Energy efficiency management system based on power utilization of air conditioner

Abstract

The invention relates to the technical field of air conditioners, and particularly discloses an energy efficiency management system based on air conditioner power utilization, which comprises an air conditioner group, a pipeline and a sub air conditioner unit, the sub-air-conditioning units are uniformly controlled through a platform controller; the controller is in telecommunication connection with the air conditioner group; the pipeline is formed by connecting air pipes through flanges; an air diffuser is arranged at the tail end of each branch; the controller carries out energy efficiency pre-adjustment on the air conditioner set by predicting the future human traffic of the indoor place; after the air conditioning unit carries out energy efficiency pre-adjustment, the predicted future people flow is corrected by monitoring the people flow in the indoor place in real time; and the controller carries out matching adjustment on the corrected human traffic and the energy efficiency of the air conditioning unit. A central air-conditioning system of a shopping mall can accurately match temperature requirements in different seasons, different weathers, different dates, different time periods and different visitor flows, and the dual purposes of saving energy and improving customer experience are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning, and in particular to an energy efficiency management system based on air conditioning electricity consumption. Background Art

[0002] In today's ever-changing era, with the rapid advancement of technology and the significant improvement of people's quality of life, air conditioning has quietly become an indispensable part of daily life. Whether in hot summer or cold winter, air conditioning creates a pleasant indoor environment with its excellent heating and cooling functions, greatly improving comfort. Air conditioning provides us with the possibility of adjusting indoor temperature, but the realization of this process is highly dependent on the user's personal judgment and operation. Many times, we may set a certain temperature value based on our subjective feelings or habitually, but such a setting may not be able to accurately meet our actual body comfort needs. The high energy consumption of air conditioning is also worthy of our deep consideration. While pursuing indoor comfort, the energy consumption and environmental impact brought by the operation of air conditioning are often ignored.

[0003] A method for automatically adjusting the temperature of an air conditioner is disclosed in the patent "Method and air conditioner for automatically adjusting the temperature of an air conditioner based on ambient temperature" (publication number CN115789922A, hereinafter referred to as prior art 1). Prior art 1 is a method and air conditioner for automatically adjusting the temperature of an air conditioner based on ambient temperature. The method includes obtaining the current ambient temperature, querying in a pre-built local experience database whether there is a corresponding historical adjustment record for the current ambient temperature, and if so, adjusting the air conditioner temperature according to the historical adjustment record; if not, inputting the current ambient temperature into a pre-trained temperature adjustment model, and adjusting the air conditioner temperature according to the output result of the temperature adjustment model. The temperature adjustment model is trained based on historical adjustment records. This method can perform customized temperature adjustment according to the different usage habits of different users.

[0004] In the current field of intelligent temperature control technology, although the prior art 1 has realized the function of automatically adjusting the indoor temperature based on ambient temperature monitoring and historical adjustment records, its performance in practical applications is far from ideal. This is mainly because the temperature adjustment method and influencing parameters relied on by the technology are relatively single and limited, and it is impossible to fully and accurately capture and respond to the complex changes in the indoor environment, resulting in insufficient accuracy of temperature adjustment and a significant increase in energy consumption. Specifically, in the temperature adjustment process, the prior art 1 often only relies on the real-time reading of the ambient temperature or the historical adjustment data as the basis for decision-making. This simple adjustment logic ignores the multiple influencing factors of the indoor environment, such as changes in the number of people indoors, changes in weather, and different seasons, which will affect the changes in indoor temperature to varying degrees. Therefore, it is difficult to achieve accurate temperature control by relying solely on ambient temperature or historical records for temperature adjustment, and there may even be problems such as excessive temperature fluctuations and adjustment lags, which in turn affect the comfort and health of the occupants. Due to the inaccuracy of temperature adjustment, the air-conditioning system often needs to be started and stopped frequently in an attempt to achieve the preset temperature target. This frequent start-stop operation not only aggravates the wear and aging of the air-conditioning system, but also leads to a large amount of energy waste. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides an energy efficiency management system based on air conditioning electricity consumption, so as to solve the problem in the prior art that the temperature adjustment of the air conditioning group is inaccurate and frequent starting and stopping are required, resulting in a large amount of energy waste.

[0006] An embodiment of the present invention provides an energy efficiency management system based on air-conditioning electricity consumption, including an air-conditioning group for adjusting the temperature of an indoor place and a plurality of pipelines extending from the air-conditioning group; the air-conditioning group includes a plurality of sub-air-conditioning units; all the sub-air-conditioning units are uniformly controlled by the same controller; the controller is electrically connected to the air-conditioning group; the pipeline is composed of a plurality of air ducts connected by flanges; a plurality of branches are arranged in the pipeline, each of the branches is connected to a different position in the indoor place; a diffuser is arranged at the end of each branch; the controller pre-adjusts the energy efficiency of the air-conditioning group by predicting the future flow of people in the indoor place; after the air-conditioning group pre-adjusts the energy efficiency, the predicted future flow of people is corrected by real-time monitoring of the flow of people in the indoor place; the controller matches the corrected flow of people with the energy efficiency of the air-conditioning group.

[0007] Preferably, the air duct is made by splicing a first angle plate and a second angle plate; the first angle plate and the second angle plate are both right-angle plates; the two ends of the first angle plate are respectively a snap-in end and a snap-in end; the snap-in end is arranged in a flat plate; the snap-in end is made by bending several times, and forms a first bending groove and a second bending groove; when the first angle plate and the second angle plate are installed, the snap-in end is connected to the second bending groove to complete the snap-in connection, and the end of the second bending groove is bent to prevent the snap-in end from falling out; the first angle plate and the second angle plate are arranged in the same manner; the first angle plate and the second angle plate are assembled by splicing end to end.

[0008] Preferably, the end of each branch is supplied with air through a branch pipe, and the diffuser is arranged at the end of the branch pipe; a regulating valve is provided at the opening of the diffuser for adjusting the air volume; a plurality of diffuser plates are provided at the end of the regulating valve; and the plurality of diffuser plates are inclined at a preset angle.

[0009] Preferably, including:

[0010] S1: Set the initial working state of the air conditioning group;

[0011] S2: Collect real-time energy consumption data and predict energy consumption demand in the future;

[0012] S3: Compare the energy consumption demand at the future moment with the output energy consumption of the air conditioning group;

[0013] S4: Determine and adjust the operation mode of the air conditioning group according to the comparison result;

[0014] Preferably, S2 includes predicting the future energy consumption demand based on the future passenger flow, the thermal conductivity characteristics of the building, and the energy consumption impact data caused by the loss of air conditioning gas when it is transported inside the air duct; the future passenger flow is predicted by a passenger flow prediction model;

[0015] Among them, the energy consumption demand at the future moment is: Q t+1 =Q 人′ +Q 物 +Q 管 ;

[0016] Among them, the Q t+1 is the energy demand at time t+1; Q 人′ Energy consumption demand due to temperature rise caused by human traffic; Q 物 Energy consumption demand caused by the temperature rise due to heat conduction of the building; Q 管 is the energy consumption demand caused by the loss of air conditioning gas in the air duct (123) during transportation; wherein, Q 人′ =N×q; N is the impact of the heat brought by each person on energy consumption; q is the number of people.

