Air conditioner energy-saving potential analysis method based on smart energy management system

Abstract

The invention belongs to the technical field of energy efficiency management, and discloses an air conditioner energy-saving potential analysis method based on a smart energy management system. The method comprises the steps of collecting activity types and calculating to obtain a metabolic rate coefficient; acquiring indoor and outdoor temperature and age distribution, and calculating to obtain dynamic comfortable temperature; calculating according to the metabolic rate coefficient to obtain a thermal load distribution map and a dynamic air supply amount; calculating according to the dynamic comfortable temperature to obtain a temperature adjustment potential; according to the thermal load distribution map, the dynamic air supply amount and the temperature adjustment potential, a predictive control strategy is obtained through calculation; analyzing according to the comfort interval and the service life of the equipment to obtain a list of the equipment with abnormal standby energy consumption; acquiring real-time power of equipment, and calculating to obtain an energy efficiency attenuation rate; and analyzing the list of the equipment with the abnormal standby energy consumption and the predictive control strategy to obtain optimization parameter update. The energy efficiency of the air conditioner is accurately optimized through multi-dimensional dynamic analysis, and the energy-saving effect of the building is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of energy efficiency management technology, and more specifically, to a method for analyzing the energy-saving potential of air conditioners based on a smart energy management system. Background Technology

[0002] Currently, building air conditioning systems account for over 30% of total building energy consumption, with air conditioning systems alone accounting for 40%-60%, making them a major component of building energy consumption. Traditional air conditioning systems generally employ decentralized control methods, lacking dynamic sensing and intelligent adjustment capabilities for equipment aging, environmental parameter changes, and human activity characteristics, leading to significant energy waste. Existing technologies primarily rely on static parameter settings for air conditioning energy efficiency management, failing to fully consider the impact of equipment age on energy efficiency, nor dynamically adjust operating strategies in conjunction with seasonal changes. They also cannot accurately identify equipment with abnormal standby energy consumption and lack intelligent optimization mechanisms for balancing comfort and energy consumption. Existing systems often use fixed thresholds to judge abnormal energy consumption, failing to conduct multi-dimensional correlation analysis of energy efficiency degradation rates, equipment aging coefficients, and seasonal correction factors, resulting in energy-saving optimization strategies lacking specificity and precision. Furthermore, traditional systems lack an energy efficiency index evaluation system, making it impossible to quantify the effectiveness of optimization strategies and hindering the continuous improvement and dynamic optimization of air conditioning system energy efficiency. These problems severely restrict the improvement of building air conditioning system energy efficiency and fail to meet the urgent needs of building energy conservation under the "dual carbon" target.

[0003] Chinese patent application CN118640564B discloses a smart AI energy-saving method based on integrated energy management. The method includes: collecting signal strength data from different fixed locations to form fingerprint information and expanding the fingerprint; collecting signal strength data from mobile devices within a building for fingerprint positioning; clustering personnel location information to construct an adaptive air conditioning control model; and adjusting air conditioning parameters based on personnel location cluster information and air conditioning location information. This invention combines the Grey Wolf optimization algorithm to iteratively optimize interpolation parameters. It uses interpolation parameters to calculate fingerprint information for building locations lacking fingerprint information, thus expanding the fingerprint information within the building. It uses this fingerprint information to locate personnel within the building and adjusts air conditioning parameters based on the personnel location distribution characteristics near different air conditioning locations, achieving air conditioning energy management based on personnel location distribution.

[0004] While the above methods can meet the needs of most scenarios, research and practical application of these methods and existing technologies have revealed at least the following shortcomings:

[0005] This patent relies solely on fixed temperature thresholds and general equipment energy efficiency models for environmental control, without establishing a dynamic comfort temperature calculation mechanism for specific population groups. It only uses a single indoor temperature setpoint for control; it lacks a collaborative optimization mechanism for heat load distribution maps and dynamic air volume, and only adopts a simple temperature control strategy; it fails to achieve multi-dimensional balance optimization between comfort and energy efficiency, resulting in an inability to meet the special needs of environmental control.

[0006] In view of this, the present invention proposes a method for analyzing the energy-saving potential of air conditioning based on a smart energy management system to solve the above problems. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a method for analyzing the energy-saving potential of air conditioning based on a smart energy management system, comprising:

[0008] The activity type, age distribution, and indoor / outdoor temperature within the data collection area are analyzed; the metabolic rate coefficient is calculated based on the activity type; and the dynamic comfort temperature is calculated based on the indoor / outdoor temperature and age distribution.

