An energy efficiency management system based on air conditioning electricity consumption

Abstract

The present invention relates to the field of air conditioning technology, and specifically discloses an energy efficiency management system based on air conditioning electricity consumption, comprising an air conditioning group, pipelines, and sub-air conditioning units; the sub-air conditioning units are uniformly controlled by a central controller; the controller is electrically connected to the air conditioning group; the pipelines are composed of air ducts connected by flanges; a diffuser is provided at the end of each branch; the controller pre-adjusts the energy efficiency of the air conditioning group based on a prediction of future indoor passenger flow; after the air conditioning group pre-adjusts the energy efficiency, the predicted future passenger flow is corrected by real-time monitoring of indoor passenger flow; and the controller matches the corrected passenger flow with the energy efficiency of the air conditioning group. This system enables the central air conditioning system of a shopping mall to accurately match temperature requirements in different seasons, weather conditions, dates, time periods, and passenger flow, achieving the dual goals of energy saving and improving customer experience.

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 rapidly changing world, with rapid technological advancements and significant improvements in people's quality of life, air conditioners have quietly become an indispensable part of daily life. Whether in the scorching summer or the chilly winter, air conditioners, with their superior heating and cooling capabilities, create a pleasant indoor environment and greatly enhance comfort. Air conditioners offer the ability to adjust indoor temperature, but this process relies heavily on the user's personal judgment and operation. Often, we set a temperature based on our subjective feelings or habit, but such settings may not accurately meet our actual comfort needs. The high energy consumption of air conditioners is also worthy of our consideration. While pursuing indoor comfort, the energy consumption and environmental impact of air conditioning operation are often overlooked.

[0003] 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), a method for automatically adjusting the temperature of an air conditioner is disclosed. 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, although prior art 1 has achieved automatic indoor temperature adjustment based on ambient temperature monitoring and historical temperature records, its performance in practical applications is far from ideal. This is primarily because the temperature adjustment method and influencing parameters relied upon by this technology are relatively simple and limited, failing to fully and accurately capture and respond to complex changes in the indoor environment. This results in insufficient temperature adjustment precision and a significant increase in energy consumption. Specifically, prior art 1 often relies solely on real-time ambient temperature readings or historical temperature data as the basis for decision-making during temperature adjustment. This simple adjustment logic ignores multiple factors influencing the indoor environment, such as changes in the number of people indoors, weather conditions, and seasonal variations, all of which can affect indoor temperature fluctuations to varying degrees. Therefore, relying solely on ambient temperature or historical temperature records for temperature adjustment makes it difficult to achieve precise temperature control and may even result in problems such as excessive temperature fluctuations and delayed adjustment, which in turn affect occupant comfort and health. Due to inaccurate temperature adjustment, air conditioning systems often need to be frequently started and stopped in an attempt to achieve the preset temperature target. This frequent start-stop operation not only exacerbates wear and tear on the air conditioning system but also results in significant 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 to solve the problem in the prior art that the temperature adjustment of the air conditioning group is inaccurate and the frequent starting and stopping requires 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, comprising an air-conditioning group for regulating the temperature of an indoor place and a plurality of pipelines extending from the air-conditioning group; the air-conditioning group comprises 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 provided in the pipeline, each of the branches is connected to a different position in the indoor place; a diffuser is provided 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 energy efficiency pre-adjustment is performed, the air-conditioning group corrects the predicted future flow of people 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 set as 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 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 set to the same; the first angle plate and the second angle plate are assembled by splicing them 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 future energy consumption needs;

