A smart energy integrated management method and system for dual carbon regulation

Abstract

The present invention provides a smart energy comprehensive management method and system for dual-carbon supervision, which belongs to the field of energy management technology, and specifically includes: utilizing the control parameters of the air-conditioning system in different time period groups and the monitoring results of historical energy consumption data to determine the energy consumption abnormality coefficient and energy consumption abnormality period of the air-conditioning system in different time periods, based on the prediction results of weather data, determining the number of matching time periods with different time period groups within a preset time period, and combining the distribution data of energy consumption abnormality time periods in different time period groups to determine that the energy consumption abnormality probability of the air-conditioning system meets the requirements, determining the matching time period group using the matching time period number, determining the change of energy consumption data of the air-conditioning system under different control parameters in the matching time period group, and utilizing the change of energy consumption data under different control parameters and the energy consumption abnormality probability to determine the dual-carbon supervision strategy of the air-conditioning system, thereby improving the reliability of the supervision and processing of energy consumption data.

Description

Technical Field

[0001] The present invention belongs to the field of energy management technology, and in particular relates to a smart energy integrated management method and system for dual-carbon regulation. Background Art

[0002] In order to achieve energy consumption management, the invention patent application CN202311665779.4 "A method and system for monitoring and managing building energy consumption data" sets up a building energy consumption monitoring and management system to determine whether the energy consumption of the air-conditioning system is normal, thereby facilitating the realization of energy saving. However, the following technical problems exist:

[0003] In the process of energy consumption management of air-conditioning systems, due to the differences in the distribution locations of air-conditioning systems and the differences in the operating parameters of air-conditioning systems, the energy consumption data and carbon emission data of different air-conditioning systems are different. Therefore, if differentiated supervision strategies cannot be determined based on the energy consumption data and carbon emission data of the air-conditioning systems, the supervision reliability of the energy consumption data of the air-conditioning systems cannot be improved.

[0004] In response to the above technical problems, the present invention provides a smart energy comprehensive management method and system for dual carbon regulation. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a smart energy integrated management method for dual carbon regulation is provided.

[0007] A smart energy integrated management method for dual carbon regulation, specifically including:

[0008] S1 determines similar historical periods according to the forecast results of weather data in different periods within a preset time period, and obtains the changes in historical energy consumption data of the air-conditioning system in different similar historical periods;

[0009] S2 When the changes in the historical energy consumption data of the air-conditioning system in different similar historical periods meet the requirements, the historical periods are divided into different period groups using weather data, and the control parameters of the air-conditioning system in different period groups and the monitoring results of the historical energy consumption data are used to determine the energy consumption abnormality coefficient and energy consumption abnormality period of the air-conditioning system in different periods;

[0010] S3 determines the number of matching time periods with different time period groups within a preset time period based on the forecast result of the weather data, and combines the distribution data of energy consumption abnormal time periods in different time period groups to determine that the energy consumption abnormality probability of the air conditioning system meets the requirements, and then proceeds to the next step;

[0011] S4 uses the number of matching time periods to determine the matching time period group, determines the change in energy consumption data of the air-conditioning system in the matching time period group under different control parameters, and uses the change in energy consumption data under different control parameters and the probability of abnormal energy consumption to determine the dual-carbon supervision strategy of the air-conditioning system.

[0012] The beneficial effects of the present invention are:

[0013] Whether the probability of abnormal energy consumption of the air-conditioning system meets the requirements is determined based on the number of matching time periods with different time period groups within a preset time period and the distribution data of abnormal energy consumption time periods in different time period groups. This not only takes into account the matching situation with different time period groups within the preset time period, but also takes into account the difference in the degree of abnormal energy consumption of time period groups caused by the difference in the distribution number of abnormal energy consumption time periods in different time period groups. This realizes the determination of the abnormal energy consumption probability of the air-conditioning system from multiple angles, and also lays the foundation for determining differentiated supervision strategies for the air-conditioning system based on the differences in the abnormal energy consumption probability.

