Air conditioner load analysis method and system based on data fusion and carbon emission reduction accounting

Abstract

The application relates to the technical field of power systems, and discloses an air conditioner load analysis method and system based on data fusion and carbon emission reduction accounting. The method comprises the following steps: acquiring air conditioner related data in different dimensions, and performing fusion processing on all the air conditioner related data to obtain air conditioner load fusion knowledge; a plurality of benchmark scenes are set based on first power consumption behavior characteristics, and the first air conditioner carbon emission of each benchmark scene is determined according to the air conditioner load fusion knowledge; a project scene is set based on second power consumption behavior characteristics, and the second air conditioner carbon emission of the project scene is determined according to the air conditioner load fusion knowledge; the air conditioner carbon emission reduction is obtained based on each first air conditioner carbon emission and second air conditioner carbon emission, and the air conditioner carbon emission reduction and the air conditioner power consumption and charging data obtained by analyzing the air conditioner load fusion knowledge are integrated to form an air conditioner load analysis result. The application deeply couples the air conditioner load data and the carbon emission reduction accounting, and helps the energy system to upgrade to intelligentization.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and system for analyzing air conditioning load based on data fusion and carbon emission reduction accounting. Background Technology

[0002] With the deep integration of smart grids and user-side digitalization, residential electricity consumption behavior analysis and energy-saving guidance have become important directions for upgrading power grid services. Currently, residential smart air conditioner load data (such as analysis cycle, device serial number, rated power), power grid operation data (such as time-of-use pricing, tiered thresholds), and resident activity data (such as energy-saving notifications, reward rules) are stored on heterogeneous platforms such as load interaction platforms, power grid business systems, and the State Grid online system. This results in fragmented data resources and a lack of effective correlation. Existing electricity bill generation technologies only calculate electricity consumption based on single power grid data, with output limited to total electricity costs and tiered electricity consumption. They fail to present detailed information such as peak-valley distribution of air conditioner load and tiered pricing trigger points, making it difficult for residents to understand the impact of air conditioner consumption on electricity costs. This leads to insufficient user initiative in participating in energy-saving responses, resulting in low practicality and user stickiness of electricity bills.

[0003] As a crucial component of adjustable loads in the power system, residential air conditioning's carbon emission reduction value through grid-based energy-saving interactions is increasingly evident. However, existing technologies, such as a demand response mechanism modeling method that considers real-time carbon emission reduction, while involving carbon cost modeling in demand response, lack standardized carbon emission reduction accounting methods specifically for residential air conditioning. In particular, they lack technical specifications for key aspects such as baseline setting and emission reduction quantification, resulting in inaccurate measurement of emission reduction effects. Currently, there is no methodology for carbon emission reduction through residential air conditioning's participation in grid interaction, creating a technological gap and hindering the effective establishment of a residential-side carbon emission accounting and verification system.

[0004] Therefore, it is necessary to build a load analysis system that integrates multi-source data and a standardized carbon emission reduction methodology to improve the quality of power grid services and the proactive response of residents' energy-saving behaviors. Summary of the Invention

[0005] To address the shortcomings of existing technologies in multi-dimensional fusion of residential electricity consumption data and quantification of carbon emission reduction through air conditioning power consumption, this invention provides a method and system for analyzing air conditioning load based on data fusion and carbon emission reduction accounting.

[0006] In a first aspect, embodiments of the present invention provide a method for analyzing air conditioning loads based on data fusion and carbon emission reduction accounting, including:

[0007] Obtain air conditioning-related data from different dimensions, and perform fusion processing on all the air conditioning-related data to obtain air conditioning load fusion knowledge;

[0008] Several benchmark scenarios are set based on the first electricity consumption behavior characteristics that reflect that the air conditioner does not participate in energy-saving activities, and the first air conditioner carbon emission of each benchmark scenario is determined according to the air conditioner load fusion knowledge.