[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 future energy consumption demand based on future pedestrian flow, building heat conduction characteristics, and energy consumption impact data caused by loss of air conditioning gas during transportation within the air duct; the future pedestrian flow is predicted using a pedestrian 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 consumption 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 increase due to heat conduction of the building; Q 管 The energy consumption demand caused by the loss of air conditioning gas in the air duct (123) during transportation; wherein, the Q 人′ =N×q; N is the impact of 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 coefficient; q t is the passenger flow at time t predicted at the previous moment of 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 requirements caused by heat loss due to the material of the air duct, and the energy consumption requirements caused by air leakage loss due to 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 status of the air-conditioning group; an acquisition module: collects real-time energy consumption data and past energy consumption data of the central air-conditioning; a prediction module: predicts future energy consumption data through real-time energy consumption data and past energy consumption data; a comparison module: compares the output energy consumption of the central air-conditioning with the predicted future demand energy consumption; a judgment module: determines the working mode or status of the air-conditioning group according to the comparison data of the comparison module; wherein, the control module, acquisition module, prediction module, comparison module and judgment 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 power consumption provided by the present invention has the following beneficial effects:

[0024] In this application, the air conditioning's conditioning effects are distributed to different locations by configuring different branch pipes. Energy consumption requirements are adjusted through the energy efficiency management system's regulation methods and the duct structure. Furthermore, through data collection, intelligent prediction, and real-time adjustments, the mall's central air conditioning system can predict and adapt temperature requirements in advance, depending on the season, weather, date, time of day, and foot traffic, achieving the dual goals of energy conservation and an enhanced customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work, and these are all within the scope of protection 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 This is a schematic diagram of part of the air duct structure;

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

[0029] Figure 4 is a structural diagram of the second corner plate;

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

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

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

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

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

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

[0036] Figure 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 solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments 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 actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the directions or positional relationships indicated by the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore should not be understood as limiting the present invention. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further limitations, elements defined by the phrase "comprising..." do not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the elements. The embodiments of the present invention and the features thereof may be combined with each other if there is no conflict, and all are within the scope of protection of the present invention.

[0044] Example 1

[0045] See Figure 1 The present invention provides an energy efficiency management system based on air conditioning electricity consumption. This system analyzes and predicts the energy consumption requirements of a particular indoor public space and dynamically adjusts the central air conditioning system's operating strategy, thereby minimizing energy consumption while ensuring a comfortable environment.

[0046] This energy efficiency management system is suitable for public spaces such as large shopping malls, office buildings, exhibition centers, and airport terminals, where central air conditioning systems are often a major source of energy consumption. By combining crowd flow prediction with temperature pre-adjustment, the system can effectively reduce air conditioning electricity costs while ensuring user comfort, resulting in significant energy-saving and environmental benefits. The information collected varies depending on the venue.

[0047] See Figure 1In this embodiment, an energy efficiency management system based on air conditioning electricity consumption is provided. This energy efficiency management system based on air conditioning electricity consumption is intended 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 plurality 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 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] Each air conditioning group, or all sub-air conditioners, are managed by a central controller 200. Controller 200 communicates with the group via a telecommunications connection, monitoring each sub-air conditioner's operating status, temperature settings, and energy consumption in real time. It automatically adjusts the operating status of each sub-air conditioner based on the actual indoor location and pre-set energy-saving strategies. Controller 200 can also receive external input, such as energy demand, weather data, and pedestrian flow forecasts, to more precisely control the operation of the air conditioning system.

[0049] See Figure 10 The controller 200 is controlled by a PLC. The PLC control panel is equipped with several infrared sensors, monitors, and other monitoring devices to monitor energy consumption requirements in different operating environments. It also includes temperature sensors for monitoring indoor and outdoor temperatures, the air duct 123, and the building's temperature. It also includes several control elements and status indicators. Various components, such as water pumps, supply fans, return fans, and exhaust fans, are connected via frequency converters. It also includes several actuators.

[0050] For further information, see Figure 2 The air conditioning unit delivers cooling or heating air to various areas of the room through a number of air ducts 123, ensuring efficient and energy-efficient air circulation. The air ducts 123 are connected by flanges 130 and are fitted with gaskets 133. This connection ensures the tightness of the ducts 120 and facilitates their installation and maintenance. The design of the duct 120 system fully considers the optimization of air flow to reduce energy loss and improve transmission efficiency. Figure 8 The flange 130 is composed of multiple angle steels 131, and the angle steels 131 are fixed to each other through four corner fixing pieces 132.