[0014] The dual-carbon supervision strategy of the air-conditioning system is determined by utilizing the changes in energy consumption data under different control parameters and the probability of abnormal energy consumption. The changes in energy consumption data under different control parameters can be used to determine the probability of abnormal changes in the energy consumption data of the air-conditioning system. At the same time, combined with the probability of abnormal energy consumption, differentiated supervision of different air-conditioning systems is achieved, ensuring the reliability of the supervision and processing of the air-conditioning system, laying the foundation for real-time intervention control of air-conditioning systems with abnormal energy consumption, and reducing unnecessary carbon emissions.

[0015] A further technical solution is that the prediction result of the weather data is determined based on the prediction data of the weather data of a designated platform.

[0016] A further technical solution is that the method for determining the similar historical period is:

[0017] Determine the deviation between the forecast results of weather data in different time periods in a preset time period and the forecast results of weather data in different corresponding time periods in a historical time period based on the forecast results of weather data in different time periods in a preset time period;

[0018] Determining similar corresponding time periods based on deviations of forecast results of weather data at different corresponding time periods;

[0019] The similar historical periods in the historical time period are determined according to the quantity ratio of similar corresponding time periods.

[0020] A further technical solution is that the similar corresponding time period is a corresponding time period in which the deviation of the forecast result of the weather data is within a preset deviation range.

[0021] A further technical solution is that when the proportion of the number of similar corresponding time periods is greater than the proportion of the number of preset time periods, the historical time period is determined to be a similar historical period.

[0022] A further technical solution is that the method for determining the dual-carbon regulatory strategy of the air-conditioning system is:

[0023] Determine the deviation of the energy consumption data between different control parameters based on the energy consumption data of the air-conditioning system under different control parameters in different matching time period groups;

[0024] Determine the deviation control parameters in different matching period groups according to the deviation of energy consumption data between different control parameters, and determine the energy consumption variation coefficient of different matching period groups according to the proportion of the deviation control parameters in the control parameters;

[0025] The regulation demand coefficient of the air-conditioning system is determined based on the maximum value of the energy consumption variation coefficient and the sum of the energy consumption abnormality coefficient in different matching time period groups, and the dual-carbon regulation strategy of the air-conditioning system is determined using the regulation demand coefficient.

[0026] A further technical solution is to use the regulatory demand coefficient to determine the dual-carbon regulatory strategy of the air-conditioning system, specifically including:

[0027] The matching supervision strategy of the air-conditioning system is determined by using the supervision demand coefficient, and the dual-carbon supervision strategy of the air-conditioning system is determined according to the matching supervision strategy.

[0028] A further technical solution is that the dual-carbon supervision strategy of the air-conditioning system is divided according to whether it is connected to a unified energy consumption supervision platform, wherein the preset dual-carbon supervision strategy is to connect to a unified energy consumption supervision platform.

[0029] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned smart energy integrated management method for dual carbon regulation when running the computer program.

[0030] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0033] Figure 1 It is a flow chart of a smart energy integrated management method for dual carbon regulation;

[0034] Figure 2 is a flow chart of the method for determining similar historical periods;

[0035] Figure 3 It is a flow chart to determine whether the changes in the historical energy consumption data of the air-conditioning system in a similar historical period meet the requirements;

[0036] Figure 4 It is a flow chart of a method for determining an abnormal coefficient of energy consumption in a time period;

[0037] Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0039] In the process of energy consumption management of air-conditioning systems, due to the differences in the distribution locations of air-conditioning systems, the energy consumption data and carbon emission data of different air-conditioning systems are different. Therefore, how to determine differentiated supervision strategies based on the energy consumption data and carbon emission data of different air-conditioning systems has become a technical problem that needs to be solved urgently.

[0040] Changes in historical energy consumption data: Determined based on the deviations in historical energy consumption data of air-conditioning systems in different similar historical periods.

[0041] The changes in the historical energy consumption data of the air-conditioning system meet the requirements: using the historical energy consumption data of the air-conditioning system in different similar historical periods, determine the median of the historical energy consumption data of the air-conditioning system in different similar historical periods, and use it as the benchmark energy consumption data; based on the deviation between the historical energy consumption data of the air-conditioning system in different similar historical periods and the benchmark energy consumption data, determine the deviation historical period in the similar historical period; based on the proportion of the number of deviation historical periods, when the proportion of the number of deviation historical periods is greater than 0.5, it is determined that the changes in the historical energy consumption data of the air-conditioning system in similar historical periods do not meet the requirements.