[0009] The project scenario is set based on the second electricity consumption behavior characteristics that reflect the participation of air conditioners in energy-saving activities, and the second air conditioner carbon emissions of the project scenario are determined according to the air conditioner load fusion knowledge.

[0010] The carbon emission reduction of air conditioning is obtained based on the carbon emissions of the first air conditioner and the carbon emissions of the second air conditioner. The carbon emission reduction of air conditioning is then integrated with the air conditioning electricity consumption and billing data obtained by analyzing the air conditioning load fusion knowledge to form the air conditioning load analysis result.

[0011] Preferably, the step of acquiring air conditioning-related data from different dimensions and fusing all the air conditioning-related data to obtain air conditioning load fusion knowledge includes:

[0012] It connects to several power service-related platforms to obtain air conditioning load data, power grid operation data, and residential activity data.

[0013] The air conditioning load data, the power grid operation data, and the residents' activity data are correlated, fused, and structured to obtain an air conditioning load data map;

[0014] Rule-based reasoning is performed on the air conditioning load data map to obtain air conditioning load association rules;

[0015] Based on the air conditioning load data map and the air conditioning load association rules, air conditioning load fusion knowledge is formed.

[0016] Preferably, the step of linking, fusing, and structurally modeling the air conditioning load data, the power grid operation data, and the residential activity data to obtain an air conditioning load data map includes:

[0017] The air conditioning load data, the power grid operation data, and the residential activity data are preprocessed to obtain a standardized dataset.

[0018] Based on preset ontology relationships, entity recognition and attribute extraction are performed on the standardized dataset to obtain a multi-source entity set;

[0019] The multi-source entity set is aligned with its own source entities to obtain the mapping relationship between entities;

[0020] Using entities as nodes and the mapping relationships between the entities as edges, an air conditioning load data map containing hierarchical structure and associated attributes is constructed.

[0021] Preferably, the step of setting several benchmark scenarios based on the first electricity consumption behavior characteristics reflecting that the air conditioner does not participate in energy-saving activities, and determining the first air conditioner carbon emission of each benchmark scenario according to the air conditioner load fusion knowledge, includes:

[0022] The first benchmark scenario, the second benchmark scenario, and the third benchmark scenario are set based on the first electricity consumption behavior characteristic that reflects that the air conditioner does not participate in energy-saving activities.

[0023] The first air conditioning carbon emission of the first benchmark scenario is determined based on the air conditioning load fusion knowledge, wherein the first benchmark scenario is a scenario in which the air conditioner operates according to the average electricity consumption mode of the power grid operation area.

[0024] The first air conditioning carbon emission of the second benchmark scenario is determined based on the air conditioning load fusion knowledge, wherein the second benchmark scenario is a scenario in which the air conditioner operates according to the user's historical electricity consumption pattern.

[0025] The first air conditioning carbon emission of the third benchmark scenario is determined based on the air conditioning load fusion knowledge, wherein the third benchmark scenario is a scenario in which the air conditioner operates according to a preset standard benchmark load curve.

[0026] Preferably, determining the first air conditioning carbon emission of the first benchmark scenario based on the air conditioning load fusion knowledge includes:

[0027] Based on the air conditioning load data map, parameters are extracted from the first benchmark scenario to obtain a first parameter set, wherein the first parameter set includes the total electricity consumption of residents in the power grid operating area, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate, the total number of households in the power grid operating area, and the advanced coefficient. The comprehensive marginal emission factor of the power grid is obtained based on the marginal emission factor of the power grid electricity and the marginal emission factor of the power grid capacity.

[0028] Based on the air conditioning load association rules, the total electricity consumption of residents in the power grid operating area, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate, the total number of households in the power grid operating area, and the advancement coefficient are calculated to obtain the first air conditioning carbon emission of the first benchmark scenario.

