Adaptive load based air conditioner energy consumption management system and method

Abstract

The application discloses an air conditioner energy consumption management system and method based on adaptive load, relates to the technical field of energy consumption management, and collects outdoor temperature, solar radiation intensity, total power consumption of an air conditioning system and real-time number of people in each partition and the like through arrangement of building external environment sensors and internal environment sensors, integrates the data into a unified vector. Based on the vector, a comprehensive heat load index and a real-time energy efficiency ratio of the system are calculated to form a situation image reflecting the operation state of the system. Further, according to the situation image, an energy efficiency deviation rate and a load-power consumption consistency index are calculated, the system state is diagnosed in real time through a preset threshold value, including normal, slight energy efficiency abnormality, serious energy efficiency abnormality, load-power consumption mismatch and composite abnormality. Finally, corresponding control strategies are executed according to the diagnostic results. The method can dynamically adapt to changes in building heat load, realize real-time energy efficiency monitoring and intelligent optimization of the air conditioning system, and improve energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, specifically to an air conditioning energy management system and method based on adaptive load. Background Technology

[0002] As building energy consumption accounts for a growing proportion of total social energy consumption, the energy efficiency management of air conditioning systems, as a major energy-consuming device in buildings, has become an important research direction in the field of energy conservation. Traditional air conditioning energy management relies heavily on fixed temperature setpoints or timetables, lacking the ability to dynamically respond to changes in the building's internal and external environment, often resulting in energy waste during actual operation.

[0003] Existing technologies include air conditioning control methods based on sensor data, such as adjusting the air conditioning operation status through indoor temperature and humidity feedback. These methods often consider only a single environmental parameter and fail to fully reflect the complexity of a building. For example, the impact of dynamic factors such as solar radiation intensity and changes in occupant density on the air conditioning load is often ignored or simplified, making it difficult for the system to accurately match actual needs during operation and resulting in low energy efficiency.

[0004] Existing energy consumption assessment methods mostly focus on the statistics of total power consumption, lacking dynamic monitoring and analysis of the system's real-time energy efficiency ratio. During operation, air conditioning systems may experience abnormal states such as energy efficiency degradation and load-power mismatch. Traditional methods struggle to identify these problems in a timely manner, making it impossible to perform accurate state diagnosis and strategy adjustments, which can easily lead to the system operating in a suboptimal state for extended periods. Summary of the Invention

[0005] The purpose of this invention is to provide an air conditioning energy consumption management system and method based on adaptive load, so as to solve the problems raised in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an air conditioning energy consumption management method based on adaptive load, the method comprising the following steps: Step S100: Deploy external and internal environmental sensors to collect natural environmental parameters and the actual environmental status inside the building. Monitor the total power consumption of the air conditioning system through smart meters and use the access control system to count the real-time number of people in different areas. Step S100 includes the following steps: Step S101: Install external and internal environmental sensors for the building. The external environmental sensors are used to sense natural environmental parameters outside the building, including solar radiation sensors. The internal environmental sensors sense the actual environmental conditions of each functional area inside the building, including indoor temperature and humidity sensors. The outdoor temperature and humidity sensor measures the outdoor atmospheric temperature and relative humidity, with the corresponding core parameters being outdoor temperature T. out The unit is °C; outdoor temperature is the core variable for calculating the heat transfer load of a building structure. When the outdoor temperature is higher than the indoor set temperature, heat will be transferred into the room through the building structure, increasing the air conditioning cooling load. The solar radiation sensor is used to measure the total solar radiation energy received per unit area and to measure the solar radiation intensity I. solar The unit is W / m². Solar radiation is the heat that enters the room through the glass curtain walls, roof, and other parts of the building, which can significantly increase the air conditioning cooling load or reduce the heating load. The accuracy of this data determines the calculation accuracy of the solar radiation impact item in the subsequent comprehensive heat load index, and avoids load estimation deviations caused by ignoring solar heat gain. Step S102: Set up sensors to monitor the energy consumption and operating status of the core equipment of the air conditioning system. Specifically, set up smart meters to measure the total power consumption of the air conditioning system and monitor the total power consumption P of the air conditioning system. total Unit: kW; This data is the most direct indicator for assessing the energy consumption level of air conditioning systems; The air conditioning work area is divided into different zones using an access control system. The number of people entering the zone is recorded by swiping cards at the access control system. Real-time person counters are built for each zone, and the real-time number of people in each zone is output. Where i represents the partition index, corresponding to the i-th functional partition. Through multi-source data collection, it provides the system with a comprehensive and real-time environmental and operational data foundation, ensuring the accuracy and reliability of subsequent analysis and decision-making.