[0042] Abnormal energy consumption coefficient of air-conditioning system in different time periods: Determine the deviation of control parameters of air-conditioning system in different time periods in different time period groups, and determine the time period with similar parameters by using the deviation of control parameters;

[0043] Determine the average value of the historical energy consumption data for the similar parameter period through the historical energy consumption data for the similar parameter period, and use it as the reference energy consumption data, and use the average value of the deviation between the historical energy consumption data for the similar parameter period and the historical energy consumption data for other reference similar periods as the average value of the energy consumption data;

[0044] Based on the ratio of the average value of the energy consumption data to the reference energy consumption data, the energy consumption anomaly coefficient of the time period with similar parameters is determined.

[0045] Abnormal energy consumption period: the period when the abnormal energy consumption coefficient is greater than 0.6.

[0046] Determine whether the abnormal probability of energy consumption of the air-conditioning system meets the requirements: determine the matching time period group in the time period group according to the number of matching time periods with different time period groups within a preset time period, determine the group abnormality coefficient of the matching time period group by using the proportion of the number of time periods with abnormal energy consumption in the matching time period group, determine the abnormal probability of energy consumption of the air-conditioning system by the average value of the group abnormality coefficients of different matching time period groups, and determine whether the abnormal probability of energy consumption of the air-conditioning system meets the requirements in combination with the abnormal probability threshold. When the abnormal probability of energy consumption is greater than 0.7, it is determined that the abnormal probability of energy consumption of the air-conditioning system does not meet the requirements.

[0047] Dual-carbon supervision strategy for air-conditioning systems: Based on the energy consumption data of air-conditioning systems under different control parameters in different matching time groups, determine the deviation of energy consumption data between different control parameters, determine the deviation control parameters in different matching time groups based on the deviation of energy consumption data between different control parameters, and determine the energy consumption variation coefficient of different matching time groups based on the proportion of deviation control parameters in the control parameters;

[0048] The regulation demand coefficient of the air-conditioning system is determined based on the maximum value of the energy consumption variation coefficient and the sum of the energy consumption abnormality coefficient in different matching time period groups, and the dual-carbon regulation strategy of the air-conditioning system is determined using the regulation demand coefficient.

[0049] Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a smart energy integrated management method for dual carbon regulation is provided, which specifically includes:

[0050] S1 determines similar historical periods according to the forecast results of weather data in different periods within a preset time period, and obtains the changes in historical energy consumption data of the air-conditioning system in different similar historical periods;

[0051] S2 When the changes in the historical energy consumption data of the air-conditioning system in different similar historical periods meet the requirements, the historical periods are divided into different period groups using weather data, and the control parameters of the air-conditioning system in different period groups and the monitoring results of the historical energy consumption data are used to determine the energy consumption abnormality coefficient and energy consumption abnormality period of the air-conditioning system in different periods;

[0052] S3 determines the number of matching time periods with different time period groups within a preset time period based on the forecast result of the weather data, and combines the distribution data of energy consumption abnormal time periods in different time period groups to determine that the energy consumption abnormality probability of the air conditioning system meets the requirements, and then proceeds to the next step;

[0053] S4 uses the number of matching time periods to determine the matching time period group, determines the change in energy consumption data of the air-conditioning system in the matching time period group under different control parameters, and uses the change in energy consumption data under different control parameters and the probability of abnormal energy consumption to determine the dual-carbon supervision strategy of the air-conditioning system.

[0054] Furthermore, the prediction result of the weather data is determined based on the prediction data of the weather data of the designated platform.

[0055] Specifically, Figure 2 As shown, the method for determining the similar historical period is:

[0056] Determine the deviation between the forecast results of weather data in different time periods in a preset time period and the forecast results of weather data in different corresponding time periods in a historical time period based on the forecast results of weather data in different time periods in a preset time period;

[0057] Determining similar corresponding time periods based on deviations of forecast results of weather data at different corresponding time periods;

[0058] The similar historical periods in the historical time period are determined according to the quantity ratio of similar corresponding time periods.