[0009] The population density and building area within the data collection area are used to calculate the heat load distribution map and dynamic air supply volume based on the metabolic rate coefficient, population density, and building area; the temperature adjustment potential is calculated based on the dynamic comfort temperature.

[0010] Analyze the indoor and outdoor temperature and heat load distribution maps to obtain intelligent decision-making for the transition season; calculate predictive control strategies based on the heat load distribution map, dynamic air supply volume, and temperature adjustment potential.

[0011] Based on the analysis of comfort range and equipment service life, a list of devices with abnormal standby power consumption is obtained; real-time power of the devices is collected, and the energy efficiency degradation rate is calculated.

[0012] The list of devices with abnormal energy consumption and the predictive control strategy are analyzed to obtain evaluation results; the evaluation results are then analyzed to obtain updated optimization parameters.

[0013] Furthermore, methods for obtaining dynamic comfort temperature include:

[0014] The activity type is analyzed to obtain the metabolic rate coefficient; the activity intensity coefficient is calculated based on the metabolic rate coefficient; and the dynamic comfort temperature is calculated based on the activity intensity coefficient, indoor and outdoor temperatures, metabolic rate coefficient, and age distribution.

[0015] Furthermore, methods for obtaining a list of devices with abnormal standby power consumption include:

[0016] Analyze the service life of the equipment to obtain the equipment aging coefficient: when the service life of the equipment is [a, b), the equipment aging coefficient is low; when the service life of the equipment is [b, c), the equipment aging coefficient is medium; when the service life of the equipment is greater than c, the equipment aging coefficient is high. a, b, and c are the equipment service life thresholds. Calculate the dynamic standby power threshold based on the equipment's rated power, equipment aging coefficient, and seasonal correction factor. Calculate the list of equipment with abnormal standby power consumption based on the dynamic standby power threshold.

[0017] Furthermore, methods for obtaining the seasonal correction factor include:

[0018] Comfort zones are determined based on historical data, including summer, winter, and transitional season comfort zones. Season types are then determined based on these comfort zones: if the comfort zone is a summer comfort zone, it is classified as summer; if it is a winter comfort zone, it is classified as winter; and if it is a transitional season comfort zone, it is classified as a transitional season. Seasonal correction factors are then obtained by analyzing the seasonal types: a summer correction factor is obtained when the season type is summer, a winter correction factor is obtained when the season type is winter, and a transitional season correction factor is obtained when the season type is a transitional season.

[0019] Furthermore, methods for obtaining the energy efficiency degradation rate include:

[0020] The system collects real-time power data from the compressor, fan, and auxiliary equipment to calculate the total operating power. It also calculates the inlet and outlet temperature difference of the air conditioner based on the inlet and outlet temperatures. Based on this temperature difference, it calculates the actual cooling capacity of the air conditioner. Finally, it calculates the actual operating energy efficiency ratio (EER) based on the sum of the operating power and the EER. And based on the actual EER, it calculates the energy efficiency degradation rate.

[0021] Furthermore, methods for obtaining heat load distribution maps include:

[0022] The metabolic heat load of the population is calculated based on the metabolic rate coefficient and the population density of the area; the glass thermal coefficient is obtained by analyzing the glass type; the solar radiation heat load is calculated based on the glass thermal coefficient and the solar radiation intensity; the metabolic heat load of the population, the solar radiation heat load and the building heat dissipation heat load are spliced ​​together to obtain the heat load distribution map.

[0023] Furthermore, methods for obtaining temperature regulation potential include:

[0024] The dynamic air supply volume is calculated based on the population density of the area; the actual temperature difference is calculated based on the indoor temperature and the dynamic comfort temperature; and the temperature adjustment potential is calculated based on the actual temperature difference.