[0007] Step S200: Integrate the collected data into a unified vector, including outdoor temperature, solar radiation intensity, total air conditioning power consumption and average number of people in the area, calculate the comprehensive heat load index and the real-time energy efficiency ratio of the system, and form a situation profile composed of the heat load index and energy efficiency ratio. Step S200 includes the following steps: Step S201: The raw data collected in step S100 is integrated into a vector and iterated over at a fixed time period Δt. Each period corresponds to a data acquisition and processing node, denoted as time t. At time t, the mathematical expression of the raw data vector is: ; In the formula, It is the original data vector. The outdoor temperature at time t. It is the total solar radiation intensity at time t. It is the average population of the region, expressed as the real-time population of each sub-region at time t. The average is obtained. This represents the total power consumption of the air conditioning system at time t. Step S202: The original data vector is fused into two high-level features, namely the system heat load level and the system energy efficiency level; The system heat load level is quantified using the comprehensive heat load index L(t): The comprehensive heat load index L(t) is the equivalent temperature difference reflecting the total effect of all thermal disturbances inside and outside the building. L(t) integrates three independent heat load sources—outdoor temperature, solar radiation, and heat dissipation from people—into a single index with weighting coefficients. ; In the formula, T reflects the contribution of outdoor temperature to the heat load. ref It is the building's interior design temperature, ΔT std This is the reference temperature, set by professionals; w1 is the weighting coefficient, set by professionals. Reflecting the contribution of solar radiation to the heat load, I std This is the standard solar radiation intensity, and w2 is a weighting coefficient set by professionals. N reflects the contribution of personnel heat dissipation to the heat load. max It is the maximum capacity of the building design, w3 is the weighting coefficient, which is set by professionals, w1+w2+w3=1; Step S203: Use the system's real-time energy efficiency ratio The system's energy efficiency level is quantified, and the real-time energy efficiency ratio reflects the system's conversion efficiency from input electrical energy to output cooling capacity. ; In the formula, This is the total power consumption of the air conditioning system at time t, in kW. This refers to the total cooling capacity of the air conditioning system, measured in kW. ; In the formula, c P ρ is the specific heat capacity of water at constant pressure, and ρ is the density of water. It is the air conditioner's chilled water flow rate. It is the difference between the temperature of the cold water entering the air conditioner heat exchanger and the temperature of the cold water flowing out of the air conditioner heat exchanger. The physical logic of this formula is: heat absorbed by cold water = mass of water × specific heat capacity × temperature change, and "heat absorbed per unit time" is the cooling capacity. Step S204: Integrate the comprehensive heat load index and the real-time energy efficiency ratio of the system to form a situation profile P(t), wherein the situation profile P(t) is an ordered set: ; By combining feature fusion and situational awareness construction, complex and multidimensional data is simplified into interpretable indicators, improving the visualization and analyzability of system status. The resulting situational profile serves as a crucial bridge connecting "data acquisition" and "intelligent decision-making," ensuring the accuracy and logic of subsequent decisions.

[0008] Step S300: Based on the situation profile, calculate the energy efficiency deviation rate and load-power consumption consistency index, and diagnose the system status in real time according to the preset threshold, including normal, slightly abnormal energy efficiency, severely abnormal energy efficiency, load-power consumption mismatch and compound abnormality. Step S300 includes the following steps: Step S301: Based on situational profile Calculate the metrics used for system evaluation, including energy efficiency deviation rate and load-power consistency index; The energy efficiency deviation rate Used to measure the deviation between current energy efficiency and expected energy efficiency: ; In the formula, COP base It uses the rated energy efficiency ratio of the air conditioning system under design conditions; The load-power consistency index CI(t) assesses whether the system power consumption matches the current building heat load. ; In the formula, P rated It is the rated total power of the air conditioning system, L max Design the air conditioner to the maximum heat load; Step S302: Classify the load-power consistency index and energy efficiency deviation rate in real time based on thresholds. Through real-time diagnosis and status classification, system anomalies and energy efficiency degradation can be detected in a timely manner, providing a clear basis for subsequent strategy execution and avoiding energy waste and equipment damage. when When this occurs, it is considered a normal state; when When this occurs, it is determined to be an abnormal energy efficiency state; when When this occurs, it is determined to be a severely abnormal energy efficiency state; when When this occurs, it is determined to be a load-power mismatch state; when and When this occurs, it is determined to be a complex abnormal state; a1 and a2 are energy efficiency judgment thresholds, a1 < a2; b1 and b2 are load-power consumption judgment thresholds, b1 < 1 < b2; all thresholds are set by professionals.