[0059] Furthermore, the similar corresponding time period is a corresponding time period in which the deviation of the forecast result of the weather data is within a preset deviation range.

[0060] Optionally, when the proportion of the number of similar corresponding time periods is greater than the proportion of the number of preset time periods, the historical time period is determined to be a similar historical period.

[0061] It should be noted that if Figure 3As shown in the figure, it is determined that the changes in the historical energy consumption data of the air-conditioning system in a similar historical period meet the requirements, including:

[0062] Using the historical energy consumption data of air-conditioning systems in different similar historical periods, determine the median of the historical energy consumption data of air-conditioning systems in different similar historical periods, and use it as the benchmark energy consumption data;

[0063] Determine a deviation historical period in the similar historical period according to the deviation between the historical energy consumption data of the air-conditioning system in different similar historical periods and the benchmark energy consumption data;

[0064] Based on the proportion of the number of the deviation historical periods, it is determined whether the change in the historical energy consumption data of the air-conditioning system in the similar historical period meets the requirements.

[0065] Furthermore, the deviation historical period is a similar historical period in which the deviation between the historical energy consumption data and the benchmark energy consumption data is not within a preset energy consumption deviation range.

[0066] It should also be noted that when the proportion of the number of deviation historical periods is greater than the proportion of the number of preset periods, it is determined that the change in the historical energy consumption data of the air-conditioning system in the similar historical period does not meet the requirements.

[0067] Optionally, determining whether changes in historical energy consumption data of air-conditioning systems in similar historical periods meet requirements includes:

[0068] Using the historical energy consumption data of air-conditioning systems in different similar historical periods, determine the median of the historical energy consumption data of air-conditioning systems in different similar historical periods, and use it as the benchmark energy consumption data;

[0069] According to the deviations between the historical energy consumption data of the air-conditioning system in different similar historical periods and the benchmark energy consumption data, an average value of the deviations in different similar historical periods is determined;

[0070] Based on the average values ​​of the deviations of different similar historical periods, it is determined whether the changes in the historical energy consumption data of the air-conditioning system in the similar historical periods meet the requirements.

[0071] In another embodiment, determining that the change in historical energy consumption data of the air-conditioning system in a similar historical period meets the requirements specifically includes:

[0072] Using the historical energy consumption data of air-conditioning systems in different similar historical periods, determine the median of the historical energy consumption data of air-conditioning systems in different similar historical periods, and use it as the benchmark energy consumption data;

[0073] According to the deviations between the historical energy consumption data of the air-conditioning system in different similar historical periods and the benchmark energy consumption data, the average values ​​of the deviations in different similar historical periods are determined; when the average values ​​of the deviations in different similar historical periods do not meet the requirements, it is determined that the changes in the historical energy consumption data of the air-conditioning system in the similar historical periods meet the requirements;

[0074] When the average value of the deviations of different similar historical periods meets the requirements:

[0075] According to the deviation amount between the historical energy consumption data of the air-conditioning system in different similar historical periods and the benchmark energy consumption data, when it is determined that there is no deviation historical period in the similar historical periods, it is determined that the change of the historical energy consumption data of the air-conditioning system in the similar historical periods meets the requirements;

[0076] When there are deviation historical cycles in similar historical cycles:

[0077] Acquire the number of the deviation historical cycles, and when the number of the deviation historical cycles is greater than the preset number of cycles, determine that the change of the historical energy consumption data of the air-conditioning system in the similar historical cycle does not meet the requirements;

[0078] When the number of the deviation history cycles is not greater than the preset number of cycles:

[0079] Determine the deviation proportional factor based on the number of deviation historical periods and the proportion of the number of different deviation historical periods. When the deviation proportional factor is within a preset proportional coefficient range, obtain the deviation amounts of different deviation historical periods. When the average values ​​of the deviation amounts of different deviation historical periods do not meet the requirements, determine that the change in the historical energy consumption data of the air-conditioning system in the similar historical period does not meet the requirements.