[0017] Preferably, the crowd flow prediction model is:

[0018] q t+1 =q t +B T ×u t+1 +K t (q t′ -q t );wherein, the q t+1 is the passenger flow at the next moment predicted based on time t; B is the weight matrix of the control input, u t+1 is the influence coefficient matrix of the control input at time t+1; K t is the correction factor; q t is the passenger flow at time t predicted at the previous time t; q t′ is the flow of people observed by the monitor or sensor at time t.

[0019] Preferably, the Q 管 This includes the energy consumption demand caused by the heat loss caused by the material of the air duct, and the energy consumption demand caused by the air leakage loss caused by the connection structure of the air duct.

[0020] Preferably, S4 includes: when the difference between the energy consumption demand at the future moment and the output energy consumption of the air-conditioning group is greater than 2.2kW, adding a number of the sub-air-conditioning units to operate; when the difference between the energy consumption demand at the future moment and the output energy consumption of the air-conditioning group is less than negative 2.2kW, shutting down the operations of a number of the sub-air-conditioning units; when the energy consumption demand at the future moment and the output energy consumption of the air-conditioning group are between 2.2kW and negative 2.2kW, maintaining the current output energy consumption of the air-conditioning group to operate.

[0021] Preferably, B and u t+1 The control inputs include season, whether it is a working day and weather; the season is divided into spring, summer, autumn and winter; whether it is a working day includes working days and non-working days; the weather includes sunny, cloudy, rainy and snowy; among which, spring, summer, autumn, winter, working days, non-working days, sunny, cloudy, rainy and snowy have different influence weights and coefficients respectively.

[0022] Preferably, it includes: a control module: used to adjust and control the working mode or working state of the air-conditioning group; a collection module: collecting real-time energy consumption data and past energy consumption data of the central air-conditioning; a prediction module: predicting future energy consumption data through real-time energy consumption data and past energy consumption data; a comparison module: comparing the output energy consumption of the central air-conditioning with the predicted future demand energy consumption; a determination module: determining the working mode or state of the air-conditioning group according to the comparison data of the comparison module; wherein the control module, the collection module, the prediction module, the comparison module and the determination module work in sequence and in a reciprocating cycle; wherein the prediction module makes predictions through data obtained by sensors and monitors.

[0023] The energy efficiency management system based on air-conditioning electricity consumption provided by the present invention has the following beneficial effects:

[0024] In the present invention application, by setting different branch pipes, the regulating effect of the air conditioner can be transmitted to different locations; and the energy consumption demand is jointly adjusted by the regulating method set by the energy efficiency management system and the structural setting of the air duct. And through the regulating method, data collection, intelligent prediction and real-time adjustment are carried out, so that the central air conditioning system of the shopping mall can predict and match the temperature demand in advance in different seasons, different weather, different dates, different time periods and different traffic flows, so as to achieve the dual goals of energy saving and improving customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the following is a brief introduction to the drawings required for use in the embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work, and these are all within the protection scope of the present invention.

[0026] Figure 1 It is a system diagram of an energy efficiency management system based on air conditioning electricity consumption;

[0027] Figure 2 It is a schematic diagram of part of the air duct structure;

[0028] Figure 3 It is a partial schematic diagram of the air duct connection;

[0029] Figure 4 is a schematic diagram of the structure of the second angle plate;

[0030] Figure 5 It is a schematic diagram of the air duct on the pipeline;

[0031] Figure 6 is a schematic diagram of the branch pipe and its diffuser;

[0032] Figure 7 It is a schematic diagram of the structure of the elbow;

[0033] Figure 8 It is a schematic diagram of the split structure of the flange;

[0034] Fig. 9 It is a working principle diagram of an energy efficiency management system based on air conditioning electricity consumption;

[0035] Fig.10 It is a PLC hardware structure diagram of an energy efficiency management system based on air conditioning power consumption;

[0036] Fig.11 It is a control block diagram of an energy efficiency management system based on air conditioning power consumption;

[0037] Parts and numbers in the picture:

[0038] 110-sub-air conditioning unit;

[0039] 120-pipeline, 121-elbow, 122-guide plate, 123-air duct, 124-first angle plate, 125-second angle plate, 126-snapping end, 127-snapping end, 128-first bending groove, 129-second bending groove;

[0040] 130-flange, 131-angle steel, 132-four corner fixing plates, 133-gasket;

[0041] 140- diffuser, 141- regulating valve, 142- diffuser plate, 143- branch pipe;

[0042] 200-Controller. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described in conjunction with the drawings in the embodiment of the present invention. It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the orientation or position relationship indicated by the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. Moreover, the term "includes", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article 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 a process, method, article or device. In the absence of further restrictions, the elements defined by the phrase "comprising..." do not exclude the existence of other identical elements in the process, method, article or device comprising the elements. If there is no conflict, the embodiments of the present invention and the various features in the embodiments can be combined with each other, all within the protection scope of the present invention.

[0044] Example 1

[0045] See also Figure 1 The embodiment of the present invention provides an energy efficiency management system based on air conditioning electricity consumption. The energy efficiency management system mainly analyzes and predicts the energy consumption demand of a certain indoor public place, dynamically adjusts the operation strategy of the central air conditioner, thereby minimizing energy consumption while ensuring a comfortable environment.

[0046] The energy efficiency management system is suitable for public places such as large shopping malls, office buildings, exhibition centers, airport terminals, etc., especially in these places, the central air conditioning system is often the main source of energy consumption. By combining crowd flow prediction and temperature pre-adjustment, the system can effectively reduce the cost of air conditioning electricity while ensuring user comfort, with significant energy-saving and environmental benefits. For different places, the information collected is also different.

[0047] See also Figure 1In this embodiment, an energy efficiency management system based on air-conditioning electricity consumption is provided, which aims to improve energy efficiency by optimizing the operation of central air-conditioning, and includes the following main components and structures; including an air-conditioning group, the air-conditioning group is composed of a number of sub-air-conditioning units 110, each sub-air-conditioning unit 110 can operate independently and has independent cooling, heating, ventilation and other functions. Furthermore, the air-conditioning group is an integrated system with integrated cooling, heating, ventilation and other functions. The air-conditioning group or the sub-air-conditioning unit 110 are all intelligently and uniformly controlled, and can be flexibly adjusted according to the specific needs of the venue.

[0048] The air conditioning group or all sub-air conditioners are uniformly managed by the same central controller 200. The controller 200 communicates with the air conditioning group through a telecommunication connection, monitors the operating status, temperature setting, energy consumption, etc. of each sub-air conditioner in real time, and automatically adjusts the working status of different numbers of sub-air conditioners according to the actual conditions of the indoor place and the preset energy-saving strategy. The controller 200 can also receive external input information, such as energy consumption requirements, weather data, pedestrian flow forecast data, etc., so as to more accurately control the operation of the air conditioning system.

[0049] See also Fig.10 , the controller 200 is controlled by PLC. The PLC control panel is provided with a number of monitoring devices such as infrared sensors and monitors for obtaining the energy consumption requirements of different working environments, etc., and is also provided with a temperature sensor for obtaining the temperature of indoor, outdoor, air duct 123, building and other components; it also includes a number of control elements and status indicator lights. Different components such as water pumps, blowers, return fans or exhaust fans are connected through frequency converters. It also includes a number of execution components.