[0025] Furthermore, methods for obtaining predictive control strategies include:

[0026] Analyzing the heat load distribution map and dynamic air supply volume, a seasonal operation strategy library was obtained: In winter, the heat compensation target is set as the sum of building heat load and occupant heat load, and the air supply volume is set as a multiple of the winter adjustment coefficient of the dynamic air supply volume; in transitional seasons, a natural ventilation priority strategy is adopted, and the air supply volume is set as a multiple of the transitional season coefficient of the dynamic air supply volume; in summer, the cooling target is set as the sum of solar radiation heat load and occupant heat load, and the air supply volume is set as a multiple of the summer coefficient of the dynamic air supply volume; analysis of the heat load distribution map yielded intelligent natural ventilation strategies for transitional seasons. Decision-making: When the solar radiation heat load is lower than the solar radiation heat load threshold and the outdoor wind speed is higher than the wind speed set threshold, natural ventilation is turned on; otherwise, natural ventilation is turned off. Based on the outdoor temperature analysis, an extreme weather emergency plan is obtained: when the outdoor temperature is greater than or equal to the high temperature threshold, the high temperature emergency plan is activated; when the indoor temperature is less than or equal to the low temperature threshold, the low temperature emergency plan is activated; when the precipitation is greater than the precipitation intensity threshold, the rainstorm emergency plan is activated; otherwise, the normal operation plan is followed. Based on the heat load distribution map, dynamic air supply volume, and temperature adjustment potential, a predictive control strategy is calculated.

[0027] Furthermore, the assessment results include anomaly equipment impact and strategy assessment, and the methods for obtaining the assessment results include:

[0028] Analyze the list of equipment with abnormal energy consumption to obtain the impact of abnormal equipment; calculate the energy efficiency index based on the predictive control strategy; analyze the energy efficiency index to obtain the strategy evaluation: when the energy efficiency index is greater than or equal to the excellent threshold, the strategy evaluation is judged as excellent; when the energy efficiency index is less than the excellent threshold but greater than or equal to the good threshold, it is judged as good; when the energy efficiency index is less than the good threshold but greater than or equal to the average threshold, it is judged as average; when the energy efficiency index is less than the average threshold, it is judged as needing optimization.

[0029] Furthermore, methods for obtaining optimized parameter updates include:

[0030] Analyzing strategy assessments and energy efficiency degradation rates yields optimization suggestions: When the strategy assessment indicates optimization is needed and the season is winter, an adjusted winter coefficient is generated; during the transition season, an adjusted transition season coefficient is generated; and during summer, an adjusted summer coefficient is generated. Analyzing the impact of abnormal equipment and optimization suggestions yields updated optimization parameters: When the optimization suggestion is to adjust the winter coefficient, the winter adjustment coefficient and the winter adjustment amount coefficient are added together based on the energy efficiency degradation rate and the impact of abnormal equipment to obtain an updated winter coefficient; when the optimization suggestion is to adjust the transition season coefficient, the transition season adjustment coefficient and the transition season adjustment amount coefficient are added together to obtain an updated transition season coefficient; and when the optimization suggestion is to adjust the summer coefficient, the summer adjustment coefficient and the summer adjustment amount coefficient are added together to obtain an updated summer coefficient.

[0031] The technical effects and advantages of the air conditioning energy-saving potential analysis method based on a smart energy management system provided by this invention are as follows:

[0032] This invention constructs a dynamic comfort temperature calculation mechanism through multi-source data fusion, accurately matching activity types and age distribution to achieve personalized indoor environment adjustment; it generates dynamic standby power thresholds based on equipment age and seasonal correction factors to accurately identify equipment with abnormal standby energy consumption; it integrates heat load distribution maps, dynamic air volume, and temperature adjustment potential to construct seasonal predictive control strategies, achieving dynamic optimization of winter heat compensation, priority of natural ventilation in transitional seasons, and summer cooling targets; and it drives automatic parameter updates through energy efficiency index and strategy evaluation, reducing energy efficiency degradation rate, improving the overall energy saving rate of building air conditioning systems and the quantitative accuracy of abnormal equipment impact, significantly enhancing system energy efficiency stability and user comfort. Attached Figure Description

[0033] Figure 1 This is a flowchart of an air conditioning energy-saving potential analysis method based on a smart energy management system according to the present invention.

[0034] Figure 2 This is a flowchart of the method for obtaining a list of devices with abnormal standby power consumption according to the present invention;

[0035] Figure 3 This is a flowchart of the method for obtaining a heat load distribution map according to the present invention;

[0036] Figure 4 This is a flowchart of the method for obtaining optimized parameter updates according to the present invention. Detailed Implementation

[0037] 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.