[0080] When the deviation proportional factor is not within the preset proportional coefficient range or the average value of the deviations in different deviation historical periods meets the requirements:

[0081] Based on the deviation amounts of different deviation historical periods, the energy consumption deviation coefficient is determined, and the comprehensive energy consumption deviation coefficient is determined in combination with the deviation proportional factor. The comprehensive energy consumption deviation coefficient is used to determine whether the changes in the historical energy consumption data of the air-conditioning system in the similar historical period meet the requirements.

[0082] Specifically, the historical periods are divided into different period groups, including:

[0083] According to the weather data of different historical periods, the deviation of weather data in different dimensions of different historical periods is determined;

[0084] The deviation amount average is determined by the deviation amount of weather data in different dimensions, and the historical time periods are divided into different time period groups based on the deviation amount average.

[0085] It should be noted that the historical periods are divided into different period groups based on the average value of the deviation, including:

[0086] The historical periods whose average deviation values ​​are within the preset deviation range are divided into the same period group.

[0087] Furthermore, the control parameters include the control temperature and wind speed of the air-conditioning system.

[0088] Specifically, Figure 4 As shown, the method for determining the energy consumption abnormality coefficient of the time period is:

[0089] Determine the deviation of the control parameters of the air conditioning system in different time periods according to the control parameters of the air conditioning system in different time periods in different time period groups, and determine the parameter similarity time period according to the deviation of the control parameters;

[0090] Determine the average value of the historical energy consumption data for the similar parameter period through the historical energy consumption data for the similar parameter period, and use it as the reference energy consumption data, and use the average value of the deviation between the historical energy consumption data for the similar parameter period and the historical energy consumption data for other reference similar periods as the average value of energy consumption data;

[0091] Based on the ratio of the average value of the energy consumption data to the reference energy consumption data, the energy consumption anomaly coefficient of the time period with similar parameters is determined.

[0092] Furthermore, the parameter similarity period is a period during which the deviation of the control parameter is within a preset parameter deviation range.

[0093] Optionally, the energy consumption anomaly coefficient of the reference similar time period ranges from 0 to 1, wherein when the energy consumption anomaly coefficient of the reference similar time period is greater than a preset anomaly coefficient threshold, the reference similar time period is determined to be an energy consumption anomaly period.

[0094] Furthermore, the method for determining the energy consumption abnormality coefficient of the time period is:

[0095] Determine the deviation of the control parameters of the air conditioning system in different time periods according to the control parameters of the air conditioning system in different time periods in different time period groups, and determine the parameter similarity time period according to the deviation of the control parameters;

[0096] Determine the average value of the historical energy consumption data for the period with similar parameters through the historical energy consumption data for the period with similar parameters, and use it as the reference energy consumption data;

[0097] Based on the deviation between the historical energy consumption data of the time period with similar parameters and the reference energy consumption data, the energy consumption abnormality coefficient of the time period with similar parameters is determined.

[0098] It should also be noted that the energy consumption anomaly coefficient of the parameter-similar period is determined based on the deviation between the historical energy consumption data of the parameter-similar period and the reference energy consumption data, and the ratio of the reference energy consumption data.

[0099] Optionally, the method for determining the energy consumption abnormality coefficient of the time period is:

[0100] Determine the deviation of the control parameters of the air conditioning system in different time periods according to the control parameters of the air conditioning system in different time periods in different time period groups, and determine the parameter similarity time period according to the deviation of the control parameters;

[0101] Determine the deviation between the historical energy consumption data of the period with similar parameters and the historical energy consumption data of other periods with similar parameters through the historical energy consumption data of the period with similar parameters, and when the deviation between the historical energy consumption data of other periods with similar parameters is within a preset deviation range, determine that the period does not belong to an abnormal energy consumption period;

[0102] When there are other similar parameter periods whose deviation is not within the preset deviation range in the similar parameter period:

[0103] Other parameter-similar time periods whose deviation amount is not within the preset deviation range are regarded as deviation time periods, and when the proportion of the deviation time periods in the parameter-similar time periods is not greater than the preset proportion threshold:

[0104] Obtaining deviation amounts of different deviation time periods, and when there is no deviation time period in which the deviation amount is greater than a preset deviation threshold, determining that the time period does not belong to an abnormal energy consumption time period;

[0105] When there is a deviation period whose deviation is greater than the preset deviation threshold or the proportion of the deviation period in the parameter similarity period is greater than the preset proportion threshold:

[0106] Determine the average value of the historical energy consumption data for the similar parameter period through the historical energy consumption data for the similar parameter period, and use it as the reference energy consumption data, and use the average value of the deviation between the historical energy consumption data for the similar parameter period and the historical energy consumption data for other reference similar periods as the average value of energy consumption data;

[0107] Based on the ratio of the average value of the energy consumption data to the reference energy consumption data, the energy consumption anomaly coefficient of the time period with similar parameters is determined.

[0108] Further, determining whether the probability of abnormal energy consumption of the air-conditioning system meets the requirements specifically includes:

[0109] Determine matching weight coefficients of different time period groups according to the number of matching time periods with different time period groups within a preset time period, and determine matching time period groups in the time period groups using the matching weight coefficients;

[0110] Based on the distribution data of energy consumption abnormal time periods in different matching time period groups, determining the time period ratio of energy consumption abnormal time periods in different matching time period groups, and determining the group abnormality coefficient of the matching time period group by using the time period ratio of energy consumption abnormal time periods in the matching time period groups;

[0111] The energy consumption abnormality probability of the air-conditioning system is determined by the average value of the group abnormality coefficients of different matching time period groups, and whether the energy consumption abnormality probability of the air-conditioning system meets the requirements is determined in combination with the abnormal probability threshold.

[0112] Specifically, the matching weight coefficient of the time period group is determined according to the proportion of the number of matching time periods within a preset time period.

[0113] It should be noted that, when the abnormal probability of energy consumption of the air-conditioning system is less than the abnormal probability threshold, it is determined that the abnormal probability of energy consumption of the air-conditioning system meets the requirement.

[0114] Furthermore, when the probability of abnormal energy consumption of the air-conditioning system does not meet the requirement, the dual-carbon supervision strategy of the air-conditioning system is determined by using a preset dual-carbon supervision strategy.

[0115] It should also be noted that determining that the probability of abnormal energy consumption of the air-conditioning system meets the requirements specifically includes:

[0116] S31 determines matching weight coefficients of different time period groups according to the number of matching time periods with different time period groups within a preset time period;

[0117] S32 determines the period quantity ratio of the energy consumption abnormal period in different period groups based on the distribution data of the energy consumption abnormal period in different period groups, and determines the group abnormality coefficient of the period group by using the period quantity ratio of the energy consumption abnormal period in the period group and the energy consumption abnormality coefficient of different energy consumption abnormal period;

[0118] S33 determines the energy consumption abnormality probability of the air-conditioning system through the matching weight coefficients of different time period groups and the group abnormality coefficients of the time period groups, and determines whether the energy consumption abnormality probability of the air-conditioning system meets the requirements in combination with the abnormal probability threshold.

[0119] Optionally, the above step S32 includes the following contents:

[0120] When it is determined in step S321 that there is a matching time period group in the time period group according to the matching weight coefficients with different time period groups within the preset time period, the process proceeds to step S322; when there is no matching time period group in the time period group, the process proceeds to step S323.

[0121] S322 determines the time period ratio of the energy consumption abnormal time period in different time period groups based on the distribution data of the energy consumption abnormal time period in different time period groups. When there is a matching time period group whose time period ratio is greater than the preset number ratio, it is determined that the energy consumption abnormal probability of the air-conditioning system does not meet the requirement. When there is no matching time period group whose time period ratio is not greater than the preset number ratio, it proceeds to step S324.

[0122] S323 obtains the number of matching time period groups, and determines the screening abnormality coefficient in combination with the ratio of the number of energy consumption abnormality periods in different matching time period groups. When the screening abnormality coefficient does not meet the requirement, it is determined that the energy consumption abnormality probability of the air-conditioning system does not meet the requirement. When the screening abnormality coefficient meets the requirement, the process proceeds to step S324.