[0050] For further information, see Figure 2 The air conditioning unit delivers cold or warm air to various areas of the room through a number of air ducts 123, ensuring efficient and energy-saving air circulation. The air ducts 123 are connected by flanges 130, and gaskets 133 are provided between them; this connection method not only ensures the sealing of the pipes 120, but also facilitates the installation and maintenance of the pipes 120. The design of the pipe 120 system fully considers the optimization of air flow to reduce energy loss and improve transmission efficiency. See Figure 8 The flange 130 is composed of a plurality of angle steels 131 , and two angle steels 131 are fixed by four-corner fixing plates 132 .

[0051] See also Figure 5 and Figure 6In the piping system, several branches extend from the main air duct and are connected to different locations in the indoor place to ensure that the air can be evenly distributed to each area. This branch design allows the system to adjust the temperature and air flow according to the needs of different areas, thereby achieving more precise temperature control and energy saving effects. A diffuser 140 is provided at the end of each branch. The diffuser 140 is responsible for evenly dispersing the air transported through the air duct 123 to the indoor place. Depending on the installation location and the function of the area, the diffuser 140 can choose different forms, such as fixed blade type, adjustable blade type, etc., to meet different air supply needs. The diffuser 140 adopts a square diffuser.

[0052] For further information, see Figure 7 , the main air duct usually divides into several branches to transport air to different areas or rooms. In order to achieve this branch transportation and ensure the efficiency and directionality of air flow, specific pipe accessories are needed to change the direction of the air duct. In this case, the elbow 121 allows the main air duct to change direction smoothly during the transportation process, thereby guiding the wind flow to each branch area. The inside of the elbow 121 is usually equipped with a guide plate 122, and its main function is to optimize air flow, reduce wind resistance, and improve transportation efficiency. The guide plate 122 ensures that the air can be evenly and effectively distributed to each branch air duct by adjusting and guiding the direction of the airflow, thereby meeting the ventilation needs of different areas.

[0053] See also Figure 1 and Fig.10 The controller 200 integrates a control module and a prediction module, and realizes pre-adjustment of the working condition of the central air conditioner by predicting the future energy consumption demand. In the prediction module, the flow of people in the indoor place in a certain time period or time in the future can be predicted based on the analysis of historical data (such as seasonal changes in human flow, weather conditions, special events, etc.), and the change of human flow can be accurate to every hour or even shorter time intervals. Based on the influence of the predicted human flow data on the demand for cooling capacity, the demand for the number of people and energy efficiency is judged; the controller 200 will adjust the operating parameters or operating mode of the air conditioning group in advance, such as starting more sub-air conditioning units 110 to increase the output of cooling or heating, or appropriately shutting down the working intensity of some sub-air conditioners to save energy. Through pre-adjustment, the system can adjust the indoor temperature to a suitable level according to the cooling demand of the upcoming human flow before the predicted peak of human flow arrives, so as to ensure the comfort level in the place. In order to improve the response speed and accuracy of the system, the controller 200 is equipped with a real-time monitoring component; the component obtains the current actual human flow data in real time through various sensors (such as infrared sensors, cameras, etc.) installed in the place. These sensors can accurately detect the number, density and distribution of people in the venue. The real-time monitoring data will be compared and analyzed with the previous prediction results.

[0054] The system in this embodiment combines crowd flow prediction, real-time monitoring, prediction correction and energy efficiency matching adjustment, so that the system can accurately predict future environmental needs and make adjustments in advance. This high-precision control method ensures that the system can provide a stable and comfortable ambient temperature while maximizing energy conservation.

[0055] See also Fig.11 In this embodiment, based on the control of PLC, an automatic control system for central air conditioning is obtained, which is mainly composed of two parts and four links. The two parts are the control device and the controlled object (the temperature of the indoor place). The four links are mainly composed of the measuring element, the comparing element, the actuator and the controlled object.

[0056] Measuring element refers to the element that measures the output. In this system, it is a humidity sensor, infrared sensor and monitor. Comparison element refers to the element that compares the output with the given value and calculates it to obtain the error signal as the control signal adjustment. The actuator acts according to the error signal to make the controlled object close to the target given value. The actuator of this control system is the various components of the central air conditioner. The controlled object refers to the equipment or target parameters controlled by the automatic control system. In this system, it is the temperature of the indoor place. Interference may come from inside the control system or from outside the system, but it is an adverse factor that affects the control of the system. Contrary to the system output, the effect of disturbance should be avoided as much as possible. This system is such as the constant changes in outdoor weather conditions and changes in indoor personnel.

[0057] See also Figure 2 and Figure 3 The air duct 123 is an important component in the central air-conditioning system, which is responsible for delivering conditioned air to various areas of the indoor place. The air duct 123 is composed of multiple sections, and each section is spliced ​​by angle plates to ensure the sealing and stability of the pipeline. The spliced ​​part of the air duct 123 is composed of a first angle plate 124 and a second angle plate 125, both of which are right-angle plates. This design ensures the structural strength of the splicing of the air duct 123, and can withstand mechanical stress and environmental changes such as thermal expansion and contraction in daily use. The first angle plate 124 and the second angle plate 125 are consistent in shape and size, and are both right-angled, ensuring that the spliced ​​air duct 123 sections can fit tightly and are not prone to gaps or looseness.

[0058] For further information, see Figure 4, the two ends of the first angle plate 124 are respectively set as a snap-in end 126 and a snap-in end 127. The snap-in end 126 adopts a flat plate design, which is simple and practical in structure and can be easily inserted into the bending groove of another angle plate; the snap-in end 127 is formed by a precise bending process and has a multi-level bending groove structure, specifically including a first bending groove 128 and a second bending groove 129. The first bending groove 128 provides a preliminary fixing force for the angle plate, while the second bending groove 129 provides a stronger fixing effect through further snapping. When the first angle plate 124 and the second angle plate 125 are spliced, the snap-in end 126 of the first angle plate 124 will be inserted into the second bending groove 129 of the second angle plate 125. Through this design, the angle plates can be tightly snapped together to avoid loosening or falling off. At the same time, the end of the second bending groove 129 is further bent to increase the anti-slip function, effectively preventing the snap-in end 126 from slipping out of the groove. This structure not only enhances the stability of the joints of the air duct 123, but also simplifies the installation process and reduces the amount of air leakage when the air duct 123 is conveying air.

[0059] See also Figure 2 , Figure 3 and Figure 5 In order to ensure the continuity and sealing of the air duct 123, the first angle plate 124 and the second angle plate 125 are assembled by end-to-end splicing. That is, one end of the first angle plate 124 is connected to the second angle plate 125 of another section of the air duct 123, and so on, until the installation of the entire air duct 123 system is completed. This splicing method ensures that the various sections of the air duct 123 can be tightly combined without leaving gaps, thereby preventing air leakage, reducing air leakage, and improving the transmission efficiency of the system. Since the first angle plate 124 and the second angle plate 125 have a simple design and a consistent structure, the splicing process of the air duct 123 is more convenient and the installation efficiency is significantly improved. The bending groove setting of the angle plate makes the connection of the air duct 123 have good stability, can maintain the stability of the connection for a long time, and is not easily affected by external forces and loosened. This highly stable splicing method extends the service life of the air duct 123.

[0060] Furthermore, the material of the air duct 123 is selected to be a polyurethane composite board, and the heat transfer coefficient of such a composite board is generally 0.2-0.4 W / m 2·℃, and has very excellent thermal insulation properties. When this material is used in air-conditioning systems, it can greatly reduce the transfer of heat, thereby reducing the loss of air volume. Therefore, when used in air-conditioning systems, it can effectively maintain the temperature of cold or warm air and reduce the energy consumption of air-conditioning systems. In addition, polyurethane composite panels also have good mechanical strength and durability, and can work stably for a long time in various environments, ensuring the stable operation of air-conditioning systems. Therefore, choosing polyurethane composite panels as the material for air duct 123 can not only improve the performance of the air-conditioning system, but also extend its service life, with significant economic and environmental benefits.