[0051] See 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 regional function, 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 branches out into several branches in order to deliver air to different areas or rooms. In order to achieve this branch delivery while ensuring the efficiency and directionality of the air flow, specific pipe accessories are needed to change the direction of the air duct. In this case, the elbow 121 can enable the main air duct to change direction smoothly during the delivery 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 delivery 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 Figure 1 and Figure 10 The controller 200 integrates a control module and a prediction module. This module pre-adjusts the central air conditioning system's operating conditions by predicting future energy demand. The prediction module also predicts the future flow of people in indoor spaces within a specific time period or time based on analysis of historical data (such as seasonal changes in traffic, weather conditions, and special events). This can be accurate to hourly or even shorter time intervals. Based on the impact of predicted traffic flow data on cooling demand, the controller 200 determines the number of people and energy efficiency requirements. The controller then pre-adjusts the operating parameters or mode of the air conditioning system, such as activating more sub-air conditioning units 110 to increase cooling or heating output, or appropriately shutting down some sub-air conditioning units to save energy. This pre-adjustment allows the system to adjust the indoor temperature to an appropriate level based on the expected cooling demand of incoming traffic, ensuring comfort within the space. To improve system response speed and accuracy, the controller 200 is equipped with a real-time monitoring component that acquires current actual traffic flow data in real time using various sensors installed within the space (such as infrared sensors and cameras). These sensors can accurately detect the number, density, and distribution of people in a venue. The real-time monitoring data will be compared and analyzed with previous predictions.

[0054] The system in this embodiment combines crowd flow prediction, real-time monitoring, prediction correction, and energy efficiency matching and adjustment. This 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 Figure 11 In this embodiment, a PLC-based automatic control system for a central air conditioner is obtained. The system consists of two main parts and four links. The two main parts are the control device and the controlled object (the temperature of the indoor space). The four links mainly consist of a measuring element, a comparison element, an actuator, and the controlled object.

[0056] Measuring elements refer to components that measure output quantities. In this system, these include humidity sensors, infrared sensors, and monitors. Comparing elements compare the output quantity with a given value, generating an error signal that serves as a control signal for adjustment. Actuators, acting in response to the error signal, bring the controlled object closer to the target value. In this control system, the actuators represent the various components of the central air conditioning system. The controlled object refers to the equipment or target parameter controlled by the automatic control system. In this system, this refers to the temperature of the indoor space. Interference can originate from within or outside the control system, but it is always a negative factor that affects system control. Disturbances, contrary to system output, should be avoided as much as possible. Examples of such disturbances include changing outdoor weather conditions and changes in indoor personnel.

[0057] See Figure 2 and Figure 3 The air duct 123 is an important component in the central air-conditioning system, 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 duct. 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 4The 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 with a simple and practical structure, which 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 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 splicing them end to end. 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 any gaps, thereby preventing air leakage, reducing the amount of 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 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 loosened by external forces. 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 this composite board is generally 0.2-0.4W / m 2·℃, and has very excellent thermal insulation properties. When used in air conditioning systems, this material can greatly reduce heat transfer, thereby reducing air volume loss. Therefore, when used in air conditioning systems, it can effectively maintain the temperature of cold or heated air, reducing the energy consumption of the air conditioning system. In addition, polyurethane composite panels also have excellent mechanical strength and durability, and can operate stably for a long time in various environments, ensuring the stable operation of the air conditioning system. 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 this energy efficiency management system, each branch line's end is precisely supplied with air through branch pipes 143. Diffusers 140 at the ends of branch pipes 143 not only evenly distribute air but also, through the design of regulating valves 141 and diffuser plates 142, control air volume and direction, further optimizing the central air conditioning system's energy efficiency and indoor comfort.

