conditioning fan equipment to achieve dynamic power control of the air-conditioning fan equipment and avoid energy waste.

[0090] Embodiment 2. The present application provides an intelligent energy management system. Refer to Figure 4 As shown, this figure is a schematic diagram of the intelligent energy management system according to this embodiment of the present application. The management system includes:

[0091] A real-time monitoring module 100, configured to perform real-time monitoring on the operation process of the air-conditioning fan equipment;

[0092] A data processing module 200, configured to 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 an energy efficiency temperature difference sequence of the air-conditioning fan equipment based on the energy efficiency deviation threshold;

[0093] The data processing module 200 is further configured 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 operation process of the air-conditioning fan equipment through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data;

[0094] A power control module 300, configured to compensate the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stability entropy, and then adjust the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0096] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disc memories, magnetic tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0097] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent in such a process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the element.

Claims

1. An energy-saving control method for an intelligent energy management system, characterized in that, The control method includes the following steps: 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 an 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 based on 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 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 stability entropy, and then adjust the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data; Specifically, extracting the energy efficiency temperature difference data of the air-conditioning fan equipment from the monitored operation status data includes: Extract the inlet air temperature data and the outlet air temperature data of the air-conditioning fan equipment from the monitored operation status data; Determine the energy efficiency temperature difference data of the air-conditioning fan equipment through the inlet air temperature data and the outlet air temperature data; Among them, specifically determining the energy efficiency temperature difference stability 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 includes: Extract the trend characteristics of the energy efficiency temperature difference sequence to obtain an energy efficiency temperature difference trend factor; Determine the prediction deviation data based on the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; Determine the prediction deviation entropy according to the prediction deviation data; Determine the energy efficiency temperature difference stability entropy of the operation process of the air-conditioning fan equipment based on the energy efficiency temperature difference trend factor and the prediction deviation entropy; Specifically, compensating the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stability entropy includes: Determine a compensation coefficient through the energy efficiency temperature difference stability entropy; Compensate each data point in the energy efficiency temperature difference prediction data according to the compensation coefficient.

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

3. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that, Specifically, determining the energy efficiency deviation threshold of the energy efficiency temperature difference data includes: Determine the temperature difference aggregation degree of the energy efficiency temperature difference data; Determine the energy efficiency deviation threshold of the energy efficiency temperature difference data according to the temperature difference aggregation degree.

4. 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 equipment based on the energy efficiency deviation threshold is to eliminate the data points in the energy efficiency temperature difference data that are greater than the energy efficiency deviation threshold, and then obtain the energy efficiency temperature difference sequence of the air-conditioning fan equipment.

5. 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 equipment based on 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.

6. The energy-saving control method of an intelligent energy management system according to claim 1, characterized in that, Specifically, adjusting the power output of the air-conditioning fan equipment based on the compensated energy efficiency temperature difference prediction data includes: Determine the energy efficiency temperature difference fluctuation degree according to the compensated energy efficiency temperature difference prediction data; When the energy efficiency temperature difference fluctuation degree is greater than a preset fluctuation threshold, reduce the power output of the air-conditioning fan equipment; When the energy efficiency temperature difference fluctuation degree is not greater than the preset fluctuation threshold, maintain the current power output of the air-conditioning fan equipment.

7. An intelligent energy management system for implementing the energy-saving control method of an intelligent energy management system according to any one of claims 1 to 6, characterized in that, The management system includes: A real-time monitoring module for real-time monitoring of the operation process of an air-conditioning fan device; A data processing module for extracting the energy efficiency temperature difference data of the air-conditioning fan device from the monitored operation status data, determining the 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; The data processing module is further configured to predict the energy efficiency temperature difference of the air-conditioning fan device according to the energy efficiency temperature difference sequence, obtain the energy efficiency temperature difference prediction data, and determine the energy efficiency temperature difference stability entropy of the operation process of the air-conditioning fan device through the energy efficiency temperature difference sequence and the energy efficiency temperature difference prediction data; A power control module for compensating the energy efficiency temperature difference prediction data according to the energy efficiency temperature difference stability entropy, and then adjusting the power output of the air-conditioning fan device based on the compensated energy efficiency temperature difference prediction data.

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