[0061] In the energy efficiency management system, the end of each branch is precisely supplied with air through the branch pipe 143. The diffuser 140 provided at the end of the branch pipe 143 can not only evenly distribute the air, but also control the air volume and airflow direction through the design of the regulating valve 141 and the diffuser plate 142, thereby further optimizing the energy efficiency and indoor comfort of the central air conditioning system.

[0062] See also Figure 5The branch pipe 143 is a key passage connecting the branch and the diffuser 140, and is responsible for delivering the conditioned air to the diffuser 140. The diffuser 140 is installed at the end of each branch pipe 143, and is responsible for evenly distributing the air delivered through the branch pipe 143 to the indoor area. The setting of the diffuser 140 should not only ensure the uniformity of air distribution, but also take into account noise control. The diffuser 140 is usually made of durable materials and can maintain good performance during long-term use. The regulating valve 141 is set at the opening of the diffuser 140 to adjust the air volume. The regulating valve 141 can adjust the amount of air entering the diffuser 140 through manual or automatic control to meet the needs of air volume in different areas. The regulating valve 141 is two trumpet-shaped openings, and a plate is moved on the trumpet-shaped opening to control the size of the opening, and the air volume of the diffuser 140 can be adjusted to achieve local temperature or air volume control. A plurality of diffusers 142 are provided at the end of the regulating valve 141, and these diffusers 142 are inclined at a preset angle. The main function of the diffuser 142 is to guide and diffuse the airflow to ensure that the air can be evenly distributed throughout the entire area. By controlling the inclination angle of the diffuser 142, the direction and diffusion range of the airflow can be adjusted to meet the air circulation needs of different areas. For example, in a large space, the diffuser 142 can be inclined to a larger angle to expand the coverage of the airflow; in a small space, the diffuser 142 can be set to a smaller angle to concentrate the airflow direction to avoid air short circuit or uneven airflow. The inclination angle setting of the diffuser 142 can effectively optimize the airflow distribution and avoid dead corners or excessive concentration of airflow. At the same time, the material and structural design of the diffuser 142 also take noise control into consideration to ensure that while providing good air distribution, the impact of wind noise on the indoor environment is reduced. As the indoor environment and the flow of people change, the system can dynamically adjust the distribution pattern of the airflow by adjusting the opening of the diffuser 140 and the angle of the diffuser 142. This dynamic adaptability enables the system to operate efficiently in a variety of usage scenarios, meeting air demand during peak hours while saving energy during off-peak hours.

[0063] The controller 200 of the system generates an efficient energy management solution through sophisticated data collection and processing steps combined with a complex crowd flow prediction model, and pre-adjusts the indoor environment to ensure that the air-conditioning system can automatically adapt to future peak or trough traffic flow, thereby achieving optimal energy efficiency.

[0064] 1. Data collection:

[0065] Various types of monitoring equipment are installed in various areas (imports and exports) of indoor places (shopping malls), including infrared sensors, cameras, temperature sensors, humidity sensors, weather station interfaces, etc. These devices can collect human traffic data in real time and accurately, as well as environmental information such as the current weather, season, date, etc. The operation data of the shopping mall includes historical temperature records, holiday information, promotional activities, etc. that are directly related to human traffic. These external factors will be included in the data collection scope to produce a more accurate prediction of human traffic. The collected data is transmitted to the central data processing unit of the controller 200 in real time via a wireless or wired network, and stored in a high-capacity database for subsequent analysis and processing. The storage format and structure of the data are optimized to ensure efficient big data analysis and fast query.

[0066] 2. Data processing:

[0067] In the data processing process, the collected raw data must first be processed to remove noise or invalid data. For example, abnormal data caused by equipment failure, abnormal values ​​caused by bad weather, or sudden data anomalies caused by special events (such as power outages and fire alarms) all need to be identified and eliminated at this stage. Data collected by different devices may have different formats and units. In order to facilitate subsequent analysis, these data must be standardized into a unified format. For example, temperature data can be uniformly converted to degrees Celsius, time format can be unified to a 24-hour system, date data can be unified to a year-month-day format, etc. In addition, all data will be synchronized according to timestamps to ensure the consistency of analysis timing.

[0068] 3. Feature extraction:

[0069] After the data processing is completed, the controller 200 will further analyze the data and extract the key features that affect the air conditioning temperature regulation. These features include but are not limited to: crowd density, outdoor temperature, current weather (such as sunny, rainy, cloudy, snowy), season (such as winter, summer, autumn, winter), time (such as working day, non-working day), etc. Through the machine learning algorithm, the system can automatically identify and weight the impact of different features on the air conditioning load, and establish a model for the crowd flow calculation formula.

[0070] 4. Traffic prediction:

[0071] Prediction model: The controller 200 will use the processed data after feature extraction and input it into the prediction model to predict the flow of people. The prediction model can predict the possible flow of people in a specific period of time in the future based on the flow prediction formula summarized by the regularity, trend and periodic changes in historical data. After calculation, the system will output a series of predicted values ​​of flow of people at future time points. These predicted values ​​not only reflect the overall flow trend of people, but can also be refined to specific time intervals, providing an accurate basis for the energy efficiency management of central air conditioners.

[0072] 5. Pre-conditioning:

[0073] Based on the predicted traffic data, the controller 200 will automatically generate a corresponding energy efficiency management plan. This plan will comprehensively consider the prediction results, the current indoor and outdoor temperatures, the mall's operating hours and other factors to determine the operation strategy of the air-conditioning group in the future time period. For example, during the expected peak traffic period, the system will increase the output of cooling or heating in advance to ensure that the temperature in the venue can be maintained in a comfortable range; during the expected sparse traffic period, the air-conditioning load will be reduced or some air-conditioning units will be turned off to save energy. According to the energy efficiency management plan, the air-conditioning group will automatically adjust the temperature set point and quickly respond to the needs of changes in traffic by accurately controlling the air volume and refrigerant flow. This pre-adjustment can ensure that various areas in the mall always maintain suitable temperatures and air quality during different time periods, thereby improving user experience and optimizing energy consumption.

[0074] Example 2

[0075] This embodiment provides an energy efficiency management method based on air conditioning power consumption. In this embodiment, a medium-sized shopping mall is used as the background to describe an energy efficiency management method based on air conditioning power consumption. The energy efficiency management method based on air conditioning power consumption in this embodiment is implemented based on the system in Example 1.

[0076] S1: Set the initial working state of the air conditioning group.

[0077] The air conditioning group sets the initial temperature, wind speed and mode parameters of the air conditioning group according to the type of building, historical energy consumption data and current environmental conditions to ensure efficient startup of the system. Generally, based on a medium-sized shopping mall, when it is just starting business, there are not many people, and the air conditioning group enters an initial working state, that is, the temperature of the indoor place is controlled at a suitable temperature of 26°C. At this time, the air conditioning group has an initial output energy consumption.

[0078] S2: Collect real-time energy consumption data and predict energy consumption requirements in the future.