[0123] S324 determines the group abnormality coefficient of the time period group by using the time period number ratio of the energy consumption abnormality time period in the time period group and the energy consumption abnormality coefficients of different energy consumption abnormality time periods, and proceeds to step S33.

[0124] Optionally, the above step S33 includes the following contents:

[0125] S331 determines the corrected abnormal coefficients of different time period groups through the matching weight coefficients of different time period groups and the group abnormal coefficients of the time period groups. When there is a time period group whose corrected abnormal coefficient does not meet the requirements, it is determined that the energy consumption abnormal probability of the air-conditioning system does not meet the requirements. When there is no time period group whose corrected abnormal coefficient does not meet the requirements, it proceeds to step S332;

[0126] S332 obtains the number of time period groups in which the correction abnormality coefficient is within the preset correction coefficient interval. When the number of time period groups in which the correction abnormality coefficient is within the preset correction coefficient interval does not meet the requirement, it is determined that the energy consumption abnormality probability of the air-conditioning system does not meet the requirement. When the number of time period groups in which the correction abnormality coefficient is within the preset correction coefficient interval meets the requirement, the process proceeds to step S333;

[0127] S333 determines the abnormal probability of energy consumption of the air-conditioning system through the matching weight coefficients of different time period groups and the group abnormality coefficients of the time period groups, and determines whether the abnormal probability of energy consumption of the air-conditioning system meets the requirements in combination with the abnormal probability threshold.

[0128] Specifically, the method for determining the dual-carbon regulatory strategy of the air-conditioning system is:

[0129] Determine the deviation of the energy consumption data between different control parameters based on the energy consumption data of the air-conditioning system under different control parameters in different matching time period groups;

[0130] Determine the deviation control parameters in different matching period groups according to the deviation of energy consumption data between different control parameters, and determine the energy consumption variation coefficient of different matching period groups according to the proportion of the deviation control parameters in the control parameters;

[0131] The regulation demand coefficient of the air-conditioning system is determined based on the maximum value of the energy consumption variation coefficient and the sum of the energy consumption abnormality coefficient in different matching time period groups, and the dual-carbon regulation strategy of the air-conditioning system is determined using the regulation demand coefficient.

[0132] Optionally, the dual-carbon regulation strategy of the air-conditioning system is determined by using the regulation demand coefficient, specifically including:

[0133] The matching supervision strategy of the air-conditioning system is determined by using the supervision demand coefficient, and the dual-carbon supervision strategy of the air-conditioning system is determined according to the matching supervision strategy.

[0134] A further technical solution is that the dual-carbon supervision strategy of the air-conditioning system is divided according to whether it is connected to a unified energy consumption supervision platform, wherein the preset dual-carbon supervision strategy is to connect to a unified energy consumption supervision platform.

[0135] Embodiment 2 In the second aspect, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned smart energy integrated management method for dual carbon regulation when running the computer program.