[0029] Preferably, determining the first air conditioning carbon emission of the second benchmark scenario based on the air conditioning load fusion knowledge includes:

[0030] Based on the air conditioning load data map, parameters are extracted from the second benchmark scenario to obtain a second parameter set. The second parameter set includes the residential electricity consumption of the user in the same historical period corresponding to the calculation period, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate, the meteorological factor correlation correction coefficient and the work and rest characteristic correlation correction coefficient. The comprehensive marginal emission factor of the power grid is obtained based on the power grid electricity marginal emission factor and the power grid capacity marginal emission factor.

[0031] Based on the air conditioning load association rules, the user's historical electricity consumption for the corresponding period, the power grid's comprehensive marginal emission factor, the power grid's transmission and distribution loss rate, the meteorological factor association correction coefficient, and the work and rest characteristic association correction coefficient are calculated to obtain the first air conditioning carbon emission of the second benchmark scenario.

[0032] Preferably, determining the first air conditioning carbon emission of the third baseline scenario based on the air conditioning load fusion knowledge includes:

[0033] Based on the air conditioning load data map, parameters are extracted from the third baseline scenario to obtain a third parameter set. The third parameter set includes the residential baseline load at each time point during the calculation period, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate, and the total number of households in the power grid operating area. The comprehensive marginal emission factor of the power grid is obtained based on the marginal emission factor of the power grid electricity and the marginal emission factor of the power grid capacity.

[0034] Based on the air conditioning load association rules, the baseline residential load at each time point during the calculation period, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate, and the total number of households in the power grid operating area are calculated to obtain the first air conditioning carbon emission of the third benchmark scenario.

[0035] Preferably, the step of setting up a project scenario based on second electricity consumption behavior characteristics reflecting air conditioning participation in energy-saving activities, and determining the second air conditioning carbon emissions of the project scenario based on the air conditioning load fusion knowledge, includes:

[0036] Project scenarios are set based on the second electricity consumption behavior characteristics that reflect the participation of air conditioners in energy-saving activities, wherein the project scenario is the operating scenario when the air conditioner participates in energy-saving activities;

[0037] Based on the air conditioning load data map, parameters are extracted from the project scenario to obtain a fourth parameter set, which includes the power consumption of air conditioning participating in energy-saving activities, the comprehensive marginal emission factor of the power grid, and the power grid transmission and distribution loss rate. The comprehensive marginal emission factor of the power grid is obtained based on the marginal emission factor of the power grid electricity and the marginal emission factor of the power grid capacity.

[0038] Based on the air conditioning load association rules, the power consumption of the air conditioner participating in the energy-saving activities, the comprehensive marginal emission factor of the power grid, and the power grid transmission and distribution loss rate are calculated to obtain the second air conditioning carbon emission of the project scenario.

[0039] Preferably, the step of obtaining air conditioning carbon emission reduction based on each of the first and second air conditioning carbon emissions, and integrating the air conditioning carbon emission reduction with air conditioning electricity consumption and billing data obtained from analyzing the air conditioning load fusion knowledge to form an air conditioning load analysis result includes:

[0040] Extreme value screening is performed on each of the first air conditioner carbon emissions to obtain the maximum air conditioner carbon emissions in the benchmark scenario, and the difference calculation is performed on the maximum air conditioner carbon emissions in the benchmark scenario and the second air conditioner carbon emissions to obtain the air conditioner carbon emission reduction.

[0041] The air conditioning load fusion knowledge is used to extract indicators and perform correlation calculations to obtain air conditioning electricity consumption and billing data. The air conditioning carbon emission reduction and the air conditioning electricity consumption and billing data are then integrated to form air conditioning load analysis results. The air conditioning electricity consumption and billing data include the electricity consumption and electricity cost of air conditioning at different time periods and the electricity consumption and electricity cost of air conditioning in different tiered electricity price ranges.

[0042] Secondly, embodiments of the present invention provide an air conditioning load analysis system based on data fusion and carbon emission reduction accounting, comprising:

[0043] The data fusion processing module is used to acquire air conditioning-related data from different dimensions and to fuse all the air conditioning-related data to obtain air conditioning load fusion knowledge.