[0079] Central air conditioners collect data such as the current power consumption of the air conditioner group, the temperature difference between the inside and outside of public places, the current flow of people, and the operating status of equipment in real time through sensors and monitors. Based on the flow prediction model, the heat conduction characteristics of the building, and the energy consumption impact caused by the loss of air conditioning gas when it is transported inside the air duct, the energy consumption increase caused by the temperature change caused by the above factors is estimated and calculated, and the energy consumption demand at future moments is dynamically predicted.

[0080] S3: Compare the energy consumption demand at the future moment with the output energy consumption of the air conditioning group.

[0081] The predicted energy demand in the future is compared with the energy output of the air conditioning group in the current operating mode to identify possible excessive or insufficient energy consumption.

[0082] S4: Determine and adjust the operation mode of the air conditioning group according to the comparison result.

[0083] When the predicted future energy consumption demand exceeds the output capacity of the current air-conditioning group, the cooling (or heating) intensity, wind speed and operating frequency of each sub-air-conditioning unit of the air-conditioning group are adjusted; when the future energy consumption demand is lower than the output capacity of the current air-conditioning group, the system output power is appropriately reduced or some sub-air-conditioning units enter standby mode to avoid wasting electricity.

[0084] Furthermore, the air conditioning unit regulates the indoor temperature; the greater the regulation demand, the greater the energy consumption, which also means that the heat generated outside is also higher.

[0085] Therefore, the energy consumption demand calculation formula Q is introduced 总 =Q1+Q2+SQ N ; By combining the energy consumption of each part that affects internal consumption, the actual energy consumption demand can be obtained.

[0086] In this embodiment, the influence of heat on energy consumption caused by the flow of people, the heat conduction characteristics of the building, and the heat loss caused by the transmission of the air duct 123 and the temperature rise is taken into consideration.

[0087] Therefore, according to Q 总 =Q1+Q2+……+Q N The energy demand is: Q t+1 =Q 人 +Q 物 +Q 管 ; Since it is a prediction of future energy consumption; therefore, the energy consumption demand at the future time t+1 is Q t+1 =Q 人′ +Q 物 +Q 管 .

[0088] At time t, we predict the energy demand at time t+1 based on the energy demand formula at future times; the energy output at time t is Q 输出 , by judging and comparing Q t+1 and Q 输出 The relationship or difference range is used to determine whether the energy consumption output of the air-conditioning group at time t can match the energy consumption demand at the future time t+1. If it does not match, a matching adjustment is made.

[0089] The Q t+1 is the energy demand at time t+1.

[0090] Q 人 Energy consumption demand due to temperature rise caused by human traffic; Q 人′ The energy demand caused by the temperature rise due to the predicted flow of people. 人′ =N×q; wherein N is the impact of the heat generated by each person on energy consumption (unit: W); q is the future flow of people; the future flow of people is predicted by the flow prediction model.

[0091] In a medium-sized shopping mall, the heat dissipated by the human body usually includes the following sensible heat (this is the heat dissipated directly to the surrounding environment by the human body through the skin) and latent heat (this is the heat dissipated by the human body through evaporation). According to experience, for an adult with light activities (such as walking around in the mall, shopping), the total heat dissipated by each person affects energy consumption by about 100W to 150W. For example, light activities (such as walking and strolling): the heat dissipated by each person per hour affects energy consumption by about 100W. Medium activities (such as more walking and frequent movement between floors): the heat dissipated by each person per hour affects energy consumption by about 150W.

[0092] Q 物 Energy consumption demand caused by the temperature rise due to heat conduction of the building; Q 物 =U 建 ×A 建 ×|T 外 -T 内 |;U 建 is the heat transfer coefficient of the building (unit: W / m 2 ℃), the heat transfer coefficient is related to the building material, different materials have different values, the building material of the shopping mall is an insulated wall structure with an insulation layer, U≈0.3-0.6W / m 2 ℃, take the middle value 0.4W / m 2 ℃.

[0093] A 建 is the heat transfer area of ​​the building, which includes the wall area, roof area and window area. According to experience, the floor area of ​​a medium-sized shopping mall is 5000m2 The overall heat transfer area is approximately 8500m 2 .

[0094] T 外 is the outdoor temperature (unit: °C), T 内 For indoor temperature, indoor temperature and future outdoor temperature are obtained by prediction based on real-time temperature or weather forecast.

[0095] Q 管 Q is the energy consumption demand caused by the loss of air conditioning gas inside the air duct 123 during transportation; 管 =(U 管 ×A 管 +L×ρ×C p )ΔT represents the sum of the energy consumption demand caused by the heat loss caused by the material of the air duct 123 and the energy consumption demand caused by the air leakage loss caused by the connection structure of the air duct 123.

[0096] U 管 is the heat transfer coefficient of the air duct 123. According to the material of the air duct 123 set in Example 1, the heat transfer coefficient is 0.2-0.4 W / m 2 ℃, take the middle value 0.3W / m 2 ℃. A 管 is the surface area of ​​the air duct 123; according to experience, in a medium-sized shopping mall, a rectangular air duct 123 with a side length of 0.5m×1m needs to be laid for about 650m to meet the needs, so the surface area of ​​the air duct 123 is about 1950m 2 L is the air leakage of the rectangular air duct 123. Since the splicing structure of the air duct 123 adopted in Example 1 can greatly reduce the air leakage of the air duct, when the air leakage of the air duct 123 adopts the structure in Example 1, in practice, L is about 0.1m 3 / s. The air leakage is estimated by the possible leakage area and the sealing degree. ρ is the air density, in kg / m 3 The typical density of air is 1.2 kg / m 3 ; C p The specific heat capacity of air is expressed in J / (kg·℃), and the specific heat capacity of air is approximately 1005 J / (kg·℃); ΔT is the temperature change between the inside and outside of the air duct 123, and the unit is ℃.

[0097] Furthermore, the passenger flow prediction model is: t+1 =q t +B T ×u t+1 +K T (q T′ -q T ); said qT+1 is the passenger flow at the next moment predicted based on moment t; q t is the passenger flow at time t predicted at the previous moment of t.

[0098] For example, in a case of predicting the flow of people at time t+1, the prediction is made at time t, and the prediction is also based on the flow of people at time t.

[0099] B is the weight matrix of control input; the factors of control input include external factors such as season, weather, whether it is a weekday, whether there is an activity, etc. Since activities have uncertain factors, the deviation that needs to be corrected by actual observation of the flow of people is large; therefore, season, weather, whether it is a weekday are selected as the control input matrix, and their weights are assigned accordingly: According to statistics, the biggest influences on passenger flow are season, weather, and whether it is a weekday. Weights are assigned based on their influence on passenger flow:

[0100] u t+1 is the influence coefficient matrix of the control input at time t+1. Different seasons, weather, and whether it is a working day have different influences on the flow of people; the seasons are divided into spring, summer, autumn and winter, and the influence coefficients of spring, summer, autumn and winter are set to 1, 10, 8, and -5 respectively; the weather is divided into sunny, cloudy, rainy and snowy, and the influence coefficients of sunny, cloudy, rainy and snowy are 5, 10, -4, and -10 respectively. Sunny days are weather with a certain temperature, cloudy days are relatively cool, and rainy and snowy days are inconvenient to go out; whether it is a working day is divided into working days and non-working days, and the influence coefficients of working days and non-working days are 5 and 10 respectively; on working days, the majority of people in the mall are generally staff members, people who work and eat nearby, and some customers. The matrix is ​​obtained by setting the above influence coefficients: By calculating B T ×u t+1 Get control inputs that have an effect on the number of people.