[0038] Example 1:

[0039] Please see Figure 1 As shown in this embodiment, a method for analyzing the energy-saving potential of air conditioning based on a smart energy management system includes:

[0040] Collect indoor and outdoor temperatures, building area, air conditioning inlet and outlet temperatures, building heat dissipation load, and solar radiation values;

[0041] The system collects data on population density, activity types, and age distribution in the area. Activity types include sedentary activities, light activities, and moderate activities. Through precise data collection, it enables innovative dynamic environmental control: indoor and outdoor temperatures, building area, and solar radiation values ​​provide fundamental input for dynamic comfort temperature calculations and heat load distribution mapping, ensuring that comfort zones accurately match the physiological needs of the elderly. Activity types and age distribution, through metabolic rate coefficient calculations, enable refined modeling of heat load, reducing errors in calculating metabolic heat load and driving the precise execution of seasonal operation strategies, thereby improving the overall energy efficiency and comfort of the building's air conditioning system.

[0042] Based on the activity type, the activity intensity coefficient and metabolic rate coefficient are calculated; based on outdoor temperature, activity type, metabolic rate, and age distribution, the dynamic comfort temperature is calculated; and the comfort range is obtained based on historical data statistics.

[0043] Analyze the activity type to obtain the metabolic rate coefficient, such as the metabolic rate coefficient. ,in The metabolic rate during sedentary activity. The metabolic rate is for mild activity. The metabolic rate coefficient for moderate activity is obtained from historical data. Based on the metabolic rate coefficient, the activity intensity coefficient is calculated. ,in The baseline coefficient is used; based on the activity intensity coefficient, outdoor temperature, metabolic rate coefficient, and age distribution, the dynamic comfort temperature is calculated, such as the dynamic comfort temperature. ,in Based on the baseline comfort temperature, This is the seasonal adaptability coefficient. This is the age-adaptability coefficient. The age distribution is obtained from historical statistical data. The outdoor temperature is used as the reference point; the comfort range is determined based on historical data, including the summer comfort range, winter comfort range, and transitional season comfort range; the summer comfort range is... The winter comfort zone is The comfortable range for the transition season is ;in , and These are the lower limits of comfortable temperatures in summer, winter, and transitional seasons. , and This system defines upper limits for comfortable temperatures in summer, winter, and transitional seasons. Through a dynamic analysis module, it achieves a precise breakthrough in environmental regulation for the elderly population: innovatively linking activity type with the metabolic rate coefficient M, and constructing a dynamic heat load model based on the activity intensity coefficient to reduce errors in metabolic heat load calculation; integrating factors such as outdoor temperature, activity metabolic rate, and age distribution to dynamically adjust the comfortable temperature according to the physiological characteristics of the elderly; and precisely matching the thermal adaptation needs of the elderly through seasonal comfort ranges constructed from historical data, improving comfort while avoiding excessive cooling / heating caused by traditional fixed temperature settings. This addresses the shortcomings of existing systems that ignore the physiological characteristics and activity differences of the elderly, providing a scientific and personalized foundation for environmental regulation in senior activity centers.

[0044] The equipment's service life is analyzed to obtain the equipment aging coefficient; the seasonal type is analyzed based on the comfort range; the dynamic standby power threshold is calculated; a list of equipment with abnormal standby energy consumption is obtained based on the real-time standby power and the dynamic standby power threshold; the real-time power of the compressor, the real-time power of the fan, and the real-time power of the auxiliary equipment are collected to calculate the total operating power; and the energy efficiency degradation rate is calculated based on the total operating power.