[0062] See Figure 5The branch pipe 143 is a key channel connecting the branch and the diffuser 140, and is responsible for delivering 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 must 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 air volume requirements of different areas. The regulating valve 141 is two trumpet-shaped openings. A plate is moved on the trumpet-shaped opening to control the size of the opening. The air output of the diffuser 140 can be adjusted to achieve local temperature or air volume control. At the end of the regulating valve 141 are several diffuser panels 142, tilted at a preset angle. The primary function of the diffuser panels 142 is to direct and diffuse the airflow, ensuring even distribution throughout the area. By controlling the tilt angle of the diffuser panels 142, the direction and spread of the airflow can be adjusted to meet the air circulation needs of different areas. For example, in large spaces, the diffuser panels 142 can be tilted to a larger angle to expand the airflow coverage; in smaller spaces, the diffuser panels 142 can be set to a smaller angle to focus the airflow and avoid air short-circuits or uneven airflow. The tilt angle of the diffuser panels 142 effectively optimizes airflow distribution, avoiding dead spots or excessive air concentration. Furthermore, the material and structural design of the diffuser panels 142 take noise control into consideration, ensuring optimal air distribution while minimizing the impact of wind noise on the indoor environment. As the indoor environment and the flow of people change, the system can dynamically adjust the airflow distribution pattern by adjusting the opening of the diffuser 140 and the angle of the diffuser panels 142. This dynamic adaptability enables the system to maintain efficient operation in a variety of usage scenarios, meeting air demand during peak hours while saving energy during off-peak hours.

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

[0064] 1. Data collection:

[0065] Various types of monitoring equipment are installed in various areas (entrances and exits) of indoor places (shopping malls), including infrared sensors, cameras, temperature sensors, humidity sensors, weather station interfaces, etc. These devices can collect pedestrian flow data in real time and accurately, as well as environmental information such as the current weather, season, and date. The mall's operating data includes historical temperature records, holiday information, promotional activities, etc. that are directly related to pedestrian flow. These external factors will be included in the data collection scope in order to produce a more accurate prediction of pedestrian flow. 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 have been optimized to ensure that big data analysis and fast query can be carried out efficiently.

[0066] 2. Data processing:

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

[0068] 3. Feature extraction:

[0069] After data processing is complete, controller 200 further analyzes the data to extract key features that influence air conditioning temperature regulation. These features include, but are not limited to, crowd density, outdoor temperature, current weather (e.g., sunny, rainy, cloudy, snowy), season (e.g., winter, summer, autumn, winter), and time of day (e.g., weekday, non-workday). Using machine learning algorithms, the system automatically identifies and weights the impact of different features on air conditioning load, establishing a model for calculating crowd flow.

[0070] 4. Crowd flow prediction:

[0071] Prediction Model: Controller 200 uses the processed data, after feature extraction, as input into a prediction model to predict passenger flow. This prediction model uses a passenger flow prediction formula derived from regularities, trends, and cyclical changes in historical data to predict the likely passenger flow within a specific future time period. After calculation, the system outputs passenger flow predictions for a series of future time points. These predictions not only reflect overall passenger flow trends but can also be broken down to specific time intervals, providing a precise basis for energy efficiency management of central air conditioners.

[0072] 5. Pre-conditioning:

[0073] Based on the predicted pedestrian flow data, the controller 200 will automatically generate a corresponding energy efficiency management plan. This plan will comprehensively consider factors such as the prediction results, the current indoor and outdoor temperatures, and the mall's operating hours to determine the operating strategy of the air-conditioning group in the future time period. For example, during expected peak traffic periods, 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 expected low traffic periods, 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 changes in pedestrian flow through precise control of air volume and refrigerant flow. This pre-adjustment can ensure that each area in the mall always maintains a suitable temperature 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. This embodiment of an energy efficiency management method based on air conditioning power consumption is based on the system implementation in Example 1.

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

[0077] The air conditioning system sets initial temperature, wind speed, and mode parameters based on the building type, historical energy consumption data, and current environmental conditions to ensure efficient system startup. Typically, in a medium-sized shopping mall, when the number of people is low at the beginning of business, the air conditioning system enters an initial operating state, controlling the indoor temperature at a comfortable 26°C. At this point, the air conditioning system has an initial output energy consumption.