[0101] In this system, the weights of control inputs and the influence coefficients of control inputs are determined by Bayesian regression methods. Specifically, this process involves taking the historical data of the mall as a basis and analyzing and processing these data through the Bayesian regression model. In this way, the weights and influence coefficients of control inputs can be effectively extracted from the historical data, thus providing a scientific basis for system optimization and decision-making. This method not only makes full use of the value of historical data, but also improves the accuracy and reliability of parameter estimation through the statistical characteristics of Bayesian regression.

[0102] K t is the correction factor calculated by Kalman gain; the calculation formula of Kalman gain is:

[0103] Among them, P t is the prediction error covariance, which can be determined based on the errors in multiple dimensions. In this embodiment, the prediction error only has one factor, the number of people error. t The size of represents the size of the prediction error. t When the value is large, it means that the prediction error is large; when P t A smaller value means a smaller prediction error. Therefore, when initially predicting the flow of people, the prediction error is generally still large, so a larger value is set: 10.

[0104] Furthermore, the P t The update formula is P new =(1-K t ) old ;P old is the last value, P new The value of the next calculation; K t is the last calibration coefficient. t The value of is 10, the following R t The value is 1.44; K t The value of is about 0.87. Substituting it into the above formula, P new The calculated value is about 1.3. Therefore, when calculating the next moment after t+1, P new =1.3Substitute into K t The new correction coefficient is calculated in the calculation formula to obtain the new correction coefficient, and then the new correction coefficient is substituted into the crowd flow prediction model formula for calculation to obtain a more accurate crowd flow prediction value.

[0105] R t is the measurement error covariance, and its measurement error is related to the sensor measurement error. The sensor used in this embodiment has a factory measurement error of ±0.2%. Based on the average flow of people at each moment (assuming 1 hour as a moment) in a medium-sized shopping mall, which is about 600 people, the error at each moment is about ±1.2 people, so R t Take 1.2 squared, which is 1.44.

[0106] q t′ is the flow of people observed by the monitor or sensor at time t.

[0107] Furthermore, the S4 includes:

[0108] At some point in the future, if the difference between the predicted energy demand and the current central air-conditioning system output energy consumption exceeds 2.2kW, the controller will control more sub-air-conditioning units to participate in the work to meet the higher energy demand. This adjustment is to ensure that the indoor temperature can be maintained at a comfortable and stable level while also avoiding energy waste.

[0109] On the contrary, if the difference between the expected energy demand and the output energy consumption of the central air-conditioning system is less than negative 2.2kW, it means that the current air-conditioning output has exceeded the actual demand, and the system will shut down some of the working sub-air-conditioning units. Such operations help to reduce unnecessary energy consumption, thereby achieving energy conservation and emission reduction, and achieving the purpose of energy efficiency control. Air conditioning electricity consumes a lot of energy costs. By setting the energy consumption range, the system can reduce electricity expenses without affecting comfort. This is especially important for places where air conditioning needs to be turned on for a long time, which can significantly reduce electricity bills and save energy consumption.

[0110] Through the above method, the energy consumption demand of the indoor place at the next moment is predicted to achieve the purpose of pre-adjustment, which can not only allow customers to always be in a suitable environment, but also avoid the existing automatic air conditioning, which makes real-time adjustments and causes frequent startup and shutdown of the air conditioner, resulting in electricity waste.

[0111] Furthermore, when the difference between the expected energy demand and the output energy consumption of the central air-conditioning system is between 2.2kW and 2.2kW, the system will maintain the output energy consumption of the existing air-conditioning group unchanged and continue to work according to the current operating status. In this case, the system's energy supply and demand are in a relatively balanced state, and no additional adjustments are required. Such an intelligent adjustment mechanism ensures the efficient operation of the air-conditioning system and avoids the energy waste caused by frequent startup of air-conditioning components; it also provides users with a comfortable and stable indoor environment.

[0112] Example 3

[0113] The embodiment of the present invention provides a case study of the air conditioning energy efficiency management method in embodiment 2. According to the calculation formula Q of its energy consumption requirement in embodiment 2, 总 =Q1+Q2+……Q N , we can know that Q 输出 =Q 人 +Q 物 +Q 管 Therefore, the energy efficiency demand at the future moment is the sum of the energy demand affected by the flow of people at the future moment, the energy demand required to cool the heat generated by heat conduction between buildings, and the energy demand required to cool the heat generated by equipment during working hours. Therefore, Q t+1 =Q 人′ +Q物 +Q 管 .

[0114] In the actual application case, the mall is equipped with 20 sub-air conditioner units with an output power of 50kW; it is assumed that the mall is in a sunny day on a summer weekday at time t; the outdoor temperature is 32°C; the indoor temperature is 24°C, and the temperature difference between the inside and outside of the air duct 123 is 2°C; the predicted flow of people at the previous moment t at time t is about 2000 people, and the flow of people monitored by the monitor and sensor at time t is about 2100 people.

[0115] According to Example 2, Q 人′ =N×q. In the formula, N is the impact of heat generated by each person on energy consumption (unit: W); since the impact of human flow on temperature and the perception of temperature are more obvious, the impact of heat generated by each person on energy consumption is taken as 150W. Therefore, Q 人′ =150W×2000=300000W.

[0116] According to Example 2, Q 物 =U×A×|T 外 -T 内 |. Therefore, Q 物 =0.4W / m2·℃×8500m2×|32-24|℃=27200W.

[0117] According to Example 2, Q 管 =(U 管 ×A 管 +L×ρ×C p )ΔT. According to Example 1 and Example 2, Q 管 =(U 管 ×A 管 +L×ρ×C p )ΔT=(0.3W / m 2 ℃×1950m 2 +0.1m 3 / s×1.2kg / m 3 ×1005J / kg)×2℃≈1411W.

[0118] Therefore, the output power required by the shopping mall at time t is:

[0119] Q t =300000W+27200W+1411W≈328.6KW

[0120] The air conditioning output power at time t is 14×50KW=700KW.

[0121] According to Embodiment 2, the future passenger flow number is predicted by a passenger flow prediction model. According to Embodiment 2, the future passenger flow prediction model is:

[0122] q T+1 =q t +B T ×u t+1 +K T (q T′ -q T )

[0123] Since the flow of people at time t is 2000, the flow of people detected by the monitor and sensor at time t is about 2100, and it is a sunny day on a summer working day. Therefore:

[0124]

[0125] When the number of operating sub-air conditioning units remains unchanged, the output power required at time t+1 is predicted to be:

[0126] Q T+1 =2377×150W+27200W+1411W≈385.2kW

[0127] The air conditioner output power at time t is 14×50KW=700kW and the output power Q required at time t+1 is predicted. t+1 ≈385.2kW, the air conditioner output power at time t is compared with the output power Q required at time t+1. t+1 By comparison, the difference is 314.8kW, far exceeding the range of 2.2kW; and the output power at time t is far exceeding the output power required at the predicted time t+1; therefore, it is necessary to shut down some of the operating sub-air conditioning units 110 and adjust the 8 sub-air conditioning units 110 that are adapted to operate so that the temperature in the mall remains appropriate. It also avoids excessive energy efficiency output of the central air conditioning unit, which leads to waste of air conditioning electricity.