[0009] Step S400: Implement the corresponding strategy based on the diagnostic results.

[0010] The specific strategy executed in step S400 based on the diagnostic results is as follows: If the diagnosis indicates a normal state, maintain the current operating parameters of the air conditioning system. If a minor energy efficiency abnormality is diagnosed, a parameter optimization strategy is executed, which includes automatically adjusting the operating setpoints or operating modes of the air conditioning system. If a serious energy efficiency anomaly is diagnosed, a system intervention strategy will be implemented. The system intervention strategy includes generating alarm information and sending it to maintenance personnel, reducing the load on the air conditioning system and switching to a safe operating mode. If a load-power mismatch is diagnosed, a data verification and load tracking strategy is executed. The tracking strategy includes generating alarm information and sending it to maintenance personnel, verifying the validity of sensor data, and having maintenance personnel adjust the output power of the air conditioning system to match the current heat load. If a complex abnormal condition is diagnosed, the machine should be stopped immediately for inspection and repair.

[0011] An air conditioning energy management system based on adaptive load, comprising a data acquisition module, a data integration and feature extraction module, a status diagnosis module, and a strategy execution module; The data acquisition module collects raw data such as external environmental parameters, internal environmental status, air conditioning system power consumption, and real-time number of people in each zone by deploying sensors and monitoring equipment. Through modular design, it realizes closed-loop management of data acquisition, processing, diagnosis and execution, ensuring that the system responds quickly and reliably. The data integration and feature extraction module integrates the collected raw data into a unified vector, calculates the comprehensive heat load index and the real-time energy efficiency ratio of the system, forms a situation profile, and provides advanced features for system status diagnosis. The status diagnosis module calculates the energy efficiency deviation rate and load-power consistency index based on the situation profile, and diagnoses the system status in real time according to preset thresholds, including normal, slightly abnormal energy efficiency, severely abnormal energy efficiency, and load-power mismatch. The strategy execution module automatically triggers and executes corresponding energy consumption management strategies based on the output of the status diagnosis module. It directly acts on the air conditioning system to make adaptive adjustments for different system anomalies or optimization needs, so as to achieve optimized control of the energy consumption of the air conditioning system and ensure that the system operates in an efficient and matched state. As the decision-making end of the entire management process, the strategy execution module transforms the analysis and diagnosis results of the preceding modules into specific control actions.

[0012] The data acquisition module includes a sensor deployment unit and an energy consumption and personnel monitoring unit; The sensor arrangement unit arranges external and internal environmental sensors for sensing natural environmental parameters such as outdoor temperature and solar radiation intensity, as well as the actual environmental status of each functional area inside the building. The energy consumption and personnel monitoring unit monitors the total power consumption of the air conditioning system through a smart meter and counts the real-time number of people in each zone through the access control system, thereby achieving real-time collection of energy consumption and personnel data.

[0013] The data integration and feature extraction module includes a data vectorization unit, a heat load quantization unit, and an energy efficiency quantization unit. The data vectorization unit integrates raw data from different sources and with different dimensions, including outdoor temperature, solar radiation intensity, total air conditioning power consumption, and average number of people in the area, into a unified raw data vector according to a fixed time period. The heat load quantification unit constructs a comprehensive heat load index, which generates a comprehensive index that characterizes the total heat load level currently borne by the building by weighting and integrating three influencing factors: outdoor meteorological conditions, solar radiation, and heat dissipation from indoor occupants. The system energy efficiency quantification unit calculates the real-time energy efficiency ratio, which is the ratio of the system's output cooling capacity to the total input electrical power, to intuitively reflect the immediate efficiency of the air conditioning unit and distribution system in converting electrical energy into cooling capacity.

[0014] The status diagnosis module includes an evaluation index calculation unit and a status diagnosis unit; The evaluation index calculation unit calculates the energy efficiency deviation rate and the load-power consumption consistency index, which respectively measure the deviation between the current energy efficiency and the expected energy efficiency and the degree of matching between the system power consumption and the heat load. The status diagnosis unit performs real-time classification and diagnosis of the system status based on the thresholds of energy efficiency deviation rate and load-power consumption consistency index, and makes corresponding strategies.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. By monitoring the air conditioning system's operating data and environmental parameters in real time, a comprehensive heat load index and real-time energy efficiency ratio are constructed to form an accurate situation profile. Based on the energy efficiency deviation rate and load-power consumption consistency index, status diagnosis is performed to achieve dynamic optimization control. This system can promptly detect energy efficiency anomalies and load mismatches, avoid energy waste, improve the overall energy efficiency level of the air conditioning system, and reduce energy waste.