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

[0079] Central air conditioning uses sensors and monitors to collect real-time data on the air conditioning unit's current power consumption, the temperature difference between inside and outside the public space, current foot traffic, and equipment operating status. This data is then used to dynamically predict future energy demand by estimating the increased energy consumption caused by temperature changes due to these factors, based on foot traffic prediction models, the building's thermal conductivity characteristics, and the impact of energy losses caused by air conditioning gas transported within the ducts.

[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 impact of heat on energy consumption caused by the flow of people, the heat conduction characteristics of the building, and the loss of heat caused by the transmission of the air duct 123 and the resulting temperature rise is taken into consideration.

[0087] Therefore, according to Q 总 =Q1+Q2+……+Q N The energy consumption 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 t+1 moment 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 time; 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 between the two 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 not, a matching adjustment is made.

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

[0090] Q 人 Energy consumption demand due to temperature rise caused by human traffic; Q 人′ The energy consumption 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 typically includes sensible heat (heat dissipated directly to the surrounding environment through the skin) and latent heat (heat dissipated by the human body through evaporation). Based on experience, for an adult engaging in light activity (such as walking around the mall or shopping), the total heat dissipated by each person contributes approximately 100W to 150W of energy consumption. For example, light activity (such as walking and strolling) contributes approximately 100W of heat dissipated per person per hour to energy consumption. Moderate activity (such as frequent walking and moving between floors) contributes approximately 150W of heat dissipated per person per hour to energy consumption.

[0092] Q 物 Energy consumption demand caused by the temperature increase 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, future indoor temperature and outdoor temperature are obtained by prediction based on real-time temperature or weather forecast.

[0095] Q 管 The energy consumption demand caused by the loss of air conditioning gas inside the air duct 123 during transportation; Q 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 0.2-0.4W / m 2 ℃, take the middle value 0.3W / m when calculating 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. Therefore, 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, the air leakage of the air duct 123 is about 0.1m in practice when the structure in Example 1 is adopted. 3 / s. The air leakage is estimated by the possible leakage area and the sealing degree. ρ is the air density, the unit is kg / m 3 , the typical density of air is 1.2kg / m 3 ; C p The specific heat capacity of air is expressed in J / (kg·°C), and the specific heat capacity of air is approximately 1005 J / (kg·°C); ΔT is the temperature change between the inside and outside of the air duct 123, and the unit is °C.

[0097] Furthermore, the crowd flow prediction model is: t+1 =q t +B T ×u t+1 +K T (q T′ -q T );the qT+1 is the passenger flow at the next moment predicted based on time 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 the control input. The control input factors include external factors such as season, weather, whether it is a weekday, and whether there is an event. Due to the uncertainty of activities, the deviation required to correct the flow of people through actual observation is large. Therefore, season, weather, and whether it is a weekday are selected as the control input matrix and their weights are assigned accordingly: According to statistics, the season, weather, and whether it is a weekday have the greatest impact on pedestrian flow. Weights are assigned based on their impact on pedestrian 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 effects 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 weather that is inconvenient to go out; whether it is a working day is divided into working day and non-working day, and the influence coefficients of working day and non-working day 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 A control input having an effect on the number of people is obtained.

[0101] In this system, the weights and influence coefficients of control inputs are determined using Bayesian regression. Specifically, this process involves analyzing and processing historical mall data using a Bayesian regression model. This method effectively extracts the weights and influence coefficients of control inputs from historical data, providing a scientific basis for system optimization and decision-making. This approach not only fully utilizes the value of historical data but also improves the accuracy and reliability of parameter estimation through the statistical properties 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. Therefore, a larger value is set: 10.

[0104] Furthermore, the P t The update formula is P new =(1-K t )P old ;P old is the last value, P new The value of the next calculation; K t is the last calibration coefficient. For example, when the first P 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 to calculate 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 of 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 projected energy demand and the current central air conditioning system output exceeds 2.2kW, the controller will control more sub-air conditioning units to participate in operation 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] Conversely, if the difference between the projected energy demand and the central air conditioning system's output is less than -2.2kW, this means the current air conditioning output exceeds actual demand, and the system will shut down some operating sub-units. This operation helps reduce unnecessary energy consumption, thereby saving energy and reducing emissions, achieving energy efficiency. Air conditioning electricity consumes a significant amount of energy costs. By setting energy consumption limits, the system can reduce electricity costs without compromising comfort. This is particularly important in locations where air conditioning is required for extended periods, significantly reducing electricity bills and saving energy.