[0044] The benchmark carbon emission determination module is used to set several benchmark scenarios based on the first electricity consumption behavior characteristics that reflect that the air conditioner does not participate in energy-saving activities, and to determine the first air conditioner carbon emission of each benchmark scenario according to the air conditioner load fusion knowledge.

[0045] The project carbon emission determination module is used to set up project scenarios based on the second electricity consumption behavior characteristics that reflect the participation of air conditioning in energy-saving activities, and to determine the second air conditioning carbon emission of the project scenario according to the air conditioning load fusion knowledge.

[0046] The analysis result generation module is used to obtain the air conditioning carbon emission reduction based on each of the first air conditioning carbon emission and the second air conditioning carbon emission, and to integrate the air conditioning carbon emission reduction with the air conditioning electricity consumption and billing data obtained by analyzing the air conditioning load fusion knowledge to form the air conditioning load analysis result.

[0047] Compared with existing technologies, the air conditioning load analysis method and system based on data fusion and carbon emission reduction accounting of this invention have the following advantages: Firstly, by refining the electricity consumption and billing data based on the air conditioning load data map and calculating the carbon emission difference between the baseline scenario and the project scenario, it quantitatively correlates the time-of-use electricity consumption, tiered electricity pricing, and carbon emission reduction of individual air conditioning units for the first time. This breaks through the limitation of traditional electricity consumption data only reflecting the total amount, providing data-driven guidance for energy-saving behavior and promoting the transformation of residential electricity consumption from passive response to proactive optimization. Secondly, by identifying the carbon emission differences of air conditioning load under different scenarios, targeted demand response plans can be formulated to achieve flexible control of air conditioning load, effectively suppressing peak-valley differences in electricity consumption, reducing the impact of extreme loads on power grid equipment, and improving the economy and reliability of power grid operation. Thirdly, by deeply coupling air conditioning load data with carbon emission reduction accounting, it promotes the transformation of the power grid from a traditional power supplier to a comprehensive energy service provider, and helps the energy system upgrade to intelligence. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating an air conditioning load analysis method based on data fusion and carbon emission reduction accounting according to an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of the fusion processing flow according to an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the process for determining the carbon emissions of the first air conditioner according to an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of the process for determining the carbon emissions of the second air conditioner according to an embodiment of the present invention;

[0052] Figure 5 This is a schematic diagram of the process for generating air conditioning load analysis results according to an embodiment of the present invention;

[0053] Figure 6 This is a schematic diagram of the air conditioning load analysis results according to an embodiment of the present invention;

[0054] Figure 7 This is a schematic diagram of the structure of an air conditioning load analysis system based on data fusion and carbon emission reduction accounting according to an embodiment of the present invention;

[0055] Figure label:

[0056] 1. Data fusion and processing module; 2. Benchmark carbon emission determination module; 3. Project carbon emission determination module; 4. Analysis result generation module. Detailed Implementation

[0057] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0058] In the description of this invention, it should be understood that the terms "first" and "second," etc., are used to distinguish different objects, rather than to describe a specific order.

[0059] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by those skilled in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0060] like Figure 1 The diagram shown is a flowchart illustrating an air conditioning load analysis method based on data fusion and carbon emission reduction accounting according to an embodiment of the present invention. (Refer to...) Figure 1 This invention provides a method for analyzing air conditioning load based on data fusion and carbon emission reduction accounting, comprising the following steps:

[0061] S1. Obtain air conditioning-related data from different dimensions and perform fusion processing on all air conditioning-related data to obtain air conditioning load fusion knowledge;

[0062] like Figure 2 The diagram shown is a flowchart illustrating step S1 of an embodiment of the present invention. (Refer to...) Figure 2 Step S1 of this embodiment of the invention includes:

[0063] S101. Connect to several power service-related platforms to obtain air conditioning load data, power grid operation data, and residential activity data respectively;

[0064] Air conditioning-related data across different dimensions includes air conditioning load data (equipment operation dimension), power grid operation data (power grid management dimension), and residential activity data (user behavior dimension). Specifically, through technologies such as API interfaces and data synchronization protocols, the system accesses load interaction platforms, the new power load management system, and online State Grid and other power service platforms to collect air conditioning load data, power grid operation data, and residential activity data, forming a multi-source heterogeneous raw dataset. Air conditioning load data includes equipment serial number (SN), rated power, operating mode, operating power, date, and time period; power grid operation data covers time-of-use pricing, tiered pricing thresholds, and power grid transmission and distribution loss rates; and residential activity data includes date, time period, energy-saving notices, and reward rules.

[0065] S102. Perform correlation fusion and structured modeling on air conditioning load data, power grid operation data and residential activity data to obtain an air conditioning load data map;

[0066] Considering that correlation fusion and structured modeling are key steps in transforming multi-source heterogeneous data into air conditioning load data maps, this process will be explained in detail below.

[0067] Specifically, step S102 includes:

[0068] 1) Preprocess air conditioning load data, power grid operation data, and residential activity data to obtain a standardized dataset;

[0069] Specifically, preprocessing includes data cleaning, outlier removal, and missing value imputation.

[0070] Furthermore, for air conditioning load data, power grid operation data, and resident activity data, data cleaning is first used to remove duplicate records, incorrectly formatted data, and redundant information unrelated to the analysis period. Then, statistical methods are used to identify and remove extreme values ​​caused by equipment failure or abnormal data transmission, such as abnormal data where the air conditioning operating power exceeds the reasonable range of rated power. For missing values ​​caused by collection intervals or system failures, imputation is performed according to data characteristics using mean imputation (e.g., short-term missing data in power grid transmission and distribution loss rate), interpolation imputation (e.g., continuous time period missing data in air conditioning operating power), or association imputation based on knowledge graph ontology relationships (e.g., missing reward rules bound to specific time periods in resident activity data). Finally, a standardized dataset with uniform format and complete data is formed.

[0071] 2) Based on preset ontology relationships, perform entity recognition and attribute extraction on the standardized dataset to obtain a multi-source entity set;

[0072] Specifically, the preset ontology relationships include multi-dimensional relationship definitions such as "air conditioning associated users", "residential associated activities", "air conditioning associated electricity prices", "air conditioning associated activities", and "air conditioning associated operations".

[0073] Furthermore, based on the definitions of entities such as "air conditioner," "resident," "electricity price," "activity," and "operation" in the ontology layer, corresponding entities are identified from the standardized dataset. For example, there is an air conditioner entity with attributes such as device serial number and operating power, and a resident entity with attributes such as household number and electricity consumption. At the same time, according to the preset attribute items of each entity (such as the analysis cycle and working mode of the air conditioner, and the time-of-use electricity price and tiered electricity price threshold), the corresponding attribute values ​​are extracted from the standardized dataset to ensure that the attributes of each entity are complete and consistent with the ontology definition. Finally, a multi-source entity set containing multiple types of entities and their attributes is formed, so that data from different sources can be integrated through entity association.

[0074] 3) Align the multi-source entity sets with entities of the same origin to obtain the mapping relationship between entities;

[0075] Based on the association rules of entity attributes in the preset ontology relations, entities in the multi-source entity set are compared across datasets. Among them, the association rules include, but are not limited to, the binding relationship between air conditioner equipment serial number and resident household number, and the matching relationship between activity date and air conditioner running time.

[0076] Specifically, the system associates records of the same air conditioner across different platforms using the device's unique identifier (device SN), matches the information of the same resident in load and activity data using the household number, and aligns the electricity pricing rules with the time dimension of the corresponding air conditioner operation data using time periods and analysis cycles. For entities with slight differences in attribute values, the system performs consistency checks and corrections by combining knowledge graph reasoning rules (such as the reasonable fluctuation range of operating power), ultimately determining the mapping relationship between entities and achieving cross-dataset association and fusion of multi-source entities.