[0016] 2. Through multi-source data fusion and feature extraction, complex environmental and operational data are transformed into high-level features that enable decision-making. These features are then combined with preset thresholds for real-time status classification and strategy execution. The system possesses self-sensing, self-diagnosis, and self-adjustment capabilities, reducing reliance on manual intervention and achieving intelligent control and load tracking of the air conditioning system.

[0017] 3. By incorporating multi-dimensional variables such as outdoor temperature, solar radiation, and occupant distribution, a comprehensive heat load model is constructed, enabling the system to flexibly respond to varying conditions such as different seasons, weather, and number of users. It possesses excellent environmental adaptability and scalability, making it suitable for central air conditioning systems in various commercial, office, and public buildings.

[0018] 4. Through real-time diagnostics and multi-state classification, the system can promptly identify operational anomalies and trigger corresponding strategies, including parameter optimization, alarm prompts, safety mode switching, and even shutdown protection. This prevents risks such as equipment overload and sudden efficiency drops, extends equipment lifespan, and improves the safety and stability of system operation. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of the air conditioning energy consumption management method based on adaptive load of the present invention; Figure 2 This is a schematic diagram of the air conditioning energy consumption management system based on adaptive load according to the present invention; Figure 3 This is a flowchart illustrating the air conditioning energy consumption management system based on adaptive load according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example: Figures 1-3 As shown, this invention provides a technical solution, an air conditioning energy consumption management system and method based on adaptive load, taking a 10-story office area in an office building as the application scenario, dividing it into 3 functional zones, and setting T... ref =25℃, ΔT std =10℃, I std =1000W / m 2 N max =100 people, P rated =20kW, L max =1.0, COP base =5.5, Δt=5min, a1=5, a2=10, b1=0.8, b2=1.2, w1=0.5, w2=0.3, w3=0.2; Set to collect data at t=14:00: T out (t) = 32℃, I solar (t) = 800W / m 2 Ptotal (t) = 16 kW, F water (t) = 0.005m 3 / s, inlet and outlet water temperature difference ΔT water (t) = 4℃. The access control system is set to count the number of people in the three zones as 30, 25 and 25 people respectively, and the average number of people in the area is 26.67.

[0022] Step S100: Deploy external and internal environmental sensors to collect natural environmental parameters and the actual environmental status inside the building. Monitor the total power consumption of the air conditioning system through smart meters and use the access control system to count the real-time number of people in different areas. Step S100 includes the following steps: Step S101: Install external and internal environmental sensors for the building. The external environmental sensors are used to sense natural environmental parameters outside the building, including solar radiation sensors. The internal environmental sensors sense the actual environmental conditions of each functional area inside the building, including indoor temperature and humidity sensors. The outdoor temperature and humidity sensor measures the outdoor atmospheric temperature and relative humidity, with the corresponding core parameters being outdoor temperature T. out The unit is °C; outdoor temperature is the core variable for calculating the heat transfer load of a building structure. When the outdoor temperature is higher than the indoor set temperature, heat will be transferred into the room through the building structure, increasing the air conditioning cooling load. The solar radiation sensor is used to measure the total solar radiation energy received per unit area and to measure the solar radiation intensity I. solar The unit is W / m². Solar radiation is the heat that enters the room through the glass curtain walls, roof, and other parts of the building, which can significantly increase the air conditioning cooling load or reduce the heating load. The accuracy of this data determines the calculation accuracy of the solar radiation impact item in the subsequent comprehensive heat load index, and avoids load estimation deviations caused by ignoring solar heat gain. Step S102: Set up sensors to monitor the energy consumption and operating status of the core equipment of the air conditioning system. Specifically, set up smart meters to measure the total power consumption of the air conditioning system and monitor the total power consumption P of the air conditioning system. total Unit: kW; This data is the most direct indicator for assessing the energy consumption level of air conditioning systems; The air conditioning work area is divided into different zones using an access control system. The number of people entering the zone is recorded by swiping cards at the access control system. Real-time person counters are built for each zone, and the real-time number of people in each zone is output. Where i represents the partition index, corresponding to the i-th functional partition. Through multi-source data collection, it provides the system with a comprehensive and real-time environmental and operational data foundation, ensuring the accuracy and reliability of subsequent analysis and decision-making.