[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 not only allows customers to always be in a suitable environment, but also avoids 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 projected energy demand and the central air conditioning system's output energy consumption falls between 2.2kW and 2.2kW, the system maintains the existing AC unit's output energy consumption unchanged and continues operating at its current state. In this scenario, the system's energy supply and demand are in a relatively balanced state, eliminating the need for additional adjustments. This intelligent regulation mechanism ensures efficient operation of the air conditioning system, avoiding the energy waste associated with frequent activation of AC units and providing users with a comfortable and stable indoor environment.

[0112] Example 3

[0113] The embodiment of the present invention provides a case study on 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 consumption demand affected by the flow of people at the future moment, the energy consumption demand required for cooling the heat generated by heat conduction between buildings, and the energy consumption demand required for cooling the heat generated by equipment during working hours. Therefore, Q t+1 =Q 人′ +Q物 +Q 管 .

[0114] In an actual application case, a shopping mall is equipped with 20 sub-air conditioner units with an output power of 50 kW. It is assumed that the shopping mall is on a sunny weekday in summer 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 passenger flow at the previous moment t at time t is approximately 2,000 people, and the passenger flow monitored by the monitor and sensor at time t is approximately 2,100 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 conditioner 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 number of people at time t is 2000, the number of people detected by the monitor and sensor at time t is about 2100, and it is a sunny day on a summer weekday. 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 314.8 kW, far exceeding the 2.2 kW range. Furthermore, the output power at time t also far exceeds the predicted output power required at time t+1. Therefore, it is necessary to shut down some of the operating sub-AC units 110 and adjust the operation of the corresponding eight sub-AC units 110 to maintain a suitable temperature in the mall. This also avoids excessively high energy output from the central AC unit, which would otherwise waste 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. Based on the predicted passenger flow 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 )P oldGet: P new ≈(1-0.874)10≈1.3; P new Substitution Calculate in the middle and 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 on the flow of people in indoor places, current weather, current season, and current date.

[0132] This memory not only stores the instruction sets required for controller execution but also has the ability to collect and store critical energy consumption information. Specifically, it is designed to record and store real-time energy consumption data for indoor locations, reflecting the current energy consumption status of the location. Furthermore, it can store historical energy consumption data, which records the energy usage of the location over a period of time, providing a historical reference for energy consumption analysis and optimization.

[0133] In addition to energy consumption data, the storage device also records indoor user traffic. This means it can monitor and store real-time data on user traffic within a venue. It can also collect and store real-time environmental data, including current weather conditions, seasonal information, and the current date.

[0134] The storage device also has the ability to store historical environmental data, including historical weather conditions, seasonal changes, and dates. This historical data supports long-term energy consumption trend analysis, environmental impact assessments, and the development of more precise energy management plans. By integrating real-time and historical data, the storage device can help users achieve more efficient and intelligent energy management.