[0045] Analyze the service life of the equipment to obtain the equipment aging coefficient, such as the equipment aging coefficient. Where a, b, and c represent the service life of the equipment. For the service life of the equipment The equipment aging coefficient, For the service life of the equipment The equipment aging coefficient, For equipment with a service life greater than c, the aging factor is used; the comfort range is analyzed to obtain the seasonal type, such as the seasonal type. Analyze seasonal types to obtain seasonal correction factors, such as seasonal correction factors. ,in For summer season correction factor, For winter seasonal correction factors, The seasonal correction factor is used for the transition season; based on the equipment's rated power, equipment aging coefficient, and seasonal correction factor, the dynamic standby power threshold is calculated, such as the dynamic standby power threshold. Analyze real-time standby power and dynamic standby power thresholds to obtain a list of devices with abnormal standby power consumption, such as the list of devices with abnormal standby power consumption. ,in Real-time standby power; collect real-time power data of the compressor, fan, and auxiliary equipment, and calculate the total operating power, such as the total operating power. ,in This refers to the real-time power of the compressor. This represents the real-time power of the wind turbine. Real-time power of auxiliary equipment; calculate the inlet and outlet temperature difference based on the inlet and outlet temperatures, such as the inlet and outlet temperature difference. ,in The inlet temperature, The outlet temperature is used as the reference point; the actual cooling capacity of the air conditioner is calculated based on the temperature difference between the inlet and outlet. Where 3.517 is the unit conversion factor, and C is the specific heat capacity of air. air density, The air supply volume; the actual operating energy efficiency ratio is calculated based on the sum of the air conditioner's operating energy efficiency ratio and total operating power. (The actual operating energy efficiency ratio is...) Based on the actual operating energy efficiency ratio, the energy efficiency degradation rate is calculated, such as the energy efficiency degradation rate. ,in The initial energy efficiency ratio (EER) of the equipment is obtained from the equipment file. The equipment analysis module enables precise dynamic assessment of equipment status and energy consumption: the equipment's service life is divided into three aging stages, and an aging coefficient and seasonal correction factor are dynamically generated based on seasonal characteristics. This constructs a standby power threshold that automatically adjusts with the degree of equipment aging and seasonal changes. This threshold accurately identifies equipment with abnormal standby energy consumption, improving the accuracy of anomaly identification. Simultaneously, through dynamic calculations of real-time operating power, inlet / outlet temperature difference, and initial EER, the energy efficiency degradation rate of the equipment is accurately quantified, providing a scientific basis for equipment maintenance. This technology completely solves the problem of misjudging abnormal equipment caused by the use of fixed thresholds in traditional systems, significantly improving the accuracy of energy efficiency optimization in the elderly activity center's air conditioning system and enhancing overall energy-saving effects.

[0046] The heat load of the occupants is calculated based on the metabolic rate coefficient, regional population density, baseline metabolic rate, and building area; the solar radiation heat load is calculated based on the glass type and solar radiation intensity; the heat load of the occupants, solar radiation heat load, and building heat dissipation load are spliced ​​together to obtain a heat load distribution map; the dynamic air supply volume is calculated based on the population density; and the temperature adjustment potential is calculated based on the dynamic comfort temperature.

[0047] The metabolic heat load of the population is calculated based on the metabolic rate coefficient, building area, and regional population density. ,in Let A represent the population density of the area and the building area; analyze the glass type to obtain the glass thermal coefficient, such as the glass thermal coefficient. ,in The thermal coefficient of a single-layer glass, for The thermal coefficient is obtained from historical data statistics; the solar radiation heat load is calculated based on the glass thermal coefficient and solar radiation intensity. ,in The solar radiation intensity is used to obtain a heat load distribution map by combining the metabolic heat load of people, the solar radiation heat load, and the building heat dissipation load. Based on the population density of the area, the dynamic air supply volume is calculated, such as the dynamic air supply volume. ,in This is the density correction factor. The baseline air supply volume is used; the actual temperature difference is calculated based on the indoor temperature and dynamic comfort temperature, such as the actual temperature difference. ,in The indoor temperature is used as an indicator; the temperature adjustment potential is calculated based on the actual temperature difference. For example, if the temperature adjustment potential is... ,in This module provides a temperature adjustment coefficient and innovates refined modeling and dynamic control of heat load through a regional analysis module: It dynamically correlates regional population density with metabolic rate coefficients to calculate population heat load, improving the accuracy of heat load calculation; it automatically matches glass thermal coefficients based on glass type to accurately calculate solar radiation heat load, eliminating the shortcomings of traditional methods that ignore differences in building envelope; it constructs a heat load distribution map by splicing population heat load, solar radiation heat load, and building heat dissipation load, achieving accurate visualization of multi-dimensional heat load; it incorporates a population density correction coefficient in dynamic air supply volume calculation to match actual demand and avoid excessive air supply; and it combines dynamic comfort temperature calculation with temperature adjustment potential to provide accurate input for predictive control strategies. This module solves the problems of coarse heat load calculation and fixed air supply volume in traditional systems, improving the comfort of the air conditioning system in senior activity centers while increasing overall energy efficiency, significantly enhancing the scientific nature and energy efficiency of environmental control.

[0048] Analyze outdoor temperatures to obtain seasonal divisions and emergency plans for extreme weather; analyze heat load distribution maps and dynamic air supply volume to obtain a seasonal operation strategy library; analyze seasonal divisions and heat load distribution maps to obtain intelligent decision-making for transition seasons; and calculate predictive control strategies based on heat load distribution maps, dynamic air supply volume, and temperature adjustment potential.