[0023] Step S200: Integrate the collected data into a unified vector, including outdoor temperature, solar radiation intensity, total air conditioning power consumption and average number of people in the area, calculate the comprehensive heat load index and the real-time energy efficiency ratio of the system, and form a situation profile composed of the heat load index and energy efficiency ratio. Step S200 includes the following steps: Step S201: The raw data collected in step S100 is integrated into a vector and iterated over at a fixed time period Δt. Each period corresponds to a data acquisition and processing node, denoted as time t. At time t, the mathematical expression of the raw data vector is: ; In the formula, It is the original data vector. The outdoor temperature at time t. It is the total solar radiation intensity at time t. It is the average population of the region, expressed as the real-time population of each sub-region at time t. The average is obtained. This represents the total power consumption of the air conditioning system at time t. Step S202: The original data vector is fused into two high-level features, namely the system heat load level and the system energy efficiency level; The system heat load level is quantified using the comprehensive heat load index L(t): The comprehensive heat load index L(t) is the equivalent temperature difference reflecting the total effect of all thermal disturbances inside and outside the building. L(t) integrates three independent heat load sources—outdoor temperature, solar radiation, and heat dissipation from people—into a single index with weighting coefficients. ; In the formula, T reflects the contribution of outdoor temperature to the heat load. ref It is the building's interior design temperature, ΔT std This is the reference temperature, set by professionals; w1 is the weighting coefficient, set by professionals. Reflecting the contribution of solar radiation to the heat load, I std This is the standard solar radiation intensity, and w2 is a weighting coefficient set by professionals. N reflects the contribution of personnel heat dissipation to the heat load. max It is the maximum capacity of the building design, w3 is the weighting coefficient, which is set by professionals, w1+w2+w3=1; Step S203: Use the system's real-time energy efficiency ratio The system's energy efficiency level is quantified, and the real-time energy efficiency ratio reflects the system's conversion efficiency from input electrical energy to output cooling capacity. ; In the formula, This is the total power consumption of the air conditioning system at time t, in kW. This refers to the total cooling capacity of the air conditioning system, measured in kW. ; In the formula, c P ρ is the specific heat capacity of water at constant pressure, and ρ is the density of water. It is the air conditioner's chilled water flow rate. It is the difference between the temperature of the cold water entering the air conditioner heat exchanger and the temperature of the cold water flowing out of the air conditioner heat exchanger. The physical logic of this formula is: heat absorbed by cold water = mass of water × specific heat capacity × temperature change, and "heat absorbed per unit time" is the cooling capacity. Step S204: Integrate the comprehensive heat load index and the real-time energy efficiency ratio of the system to form a situation profile P(t), wherein the situation profile P(t) is an ordered set: ; By combining feature fusion and situational awareness construction, complex and multidimensional data is simplified into interpretable indicators, improving the visualization and analyzability of system status. The resulting situational profile serves as a crucial bridge connecting "data acquisition" and "intelligent decision-making," ensuring the accuracy and logic of subsequent decisions.

[0024] Step S300: Based on the situation profile, calculate the energy efficiency deviation rate and load-power consumption consistency index, and diagnose the system status in real time according to the preset threshold, including normal, slightly abnormal energy efficiency, severely abnormal energy efficiency, load-power consumption mismatch and compound abnormality. Step S300 includes the following steps: Step S301: Based on situational profile Calculate the metrics used for system evaluation, including energy efficiency deviation rate and load-power consistency index; The energy efficiency deviation rate Used to measure the deviation between current energy efficiency and expected energy efficiency: ; In the formula, COP base It uses the rated energy efficiency ratio of the air conditioning system under design conditions; The load-power consistency index CI(t) assesses whether the system power consumption matches the current building heat load. ; In the formula, P rated It is the rated total power of the air conditioning system, L max Design the air conditioner to the maximum heat load; Step S302: Classify the load-power consistency index and energy efficiency deviation rate in real time based on thresholds. Through real-time diagnosis and status classification, system anomalies and energy efficiency degradation can be detected in a timely manner, providing a clear basis for subsequent strategy execution and avoiding energy waste and equipment damage. when When this occurs, it is considered a normal state; when When this occurs, it is determined to be an abnormal energy efficiency state; when When this occurs, it is determined to be a severely abnormal energy efficiency state; when When this occurs, it is determined to be a load-power mismatch state; when and When this occurs, it is determined to be a complex abnormal state; a1 and a2 are energy efficiency judgment thresholds, a1 < a2; b1 and b2 are load-power consumption judgment thresholds, b1 < 1 < b2; all thresholds are set by professionals.

[0025] Step S400: Implement the corresponding strategy based on the diagnostic results.