[0135] Example 5

[0136] See Figure 9The 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 circulation back to the compressor and evaporator. The oil separator and the compressor are divided into two sets; each set of oil separators and compressors is connected to the compressor through a high-pressure gas pipe; and the pipe extends through the oil separator to the condenser. The compressor is connected to the lubricating oil pipe through a valve port consisting of a solenoid valve, an oil filter, a liquid test mirror, and a manual shut-off valve. The main function of this valve port is to control and manage the flow of lubricating oil to ensure that the compressor is properly lubricated and thus ensure its normal operation. The condenser is also connected to the heat exchanger through a high-pressure liquid pipe. The high-pressure liquid pipe and the heat exchanger are connected and equipped with a battery valve, a drying filter, and a liquid sight glass. The connection between the evaporator and the heat exchanger is also equipped with the same valve port as the high-pressure liquid pipe and the heat exchanger. The function of this 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 and achieve the desired cooling effect. This operating principle also includes pipes 120 with different functions, such as a return pipe, a pressure equalizing pipe, and a make-up pipe. The return pipe in an air conditioning system primarily serves to direct refrigerant gas from the evaporator back to the compressor. During the refrigeration cycle, the refrigerant absorbs heat in the evaporator, transforming from liquid to gas. It then returns to the compressor through the return pipe for the next cycle. The return pipe is typically connected between the evaporator outlet and the compressor inlet, ensuring a smooth return of refrigerant gas to the compressor. The pressure equalizing pipe's primary function in an air conditioning system is to balance the pressure within the system, ensuring a stable pressure during the refrigerant cycle. This helps prevent system efficiency degradation or damage caused by uneven pressure. The pressure equalizing pipe is typically connected between the high-pressure and low-pressure sides of the system to balance the pressure on both sides. The make-up pipe is used in some air conditioning systems to replenish refrigerant, ensuring the refrigerant level within the system is maintained at an appropriate level. When the refrigerant level in the system is low, new refrigerant can be added through the make-up pipe. The make-up pipe is typically connected to the low-pressure side of the system to allow refrigerant to be added when needed.

[0137] The refrigerant enters the compressor as a low-pressure gas. The compressor compresses the low-pressure gas into a high-pressure, high-temperature gas, which is then fed into the oil separator. During the compression process, the refrigerant carries some lubricating oil. The oil separator separates the oil from the refrigerant to ensure continuous lubrication of the compressor while allowing pure, high-pressure gas to enter the condenser. The high-pressure, high-temperature refrigerant gas enters the condenser, where it releases heat to the surrounding environment through heat exchange (usually via air or water cooling). During this process, the refrigerant cools from the high-pressure gas and condenses into a high-pressure liquid. Some systems may include a heat exchanger between the condenser and evaporator to improve system efficiency. In the heat exchanger, the high-pressure liquid refrigerant may exchange heat with the low-pressure gas refrigerant, further lowering the refrigerant temperature and enhancing condensation or evaporation. Once the high-pressure liquid refrigerant enters the evaporator, the reduced pressure causes it to rapidly evaporate, absorbing heat from the surrounding environment and achieving a cooling effect. The refrigerant then returns to a low-pressure gas. The low-pressure gas refrigerant then returns to the compressor, beginning the cycle anew. Through this cycle, the central air conditioning system can provide cooling or heating in the interior space (indoor places). This system can achieve efficient indoor temperature regulation through effective heat exchange and energy transfer.

[0138] Example 6

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

[0140] The control module is used to adjust and control the operating mode or status of the central air conditioning system. This module's primary function is to regulate and control the central air conditioning system's operating mode or status, ensuring efficient operation based on environmental requirements and energy-saving goals. It can automatically adjust the system's operating mode, such as temperature setting, fan speed adjustment, and compressor start and stop, based on user-defined parameters to achieve optimal comfort and energy efficiency.

[0141] The Collection Module collects real-time and historical energy consumption data for central air conditioners. This module is responsible for collecting real-time energy consumption data for the central air conditioning system, including but not limited to key indicators such as power consumption, refrigerant flow, and compressor operating time. It also records historical energy consumption data, providing basic data support for subsequent energy consumption analysis and forecasting. This long-term data accumulation can help analyze system energy consumption trends and cyclical changes.

[0142] Prediction Module: This module uses real-time and historical energy consumption data to predict future energy consumption. Using the real-time and historical energy consumption data provided by the acquisition module, the prediction module uses the prediction method described in Example 2 to predict energy consumption over a period of time. These predictions can help the system make proactive adjustments and optimize operational strategies to achieve energy conservation goals.

[0143] Comparison Module: Compares the central air conditioning system's output energy consumption with predicted future energy consumption. This module compares and analyzes the central air conditioning system's real-time output energy consumption with the future energy consumption demand provided by the prediction module. This comparison allows assessment of system efficiency and energy consumption, identifying anomalies promptly and providing a basis for subsequent optimization and adjustment.