[0049] Analyze outdoor temperatures to obtain seasonal categories, such as seasonal types. ,in Outdoor temperature This is the winter temperature threshold. The summer temperature threshold is obtained from historical data statistics; the heat load distribution map and dynamic air supply volume are analyzed to obtain a seasonal operation strategy library, such as the seasonal operation strategy library. ,in Adjustment coefficient for winter This is a transitional season adjustment factor. A summer adjustment coefficient is established; seasonal divisions and heat load distribution maps are analyzed to obtain intelligent decision-making for natural ventilation during the transition season; natural ventilation is activated when the solar radiation heat load is below the solar radiation heat load threshold and the outdoor wind speed is above the wind speed set threshold; otherwise, natural ventilation is deactivated. The solar radiation heat load threshold and wind speed set threshold are obtained from historical data statistics; extreme weather emergency plans are obtained based on outdoor temperature analysis. ,in This is the high temperature threshold temperature. This is the low temperature threshold temperature. The precipitation intensity threshold is obtained from historical data statistics. Precipitation intensity is obtained from external weather forecasts; predictive control strategies are calculated based on heat load distribution maps, dynamic air supply volume, and temperature adjustment potential. ;in This is the heat load weighting factor. This is the weighting coefficient for air supply volume. This is the temperature elasticity weighting coefficient. Historical power weighting coefficient, The outdoor temperature influence weighting coefficient, For the weighting coefficients of the operation plan, Historical operating power The quantified values ​​for the operational plan are obtained from historical data statistics. The system analysis module enables intelligent and dynamic optimization of air conditioning operation strategies: It innovatively uses temperature thresholds based on historical data statistics to achieve precise seasonal division, avoiding the control inaccuracies caused by traditional systems relying on fixed seasonal divisions; it constructs a seasonal dynamic operation strategy library, enabling precise heat compensation in winter, intelligent natural ventilation priority during transition seasons, and efficient cooling targets in summer, ensuring that environmental control fully matches the physiological needs of the elderly; it introduces dual conditions of solar radiation heat load and wind speed to determine natural ventilation during transition seasons, improving energy efficiency; it establishes three-level emergency plans for extreme weather (high temperature, low temperature, and heavy rain) to ensure stable system operation under special weather conditions; and it improves the prediction accuracy of air conditioning operation parameters through a weighted predictive control strategy that integrates multiple dimensions such as heat load distribution maps, dynamic air supply volume, and temperature potential, addressing the inability of traditional systems to dynamically adapt to the special needs of the elderly.

[0050] The process involves analyzing a list of devices with abnormal energy consumption to identify their impact; calculating an energy efficiency index based on a predictive control strategy; analyzing the energy efficiency index to obtain a strategy evaluation; analyzing the strategy evaluation and energy efficiency degradation rate to obtain optimization suggestions; and analyzing the impact of abnormal devices and optimization suggestions to obtain updated optimization parameters.

[0051] Analyze the list of devices with abnormal power consumption to obtain the impact of the abnormal devices, such as the impact of abnormal devices. ,in The operating power of abnormal equipment; based on the predictive control strategy, the energy efficiency index is calculated, such as the energy efficiency index. ,in This involves determining the real-time operating power of the equipment; analyzing the energy efficiency index to obtain strategy assessments, such as strategy evaluations. Where A is the excellent threshold, B is the good threshold, and C is the average threshold, obtained from historical data statistics; the strategy evaluation and energy efficiency degradation rate are analyzed to obtain optimization suggestions: when the strategy evaluation indicates optimization is needed and the season is winter, an adjustment winter coefficient is generated; during the transition season, an adjustment transition season coefficient is generated; and during summer, an adjustment summer coefficient is generated; the impact of abnormal equipment and optimization suggestions are analyzed to obtain updated optimization parameters: when the optimization suggestion is to adjust the winter coefficient, the winter adjustment coefficient and the winter adjustment amount coefficient are added together based on the energy efficiency degradation rate and the impact of abnormal equipment to obtain the updated winter coefficient; when the optimization suggestion is to adjust the transition season coefficient, the transition season adjustment coefficient and the transition season adjustment amount coefficient are added together to obtain the updated transition season ... When adjusting the summer coefficient, the summer adjustment coefficient and the summer adjustment amount coefficient are added together to obtain the updated summer coefficient. The winter adjustment amount coefficient, transition season adjustment amount coefficient, and summer adjustment amount coefficient are preset adjustment amounts obtained from historical data statistics. The system achieves closed-loop intelligent optimization of the air conditioning system through an effect optimization module: it innovatively quantifies the impact of abnormal equipment as a relative energy consumption percentage, accurately locating high-impact abnormal equipment; it uses a unique energy efficiency index to dynamically quantify the efficiency of strategy execution, breaking through the limitations of traditional fixed thresholds; it adaptively sets four levels of strategy evaluation standards based on historical data, ensuring precise matching between optimization targets and the actual system state; and it innovatively designs a seasonal adaptive optimization suggestion mechanism, using incremental optimization parameter updates to avoid drastic strategy fluctuations and continuously improve system energy efficiency. This module reduces the energy efficiency degradation rate of the elderly activity center's air conditioning system, improves strategy optimization efficiency, and provides core guarantees for long-term stable energy saving.