[0136] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0137] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0138] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A smart energy integrated management method for dual carbon regulation, characterized in that: Specifically include: Determine similar historical periods based on the forecast results of weather data at different time periods within a preset time period, and obtain changes in historical energy consumption data of air-conditioning systems in different similar historical periods; When the changes in the historical energy consumption data of the air-conditioning system in different similar historical periods meet the requirements, the historical time periods are divided into different time period groups using weather data, and the control parameters of the air-conditioning system in different time period groups and the monitoring results of the historical energy consumption data are used to determine the energy consumption abnormality coefficient and energy consumption abnormality time period of the air-conditioning system in different time periods; Based on the forecast result of the weather data, the number of matching time periods with different time period groups within the preset time period is determined, and when the probability of abnormal energy consumption of the air-conditioning system meets the requirements is determined in combination with the distribution data of abnormal energy consumption time periods in different time period groups, the next step is entered; Determine a matching period group using the number of matching periods, determine changes in energy consumption data of the air-conditioning system in the matching period group under different control parameters, and determine a dual-carbon supervision strategy for the air-conditioning system using changes in energy consumption data under different control parameters and energy consumption abnormality probabilities; Determining that the probability of abnormal energy consumption of the air conditioning system meets the requirements specifically includes: Determine matching weight coefficients of different time period groups according to the number of matching time periods with different time period groups within a preset time period, and determine matching time period groups in the time period groups using the matching weight coefficients; Based on the distribution data of energy consumption abnormal time periods in different matching time period groups, determining the time period ratio of energy consumption abnormal time periods in different matching time period groups, and determining the group abnormality coefficient of the matching time period group by using the time period ratio of energy consumption abnormal time periods in the matching time period groups; Determine the energy consumption abnormality probability of the air-conditioning system by the average value of the group abnormality coefficients of different matching time period groups, and determine whether the energy consumption abnormality probability of the air-conditioning system meets the requirements in combination with the abnormal probability threshold; The method for determining the dual carbon regulation strategy of the air conditioning system is: Determine the deviation of the energy consumption data between different control parameters based on the energy consumption data of the air-conditioning system under different control parameters in different matching time period groups; Determine the deviation control parameters in different matching period groups according to the deviation of energy consumption data between different control parameters, and determine the energy consumption variation coefficient of different matching period groups according to the proportion of the deviation control parameters in the control parameters; Determine the supervision demand coefficient of the air-conditioning system based on the maximum value of the energy consumption variation coefficient and the sum of the energy consumption abnormality coefficient in different matching time period groups, and determine the dual-carbon supervision strategy of the air-conditioning system using the supervision demand coefficient; The dual-carbon supervision strategy of the air-conditioning system is divided according to whether it is connected to a unified energy consumption supervision platform, wherein the preset dual-carbon supervision strategy is to connect to a unified energy consumption supervision platform.

2. The smart energy integrated management method for dual carbon regulation according to claim 1, characterized in that: The forecast result of the weather data is determined according to the forecast data of the weather data of the designated platform.

3. The smart energy integrated management method for dual carbon regulation according to claim 1 is characterized in that: The method for determining the similar historical period is: Determine the deviation between the forecast results of weather data in different time periods in a preset time period and the forecast results of weather data in different corresponding time periods in a historical time period based on the forecast results of weather data in different time periods in a preset time period; Determining similar corresponding time periods based on deviations of forecast results of weather data at different corresponding time periods; The similar historical periods in the historical time period are determined according to the quantity ratio of similar corresponding time periods.

4. The smart energy integrated management method for dual carbon regulation as claimed in claim 3 is characterized in that: The similar corresponding time period is a corresponding time period in which the deviation of the forecast result of the weather data is within a preset deviation range.

5. The smart energy integrated management method for dual carbon regulation according to claim 1, characterized in that: Determine that the changes in the historical energy consumption data of the air conditioning system in similar historical periods meet the requirements, including: Using the historical energy consumption data of air-conditioning systems in different similar historical periods, determine the median of the historical energy consumption data of air-conditioning systems in different similar historical periods, and use it as the benchmark energy consumption data; Determine a deviation historical period in the similar historical period according to the deviation between the historical energy consumption data of the air-conditioning system in different similar historical periods and the benchmark energy consumption data; Based on the proportion of the number of the deviation historical periods, it is determined whether the change in the historical energy consumption data of the air-conditioning system in the similar historical period meets the requirements.

6. The smart energy integrated management method for dual carbon regulation according to claim 5, characterized in that: The deviation historical period is a similar historical period in which the deviation between the historical energy consumption data and the benchmark energy consumption data is not within a preset energy consumption deviation range.

7. The smart energy integrated management method for dual carbon regulation according to claim 1, characterized in that: The matching weight coefficient of the time period group is determined according to the proportion of the number of matching time periods within a preset time period.

8. The smart energy integrated management method for dual carbon regulation according to claim 1, characterized in that: When the abnormal probability of energy consumption of the air-conditioning system is less than the abnormal probability threshold, it is determined that the abnormal probability of energy consumption of the air-conditioning system meets the requirement.

9. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, an intelligent energy integrated management method for dual carbon regulation as described in any one of claims 1-8 is executed.

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