[0144] The Decision Module determines the central air conditioning system's operating mode or status based on the comparison data from the Comparison Module. Based on the comparison module's analysis, the Decision Module makes a decision to determine the optimal operating mode or status for the central air conditioning system. This may include adjusting temperature settings, changing operating modes, and optimizing compressor start / stop strategies to ensure the system maximizes energy efficiency while meeting user needs.

[0145] The entire system's workflow is continuous and cyclical at every moment. The control module, acquisition module, prediction module, comparison module, and judgment module operate sequentially in a predetermined sequence, forming a closed-loop control system. The prediction module uses data captured by integrated sensors and monitors to accurately predict the system's performance, ensuring that predictions and adjustments are based on real-time, accurate information, thereby enabling 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, wherein the method described in Example 2 is implemented when the computer program instructions are executed by the processor.

[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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above 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 pipes (120) extending from the air conditioning unit; characterized in that: The air conditioning group includes a plurality of sub-air conditioning units (110); all 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 branch being connected to a different location in the indoor space; 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; the controller (200) adjusts the matching between the corrected flow of people and the energy efficiency of the air-conditioning group; the energy efficiency management system includes the following adjustment steps S1 to S4: S1: Set the initial working state of the air conditioning group; S2: Collect real-time energy consumption data and predict future energy consumption needs; Wherein, the S2 includes predicting the future energy consumption demand based on the future passenger flow, the heat conduction 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 (123); the future passenger flow is predicted by a passenger flow prediction model; the energy consumption demand at the future moment is: ; Among them, the for Energy consumption requirements at all times; Energy consumption due to temperature rise caused by predicted passenger flow; Energy consumption demand due to temperature rise caused by heat conduction in buildings; The energy consumption demand caused by the loss of air conditioning gas in the air duct (123) during transportation; Among them, the ; the impact of heat generated per person on energy consumption; is the number of people flowing in the future; the number of people flowing in the future is predicted by a people flow prediction model; The crowd flow prediction model is: ; Among them, the is the passenger flow at the next moment predicted based on time t; is the weight matrix of the control input, For The influence coefficient matrix of the moment control input; is the correction factor; is the passenger flow at time t predicted at the previous moment of t; is the flow of people observed by the monitor or sensor at time t; The weight matrix B of the control input is: ， The influence coefficient matrix of the control input at time t+1 for: ， 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.

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 a snap-in end (126) and a snap-in end (127); the snap-in end (126) is provided in a flat plate; the snap-in end (127) is formed 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 provided at the end of the branch pipe (143); a regulating valve (141) is provided at the opening of the diffuser (140) for regulating the air volume; a plurality of diffuser plates (142) are provided at the end of the regulating valve (141); and the plurality of diffuser plates (142) are inclined at a preset angle.

4. The energy efficiency management system based on air conditioning electricity consumption according to claim 1, characterized in that: described This includes the energy consumption requirements generated by heat loss caused by the material of the air duct (123), and the energy consumption requirements generated by air leakage loss caused by the connection structure of the air duct (123).

5. The energy efficiency management system based on air conditioning electricity consumption according to claim 1, 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 the 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 for operation.

6. The energy efficiency management system based on air conditioning electricity consumption according to claim 1, characterized in that: described and 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; The spring, summer, autumn, winter, working days, non-working days, sunny, cloudy, rainy and snowy seasons have different impact weights and coefficients respectively.

7. 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 unit; Collection module: collects real-time energy consumption data and past energy consumption data of central air conditioners; Prediction module: predicts future energy consumption data through real-time energy consumption data and past energy consumption data; Comparison module: compares the output energy consumption of central air conditioners with the predicted future energy consumption; Determination module: determines the working mode or status of the air conditioning group according to the comparison data of the comparison module; The control module, acquisition module, prediction module, comparison module and determination module work in sequence and in a reciprocating cycle; The prediction module performs prediction based on data acquired by sensors and monitors.

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