[0052] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0053] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the energy-saving potential of air conditioning based on a smart energy management system, characterized in that, include: The data collection area included activity types, age distribution, and indoor / outdoor temperatures. The metabolic rate coefficient is calculated based on the type of activity. Dynamic comfort temperature is calculated based on indoor and outdoor temperatures and age distribution. The population density and building area within the data collection area are used to calculate the heat load distribution map and dynamic air supply volume based on the metabolic rate coefficient, population density, and building area; the temperature adjustment potential is calculated based on the dynamic comfort temperature. Analyze the indoor and outdoor temperature and heat load distribution maps to obtain intelligent decision-making for the transition season; calculate predictive control strategies based on the heat load distribution map, dynamic air supply volume, and temperature adjustment potential. Based on the analysis of comfort range and equipment service life, a list of devices with abnormal standby power consumption is obtained; real-time power of the devices is collected, and the energy efficiency degradation rate is calculated. The list of devices with abnormal energy consumption and the predictive control strategy are analyzed to obtain evaluation results; the evaluation results are then analyzed to obtain updated optimization parameters.

2. The method for analyzing the energy-saving potential of air conditioning based on a smart energy management system according to claim 1, characterized in that, Methods for obtaining dynamic comfort temperature include: The activity type is analyzed to obtain the metabolic rate coefficient; the activity intensity coefficient is calculated based on the metabolic rate coefficient; and the dynamic comfort temperature is calculated based on the activity intensity coefficient, indoor and outdoor temperatures, metabolic rate coefficient, and age distribution.

3. The method for analyzing the energy-saving potential of air conditioning based on a smart energy management system according to claim 1, characterized in that, Methods for obtaining a list of devices with abnormal standby power consumption include: Analyze the service life of the equipment to obtain the equipment aging coefficient: when the service life of the equipment is [a, b), the equipment aging coefficient is low; when the service life of the equipment is [b, c), the equipment aging coefficient is medium; when the service life of the equipment is greater than c, the equipment aging coefficient is high. a, b, and c are the equipment service life thresholds. Calculate the dynamic standby power threshold based on the equipment's rated power, equipment aging coefficient, and seasonal correction factor. Calculate the list of equipment with abnormal standby power consumption based on the dynamic standby power threshold.

4. The method for analyzing the energy-saving potential of air conditioning based on a smart energy management system according to claim 3, characterized in that, Methods for obtaining seasonal correction factors include: Comfort zones are determined based on historical data, including summer, winter, and transitional season comfort zones. Season types are then determined based on these comfort zones: if the comfort zone is a summer comfort zone, it is classified as summer; if it is a winter comfort zone, it is classified as winter; and if it is a transitional season comfort zone, it is classified as a transitional season. Seasonal correction factors are then obtained by analyzing the seasonal types: a summer correction factor is obtained when the season type is summer, a winter correction factor is obtained when the season type is winter, and a transitional season correction factor is obtained when the season type is a transitional season.

5. The method for analyzing the energy-saving potential of air conditioning based on a smart energy management system according to claim 1, characterized in that, Methods for obtaining energy efficiency degradation rate include: The system collects real-time power data from the compressor, fan, and auxiliary equipment to calculate the total operating power. It also calculates the inlet and outlet temperature difference of the air conditioner based on the inlet and outlet temperatures. Based on this temperature difference, it calculates the actual cooling capacity of the air conditioner. Finally, it calculates the actual operating energy efficiency ratio (EER) based on the sum of the operating power and the EER. And based on the actual EER, it calculates the energy efficiency degradation rate.