[0128] When the central air-conditioning group is adjusted to operate 8 sub-air-conditioning units 110, the actual output power is 8×50KW=400kW; according to the predicted flow of people and the actual number of sub-air-conditioning units 110 in operation, the actual required output power is 2377×150W+27200W+1411W≈385.2kW; therefore, when the 8 sub-air-conditioning units are in operation, the output power required at time t+1 is met.

[0129] According to Embodiment 2, the correction coefficient is updated every time the flow of people is predicted, so as to help the next prediction to be closer to the actual flow of people; new =(1-K t ) oldGet: P new ≈(1-0.874)10≈1.3; new Substitution Calculate in the above to get the updated K new When predicting the flow of people at the next moment of time t+1, K new Substitute it into the formula of the crowd flow prediction model to calculate a more accurate crowd flow value at the next moment t+1. Repeat this step in each next moment crowd flow prediction to obtain a more accurate crowd flow prediction value.

[0130] Example 4

[0131] An embodiment of the present invention provides a memory, including a memory for storing execution instructions of a controller; wherein the memory is used to store real-time energy consumption data and past energy consumption data of indoor places; wherein the memory is also used to store real-time data and historical data of the flow of people in indoor places, current weather, current season, and current date.

[0132] The memory not only stores the instruction set required for the controller to execute, but also has the ability to collect and save key energy consumption information. Specifically, the memory is designed to record and store real-time energy consumption data of indoor places, which reflects the current energy consumption status of the place. In addition, the memory can also save historical energy consumption data, which records the energy use of the place over a period of time in the past, providing historical reference for energy consumption analysis and optimization.

[0133] In addition to energy consumption data, the storage also provides the function of recording indoor venue traffic information. This means that it can monitor and store the traffic data in the venue in real time. At the same time, the storage can also collect and store real-time data related to the environment, including current weather conditions, current season information, and current date information.

[0134] The memory also has the ability to store historical environmental data, including historical weather conditions, seasonal changes, dates, and other information. These historical data provide data support for long-term energy consumption trend analysis, environmental impact assessment, and the formulation of more accurate energy management plans. By comprehensively considering real-time data and historical data, the memory can help users better achieve more efficient and intelligent energy management.

[0135] Example 5

[0136] See also Fig. 9, the embodiment of the present invention provides a working principle of a central air conditioner. Each of the sub-air conditioning units 110 can operate independently; each of the sub-air conditioning units 110 includes a compressor, an oil separator, a condenser, a heat exchanger, and a cycle back to the compressor and the evaporator. Among them, the oil separator and the compressor include two sets; each set of oil separators and compressors are connected through a high-pressure gas pipe; and extend to the condenser through the oil separator. The compressor is connected to the lubricating oil pipe through a valve port composed of a solenoid valve, an oil filter, a liquid test mirror and a manual shut-off valve. The main function of the valve port is to control and manage the flow of lubricating oil to ensure that the compressor is properly lubricated, thereby ensuring its normal operation. The condenser is also connected to the heat exchanger through a high-pressure liquid pipe. The high-pressure liquid pipe is connected to the heat exchanger and is provided with a battery valve, a drying filter, and a sight glass; the connection between the evaporator and the heat exchanger is also provided with the same valve port as the high-pressure liquid pipe connected to the heat exchanger; the function of the valve port is to control and manage the flow of refrigerant to ensure the normal circulation and evaporation of the refrigerant in the refrigeration system to achieve the expected refrigeration effect. In this working principle, pipelines 120 with different functions such as return pipes, equalizing pipes and air supply pipes are also provided. The return pipe in the air conditioning system is mainly responsible for guiding the refrigerant gas in the evaporator back to the compressor. In the refrigeration cycle, the refrigerant changes from liquid to gas after absorbing heat in the evaporator, and then returns to the compressor through the return pipe for the next cycle. The return pipe is usually connected between the outlet of the evaporator and the inlet of the compressor to ensure that the refrigerant gas can return to the compressor smoothly. The main function of the equalizing pipe in the air conditioning system is to balance the pressure inside the system and ensure that the pressure of the refrigerant is stable during the cycle. This helps to prevent the system efficiency from being reduced or damaged due to uneven pressure. The equalizing pipe is usually connected between the high-pressure side and the low-pressure side of the system to balance the pressure on both sides. The air supply pipe is used to replenish refrigerant in some air conditioning systems to ensure that the refrigerant in the system is maintained at an appropriate level. When the refrigerant in the system is reduced, new refrigerant can be added through the air supply pipe. The air supply pipe is usually connected to the low-pressure side of the system so that refrigerant can be replenished to the system when needed.

[0137] The refrigerant enters the compressor in a low-pressure gas state. The compressor compresses the low-pressure gas into a high-pressure, high-temperature gas and sends it to the oil separator. During the compression process, the refrigerant will carry some lubricating oil. The function of the oil separator is to separate the oil in the refrigerant to ensure that the compressor can be continuously lubricated while allowing pure high-pressure gas to enter the condenser. The high-pressure and high-temperature refrigerant gas enters the condenser and releases heat to the external environment through heat exchange (usually through air or water cooling). In this process, the refrigerant is cooled and condensed from the high-pressure gas into a high-pressure liquid. Some systems may add a heat exchanger between the condenser and the evaporator to improve the efficiency of the system. In the heat exchanger, the high-pressure liquid refrigerant may exchange heat with the low-pressure gas refrigerant, so that the refrigerant temperature is further reduced, and the condensation effect or evaporation effect is improved. After the high-pressure liquid refrigerant enters the evaporator, due to the reduction in pressure, the liquid refrigerant evaporates rapidly and absorbs the surrounding heat, thereby achieving a cooling effect. At this time, the refrigerant becomes a low-pressure gas again. The low-pressure gas refrigerant returns to the compressor again and starts a new round of circulation. Through this cycle, the central air conditioning system can provide cooling or heating in the interior space (indoor place). This system can achieve efficient indoor temperature regulation through effective heat exchange and energy transfer.

[0138] Example 6

[0139] The embodiment of the present invention provides an energy efficiency management device based on air conditioning power consumption, comprising:

[0140] Control module: used to adjust and control the working mode or working state of the central air conditioning group. The main function of this module is to adjust and control the operating mode or working state of the central air conditioning system to ensure that the system can operate efficiently according to environmental requirements and energy-saving goals. It can automatically adjust the system's working mode according to the parameters set by the user, such as temperature setting, wind speed adjustment, compressor start and stop, etc., to achieve the best comfort and energy efficiency ratio.

[0141] Collection module: collects real-time energy consumption data of central air conditioners and past energy consumption data. This module is responsible for real-time collection of energy consumption data of central air conditioning systems, including but not limited to key indicators such as power consumption, refrigerant flow, and compressor operation time. At the same time, it also records historical energy consumption data to provide basic data support for subsequent energy consumption analysis and prediction. Through long-term data accumulation, it can help analyze the energy consumption trend and periodic change law of the system.

[0142] Prediction module: predicts future energy consumption data through real-time energy consumption data and past energy consumption data. Using the real-time energy consumption data and historical energy consumption data provided by the acquisition module, the prediction module predicts energy consumption in the future through the prediction method in Example 2. These predictions can help the system make adjustments in advance and optimize the operation strategy, thereby achieving energy-saving goals.