[0026] The specific strategy executed in step S400 based on the diagnostic results is as follows: If the diagnosis indicates a normal state, maintain the current operating parameters of the air conditioning system. If a minor energy efficiency abnormality is diagnosed, a parameter optimization strategy is executed, which includes automatically adjusting the operating setpoints or operating modes of the air conditioning system. If a serious energy efficiency anomaly is diagnosed, a system intervention strategy will be implemented. The system intervention strategy includes generating alarm information and sending it to maintenance personnel, reducing the load on the air conditioning system and switching to a safe operating mode. If a load-power mismatch is diagnosed, a data verification and load tracking strategy is executed. The tracking strategy includes generating alarm information and sending it to maintenance personnel, verifying the validity of sensor data, and having maintenance personnel adjust the output power of the air conditioning system to match the current heat load. If a complex abnormal condition is diagnosed, the machine should be stopped immediately for inspection and repair.

[0027] An air conditioning energy management system based on adaptive load, comprising a data acquisition module, a data integration and feature extraction module, a status diagnosis module, and a strategy execution module; The data acquisition module collects raw data such as external environmental parameters, internal environmental status, air conditioning system power consumption, and real-time number of people in each zone by deploying sensors and monitoring equipment. Through modular design, it realizes closed-loop management of data acquisition, processing, diagnosis and execution, ensuring that the system responds quickly and reliably. The data integration and feature extraction module integrates the collected raw data into a unified vector, calculates the comprehensive heat load index and the real-time energy efficiency ratio of the system, forms a situation profile, and provides advanced features for system status diagnosis. The status diagnosis module calculates the energy efficiency deviation rate and load-power consistency index based on the situation profile, and diagnoses the system status in real time according to preset thresholds, including normal, slightly abnormal energy efficiency, severely abnormal energy efficiency, and load-power mismatch. The strategy execution module automatically triggers and executes corresponding energy consumption management strategies based on the output of the status diagnosis module. It directly acts on the air conditioning system to make adaptive adjustments for different system anomalies or optimization needs, so as to achieve optimized control of the energy consumption of the air conditioning system and ensure that the system operates in an efficient and matched state. As the decision-making end of the entire management process, the strategy execution module transforms the analysis and diagnosis results of the preceding modules into specific control actions.

[0028] The data acquisition module includes a sensor deployment unit and an energy consumption and personnel monitoring unit; The sensor arrangement unit arranges external and internal environmental sensors for sensing natural environmental parameters such as outdoor temperature and solar radiation intensity, as well as the actual environmental status of each functional area inside the building. The energy consumption and personnel monitoring unit monitors the total power consumption of the air conditioning system through a smart meter and counts the real-time number of people in each zone through the access control system, thereby achieving real-time collection of energy consumption and personnel data.

[0029] The data integration and feature extraction module includes a data vectorization unit, a heat load quantization unit, and an energy efficiency quantization unit. The data vectorization unit integrates raw data from different sources and with different dimensions, including outdoor temperature, solar radiation intensity, total air conditioning power consumption, and average number of people in the area, into a unified raw data vector according to a fixed time period. The heat load quantification unit constructs a comprehensive heat load index, which generates a comprehensive index that characterizes the total heat load level currently borne by the building by weighting and integrating three influencing factors: outdoor meteorological conditions, solar radiation, and heat dissipation from indoor occupants. The system energy efficiency quantification unit calculates the real-time energy efficiency ratio, which is the ratio of the system's output cooling capacity to the total input electrical power, to intuitively reflect the immediate efficiency of the air conditioning unit and distribution system in converting electrical energy into cooling capacity.

[0030] The status diagnosis module includes an evaluation index calculation unit and a status diagnosis unit; The evaluation index calculation unit calculates the energy efficiency deviation rate and the load-power consumption consistency index, which respectively measure the deviation between the current energy efficiency and the expected energy efficiency and the degree of matching between the system power consumption and the heat load. The status diagnosis unit performs real-time classification and diagnosis of the system status based on the thresholds of energy efficiency deviation rate and load-power consumption consistency index, and makes corresponding strategies.

[0031] Example: Substituting the data into the formula yields... , Further calculations yielded , Substitute and draw , , , ; DEV cop (14:00)≈-4.91%<a1%, CI(14:00)≈1.243>b2. The final diagnosis result is a load-power mismatch state. An alarm message is generated and sent to the operation and maintenance personnel. The validity of the sensor data is verified, and a command is sent to the air conditioner DDC controller to reduce the output power.

[0032] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An air conditioning energy consumption management method based on adaptive load, characterized in that: The method includes the following steps: Step S100: Deploy external and internal environmental sensors to collect natural environmental parameters and the actual environmental status inside the building. Monitor the total power consumption of the air conditioning system through smart meters and use the access control system to count the real-time number of people in different areas. Step S200: Integrate the collected data into a unified vector, including outdoor temperature, solar radiation intensity, total air conditioning power consumption and average number of people in the area, calculate the comprehensive heat load index and the real-time energy efficiency ratio of the system, and form a situation profile composed of the heat load index and energy efficiency ratio. Step S300: Based on the situation profile, calculate the energy efficiency deviation rate and load-power consumption consistency index, and diagnose the system status in real time according to the preset threshold, including normal, slightly abnormal energy efficiency, severely abnormal energy efficiency, load-power consumption mismatch and compound abnormality. Step S400: Implement the corresponding strategy based on the diagnostic results.