6. The method for analyzing the energy-saving potential of air conditioning based on a smart energy management system according to claim 1, characterized in that, Methods for obtaining heat load distribution maps include: The metabolic heat load of the population is calculated based on the metabolic rate coefficient and the population density of the area; the glass thermal coefficient is obtained by analyzing the glass type; the solar radiation heat load is calculated based on the glass thermal coefficient and the solar radiation intensity; the metabolic heat load of the population, the solar radiation heat load and the building heat dissipation heat load are spliced ​​together to obtain the heat load distribution map.

7. The method for analyzing the energy-saving potential of air conditioning based on a smart energy management system according to claim 1, characterized in that, Methods for obtaining temperature regulation potential include: The dynamic air supply volume is calculated based on the population density of the area; the actual temperature difference is calculated based on the indoor temperature and the dynamic comfort temperature; and the temperature adjustment potential is calculated based on the actual temperature difference.

8. The method for analyzing the energy-saving potential of air conditioning based on a smart energy management system according to claim 7, characterized in that, Methods for obtaining predictive control strategies include: Analyzing the heat load distribution map and dynamic air supply volume, a seasonal operation strategy library was obtained: In winter, the heat compensation target is set as the sum of building heat load and occupant heat load, and the air supply volume is set as a multiple of the winter adjustment coefficient of the dynamic air supply volume; in transitional seasons, a natural ventilation priority strategy is adopted, and the air supply volume is set as a multiple of the transitional season coefficient of the dynamic air supply volume; in summer, the cooling target is set as the sum of solar radiation heat load and occupant heat load, and the air supply volume is set as a multiple of the summer coefficient of the dynamic air supply volume; analysis of the heat load distribution map yielded intelligent natural ventilation strategies for transitional seasons. Decision-making: When the solar radiation heat load is lower than the solar radiation heat load threshold and the outdoor wind speed is higher than the wind speed set threshold, natural ventilation is turned on; otherwise, natural ventilation is turned off. Based on the outdoor temperature analysis, an extreme weather emergency plan is obtained: when the outdoor temperature is greater than or equal to the high temperature threshold, the high temperature emergency plan is activated; when the indoor temperature is less than or equal to the low temperature threshold, the low temperature emergency plan is activated; when the precipitation is greater than the precipitation intensity threshold, the rainstorm emergency plan is activated; otherwise, the normal operation plan is followed. Based on the heat load distribution map, dynamic air supply volume, and temperature adjustment potential, a predictive control strategy is calculated.

9. The method for analyzing the energy-saving potential of air conditioning based on a smart energy management system according to claim 1, characterized in that, The assessment results include assessments of the impact of abnormal equipment and strategies. The methods for obtaining these assessment results include: Analyze the list of equipment with abnormal energy consumption to obtain the impact of abnormal equipment; calculate the energy efficiency index based on the predictive control strategy; analyze the energy efficiency index to obtain the strategy evaluation: when the energy efficiency index is greater than or equal to the excellent threshold, the strategy evaluation is judged as excellent; when the energy efficiency index is less than the excellent threshold but greater than or equal to the good threshold, it is judged as good; when the energy efficiency index is less than the good threshold but greater than or equal to the average threshold, it is judged as average; when the energy efficiency index is less than the average threshold, it is judged as needing optimization.

10. The method for analyzing the energy-saving potential of air conditioning based on a smart energy management system according to claim 1, characterized in that, Methods for obtaining optimized parameter updates include: Analyzing strategy assessments and energy efficiency degradation rates yields optimization suggestions: When the strategy assessment indicates optimization is needed and the season is winter, an adjusted winter coefficient is generated; during the transition season, an adjusted transition season coefficient is generated; and during summer, an adjusted summer coefficient is generated. Analyzing the impact of abnormal equipment and optimization suggestions yields updated optimization parameters: When the optimization suggestion is to adjust the winter coefficient, the winter adjustment coefficient and the winter adjustment amount coefficient are added together based on the energy efficiency degradation rate and the impact of abnormal equipment to obtain an updated winter coefficient; when the optimization suggestion is to adjust the transition season coefficient, the transition season adjustment coefficient and the transition season adjustment amount coefficient are added together to obtain an updated transition season coefficient; and when the optimization suggestion is to adjust the summer coefficient, the summer adjustment coefficient and the summer adjustment amount coefficient are added together to obtain an updated summer coefficient.

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