[0143] Comparison module: compares the output energy consumption of the central air conditioner with the predicted future energy consumption. This module compares and analyzes the real-time output energy consumption of the central air conditioning system with the future energy consumption provided by the prediction module. Through comparison, the operating efficiency and energy consumption performance of the system can be evaluated, abnormal situations can be discovered in time, and a basis can be provided for subsequent optimization and adjustment.

[0144] Determination module: Determines the working mode or status of the central air conditioning group based on the comparison data of the comparison module. Based on the analysis results of the comparison module, the determination module will make a decision to determine the optimal working mode or status of the central air conditioning system. This may include adjusting temperature settings, changing operating modes, optimizing compressor start and stop strategies, etc., to ensure that the system achieves maximum energy efficiency while meeting user needs.

[0145] The workflow of the entire system is continuous and cyclical at every moment. The control module, acquisition module, prediction module, comparison module and judgment module work in a predetermined order to form a closed-loop control system. The prediction module makes accurate predictions based on the data obtained by the integrated sensors and monitors, ensuring that the prediction and adjustment of the system can be based on real-time and accurate information, thereby realizing intelligent management and energy-saving optimization of the central air-conditioning system.

[0146] Example 7

[0147] An embodiment of the present invention provides an electronic device, comprising at least one processor, at least one memory, and computer program instructions stored in the memory, and when the computer program instructions are executed by the processor, the method described in Embodiment 2 is implemented.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy efficiency management system based on air conditioning electricity consumption, comprising an air conditioning unit for regulating indoor temperature and a plurality of pipelines (120) extending from the air conditioning unit; characterized in that: The air conditioning group comprises a plurality of sub-air conditioning units (110); all the sub-air conditioning units (110) are uniformly controlled by a same controller (200); the controller (200) is connected to the air conditioning group; The pipeline (120) is composed of a plurality of air ducts (123) connected by flanges (130); a plurality of branches are provided in the pipeline (120), each of the branches is connected to a different position in the indoor place; a diffuser (140) is provided at the end of each branch; The controller (200) pre-adjusts the energy efficiency of the air conditioning group by predicting the future flow of people in the indoor place; after the air conditioning group pre-adjusts the energy efficiency, the predicted future flow of people is corrected by real-time monitoring of the flow of people in the indoor place; and the controller (200) matches the corrected flow of people with the energy efficiency of the air conditioning group.

2. The energy efficiency management system based on air conditioning electricity consumption according to claim 1, characterized in that: The air duct (123) is made by splicing a first angle plate (124) and a second angle plate (125); the first angle plate (124) and the second angle plate (125) are both right-angle plates; The two ends of the first angle plate (124) are respectively a snap-in end (126) and a snap-in end (127); the snap-in end (126) is arranged in a flat plate; the snap-in end (127) is made by bending several times, and forms a first bending groove (128) and a second bending groove (129); when the first angle plate (124) and the second angle plate (125) are installed, the snap-in end (126) is connected to the second bending groove (129) to complete the snap-in connection, and the end of the second bending groove (129) is bent to prevent the snap-in end (126) from falling out; The first angle plate (124) and the second angle plate (125) are arranged in the same manner; the first angle plate (124) and the second angle plate (125) are assembled by end-to-end splicing.

3. The energy efficiency management system based on air conditioning electricity consumption according to claim 1, characterized in that: The end of each branch is supplied with air through a branch pipe (143), and the diffuser (140) is arranged at the end of the branch pipe (143); a regulating valve (141) is arranged at the opening of the diffuser (140) for regulating the air volume; a plurality of diffuser plates (142) are arranged at the end of the regulating valve (141); and the plurality of diffuser plates (142) are inclined at a preset angle.

4. An energy efficiency management system based on air conditioning electricity consumption according to any one of claims 1 to 3, characterized in that: include: S1: Set the initial working state of the air conditioning group; S2: Collect real-time energy consumption data and predict energy consumption demand in the future; S3: Compare the energy consumption demand at the future moment with the output energy consumption of the air conditioning group; S4: Determine and adjust the operation mode of the air conditioning group according to the comparison result.

5. The energy efficiency management system based on air conditioning electricity consumption according to claim 4, characterized in that: S2 includes predicting future energy consumption demand based on future passenger flow, building heat conduction characteristics, and energy consumption impact data caused by the loss of air conditioning gas when it is transported inside the air duct (123); the future passenger flow is predicted using a passenger flow prediction model; Among them, the energy consumption demand at the future moment is: Q t+1 =Q 人′ +Q 物 +Q 管 ; Among them, the Q t+1 is the energy demand at time t+1; Q 人′ Energy consumption demand due to temperature rise caused by human traffic; Q 物 Energy consumption demand caused by the temperature rise due to heat conduction of the building; Q 管 The energy consumption demand caused by the loss of air conditioning gas inside the air duct (123) during transportation; Among them, the Q 人′ =N×q; N is the impact of the heat brought by each person on energy consumption; q is the number of people.

6. The energy efficiency management system based on air conditioning electricity consumption according to claim 5, characterized in that: The crowd flow prediction model is: t+1 =q t +B t ×u t+1 +K t (q t′ -q t ); Among them, the q t+1 is the passenger flow at the next moment predicted based on time t; B is the weight matrix of the control input, u t+1 is the influence coefficient matrix of the control input at time t+1; K t is the correction factor; q t is the passenger flow at time t predicted at the previous time t; q t′ is the flow of people observed by the monitor or sensor at time t.

7. The energy efficiency management system based on air conditioning electricity consumption according to claim 5, characterized in that: The Q 管 This includes the energy consumption demand caused by heat loss caused by the material of the air duct (123), and the energy consumption demand caused by air leakage loss caused by the connection structure of the air duct (123).

8. The energy efficiency management system based on air conditioning electricity consumption according to claim 5, characterized in that: The S4 includes: When the difference between the energy consumption demand at the future moment and the output energy consumption of the air conditioning group is greater than 2.2 kW, adding a plurality of sub-air conditioning units (110) to operate; When the difference between the energy consumption demand at the future moment and the output energy consumption of the air conditioning group is less than negative 2.2 kW, shutting down the operations of a number of the sub-air conditioning units (110); When the energy consumption demand at the future moment and the output energy consumption of the air-conditioning group are between 2.2 kW and negative 2.2 kW, the current output energy consumption of the air-conditioning group is maintained to perform the operation.

9. The energy efficiency management system based on air-conditioning electricity consumption according to claim 6, characterized in that: The B and u t+1 The control inputs include season, whether it is a weekday, and weather; Said seasons are divided into spring, summer, autumn and winter; Whether the statement is a working day includes working days and non-working days; Said weather includes sunny, cloudy, rainy and snowy; Among them, the spring, summer, autumn, winter, working days, non-working days, sunny, cloudy, rainy and snowy have different impact weights and coefficients respectively.

10. The energy efficiency management system based on air conditioning electricity consumption according to claim 1, characterized in that: include: Control module: used to adjust and control the working mode or working state of the air conditioning group; Collection module: collects real-time energy consumption data of central air conditioners and past energy consumption data; Prediction module: predict future energy consumption data through real-time energy consumption data and past energy consumption data; Comparison module: compares the output energy consumption of the central air conditioner with the predicted future demand energy consumption; Determination module: Determine the working mode or state of the air conditioning group according to the comparison data of the comparison module; The control module, the acquisition module, the prediction module, the comparison module and the determination module are operated sequentially and in a reciprocating cycle; The prediction module performs prediction based on data acquired by sensors and monitors.

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