2. The air conditioning energy consumption management method based on adaptive load according to claim 1, characterized in that: Step S100 includes the following steps: Step S101: Install external and internal environmental sensors for the building. The external environmental sensors are used to sense natural environmental parameters outside the building, including solar radiation sensors. The internal environmental sensors sense the actual environmental conditions of each functional area inside the building, including indoor temperature and humidity sensors. The outdoor temperature and humidity sensor measures the temperature and relative humidity of the outdoor atmosphere. The core parameter is the outdoor temperature T. out (Unit: °C) The solar radiation sensor is used to measure the total solar radiation energy received per unit area and to measure the solar radiation intensity I. solar Unit: W / m²; Step S102: Set up sensors to monitor the energy consumption and operating status of the core equipment of the air conditioning system. Specifically, set up smart meters to measure the total power consumption of the air conditioning system and monitor the total power consumption P of the air conditioning system. total Unit: kW; The air conditioning work area is divided into different zones using an access control system. The number of people entering the zone is recorded by swiping cards at the access control system. Real-time person counters are built for each zone, and the real-time number of people in each zone is output. , where i represents the partition index, corresponding to the i-th functional partition.

3. The air conditioning energy consumption management method based on adaptive load according to claim 2, characterized in that: Step S200 includes the following steps: Step S201: The raw data collected in step S100 is integrated into a vector and iterated over at a fixed time period Δt. Each period corresponds to a data acquisition and processing node, denoted as time t. At time t, the mathematical expression of the raw data vector is: ； In the formula, It is the original data vector. The outdoor temperature at time t. It is the total solar radiation intensity at time t. It is the average population of the region, expressed as the real-time population of each sub-region at time t. The average is obtained. This represents the total power consumption of the air conditioning system at time t. Step S202: The original data vector is fused into two high-level features, namely the system heat load level and the system energy efficiency level; The system heat load level is quantified using the comprehensive heat load index L(t): The comprehensive heat load index L(t) is the equivalent temperature difference reflecting the total effect of all thermal disturbances inside and outside the building. L(t) integrates three independent heat load sources—outdoor temperature, solar radiation, and heat dissipation from people—into a single index with weighting coefficients. ； In the formula, T reflects the contribution of outdoor temperature to the heat load. ref It is the building's interior design temperature, ΔT std This is the reference temperature, set by professionals; w1 is the weighting coefficient, set by professionals. Reflecting the contribution of solar radiation to the heat load, I std This is the standard solar radiation intensity, and w2 is a weighting coefficient set by professionals. N reflects the contribution of personnel heat dissipation to the heat load. max It is the maximum capacity of the building design, w3 is the weighting coefficient, which is set by professionals, w1+w2+w3=1; Step S203: Use the system's real-time energy efficiency ratio The system's energy efficiency level is quantified, and the real-time energy efficiency ratio reflects the system's conversion efficiency from input electrical energy to output cooling capacity. ； In the formula, This is the total power consumption of the air conditioning system at time t, in kW. This refers to the total cooling capacity of the air conditioning system, measured in kW. ; In the formula, c P ρ is the specific heat capacity of water at constant pressure, and ρ is the density of water. It is the air conditioner's chilled water flow rate. It is the difference between the temperature of the cold water entering the air conditioner heat exchanger and the temperature of the cold water flowing out of the air conditioner heat exchanger; Step S204: The comprehensive heat load index and the real-time energy efficiency ratio of the system are integrated to form a situation profile P(t), which is an ordered set: 。 4. The air conditioning energy consumption management method based on adaptive load according to claim 3, characterized in that: Step S300 includes the following steps: Step S301: Based on situational profile Calculate the metrics used for system evaluation, including energy efficiency deviation rate and load-power consistency index; The energy efficiency deviation rate Used to measure the deviation between current energy efficiency and expected energy efficiency: ； In the formula, COP base It uses the rated energy efficiency ratio of the air conditioning system under design conditions; The load-power consistency index CI(t) assesses whether the system power consumption matches the current building heat load. ； In the formula, P rated It is the rated total power of the air conditioning system, L max Design the air conditioner to the maximum heat load; Step S302: Classify the load-power consistency index and energy efficiency deviation rate in real time based on a threshold, specifically as follows: when When this occurs, it is considered a normal state; when When this occurs, it is determined to be an abnormal energy efficiency state; when When this occurs, it is determined to be a severely abnormal energy efficiency state; when When this occurs, it is determined to be a load-power mismatch state; when and When this occurs, it is determined to be a complex abnormal state; a1 and a2 are energy efficiency judgment thresholds, a1 < a2; b1 and b2 are load-power consumption judgment thresholds, b1 < 1 < b2; all thresholds are set by professionals.

5. The air conditioning energy consumption management method based on adaptive load according to claim 4, characterized in that: The specific steps for implementing the corresponding strategy based on the diagnostic results in step S400 are as follows: If the diagnosis indicates a normal state, maintain the current operating parameters of the air conditioning system. If a minor energy efficiency abnormality is diagnosed, a parameter optimization strategy is executed, which includes automatically adjusting the operating setpoints or operating modes of the air conditioning system. If a serious energy efficiency anomaly is diagnosed, a system intervention strategy will be implemented. The system intervention strategy includes generating alarm information and sending it to maintenance personnel, reducing the load on the air conditioning system and switching to a safe operating mode. If a load-power mismatch is diagnosed, a data verification and load tracking strategy is executed. The tracking strategy includes generating alarm information and sending it to maintenance personnel, verifying the validity of sensor data, and having maintenance personnel adjust the output power of the air conditioning system to match the current heat load. If a complex abnormal condition is diagnosed, the machine should be stopped immediately for inspection and repair.

6. An air conditioning energy management system based on adaptive load, characterized in that: The air conditioning energy consumption management system includes a data acquisition module, a data integration and feature extraction module, a status diagnosis module, and a strategy execution module; The data acquisition module collects raw data such as external environmental parameters, internal environmental status, air conditioning system power consumption, and real-time number of people in each zone by deploying sensors and monitoring equipment. The data integration and feature extraction module integrates the collected raw data into a unified vector, calculates the comprehensive heat load index and the real-time energy efficiency ratio of the system, forms a situation profile, and provides advanced features for system status diagnosis. The status diagnosis module calculates the energy efficiency deviation rate and load-power consistency index based on the situation profile, and diagnoses the system status in real time according to preset thresholds, including normal, slightly abnormal energy efficiency, severely abnormal energy efficiency, and load-power mismatch. The strategy execution module automatically triggers and executes the corresponding energy management strategy based on the output of the status diagnosis module, and directly applies it to the air conditioning system for adaptive adjustment in response to different system anomalies or optimization needs.

7. The air conditioning energy management system based on adaptive load according to claim 6, characterized in that: The data acquisition module includes a sensor deployment unit and an energy consumption and personnel monitoring unit; The sensor arrangement unit arranges external and internal environmental sensors for sensing natural environmental parameters such as outdoor temperature and solar radiation intensity, as well as the actual environmental status of each functional area inside the building. The energy consumption and personnel monitoring unit monitors the total power consumption of the air conditioning system through a smart meter and counts the real-time number of people in each zone through the access control system, thereby achieving real-time collection of energy consumption and personnel data.

8. The air conditioning energy management system based on adaptive load according to claim 6, characterized in that: The data integration and feature extraction module includes a data vectorization unit, a heat load quantization unit, and an energy efficiency quantization unit. The data vectorization unit integrates raw data from different sources and with different dimensions, including outdoor temperature, solar radiation intensity, total air conditioning power consumption, and average number of people in the area, into a unified raw data vector according to a fixed time period. The heat load quantification unit constructs a comprehensive heat load index, which generates a comprehensive index that characterizes the total heat load level currently borne by the building by weighting and integrating three influencing factors: outdoor meteorological conditions, solar radiation, and heat dissipation from indoor occupants. The energy efficiency quantification unit calculates the real-time energy efficiency ratio, which is the ratio of the system's output cooling capacity to the total input electrical power, to intuitively reflect the immediate efficiency of the air conditioning unit and distribution system in converting electrical energy into cooling capacity.

9. The air conditioning energy management system based on adaptive load according to claim 6, characterized in that: The status diagnosis module includes an evaluation index calculation unit and a status diagnosis unit; The evaluation index calculation unit calculates the energy efficiency deviation rate and the load-power consumption consistency index, which respectively measure the deviation between the current energy efficiency and the expected energy efficiency and the degree of matching between the system power consumption and the heat load. The status diagnosis unit performs real-time classification and diagnosis of the system status based on the thresholds of energy efficiency deviation rate and load-power consumption consistency index, and makes corresponding strategies.

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