[0077] 4) Construct an air conditioning load data map containing hierarchical structure and associated attributes, using entities as nodes and mapping relationships between entities as edges.

[0078] Aligned entities from a multi-source entity set are used as nodes in the knowledge graph, and connections between nodes are constructed based on the established mapping relationships between entities. Simultaneously, attribute information extracted from a standardized dataset is loaded for each node, and the graph is hierarchically divided according to the structure defined in the ontology layer. Furthermore, this embodiment uses the knowledge graph construction tool Neo4j to structurally store the nodes, edges, and attribute information, forming an air conditioning load data graph that includes entity hierarchical relationships and cross-class association attributes.

[0079] To facilitate understanding, the structural design of the air conditioning load data graph is explained below with reference to an example:

[0080] 1) Ontology layer (entity);

[0081] Air conditioner (Attributes: Analysis period, device SN, rated power, operating mode, operating power, date, time period);

[0082] Residents (Attributes: Household Number, Current Month's Reading, Last Month's Reading, Electricity Consumption, Response Reward, Current Period's Response, Current Period's Adjustable Amount, Current Period's Power-On, Current Period's Emission Reduction, Current Period's Emissions);

[0083] Electricity price (attributes: time-of-use pricing, tiered pricing thresholds, power grid transmission and distribution loss rate);

[0084] Activity (Attributes: Date, Time Period, Notification, Reward Rules);

[0085] Operations (Attributes: Analyst, Reviewer, Power Supply Unit, Analysis Unit, Data Unit, Order Date, Analysis Cycle).

[0086] 2) Ontology layer (relationships);

[0087] "Air conditioner associated users" (many to one);

[0088] "Resident-related Activities" (many-to-many);

[0089] "Air conditioning-related electricity price" (one-to-many);

[0090] "Air Conditioning Related Activities" (One-to-Many);

[0091] "Air conditioning associated operation" (many to one).

[0092] 3) Instance layer (mapping).

[0093] Example 1: Air conditioning equipment_0000005112201299747090520589KPBJ (Analysis period = June 1, 2025 - June 30, 2025, Equipment SN = 0000005112201299747090520589KPBJ, Date = 20250612, Time period = 25, Belongs to residents = [Household number = 3750012340453]).

[0094] Example 2: Resident_3750012340453 (Household number = 3750012340453, This month's readings = 335.4 (total), 172.2 (peak), 163.2 (valley), Last month's readings = 322.4 (total), 167.2 (peak), 155.2 (valley), Electricity consumption = 13 (total), 5 (peak), 8 (valley), Amount = 5.8997 (total), 2.8845 (peak), 3.0152 (valley), Owned equipment = [Air conditioning equipment_0000005112201299747090520589KPBJ]).

[0095] Example 3: Electricity Price_202406 (Time-of-use price = 0.5769 (peak), 0.3769 (valley), Tiered price = 210 (first tier), 400 (second tier), Time period = [Analysis period = June 1, 2025 - June 30, 2025]).

[0096] S103. Perform rule-based reasoning on the air conditioning load data map to obtain air conditioning load association rules;

[0097] Based on the entities, attributes, and hierarchical relationships already established in the air conditioning load data map, pre-defined inference rules are used to logically deduce the entity attributes and relationships in the air conditioning load data map. These pre-defined inference rules include, but are not limited to:

[0098] If the air conditioner's operating power is not equal to 0, it will be marked as "on".

[0099] If the air conditioning period is ∈ [32, 87], then it is marked as "peak";

[0100] If the air conditioning time period ∈ [0,31]∪[88,95], then it is marked as "valley";

[0101] If the air conditioning period matches the activity period, and the air conditioning date matches the activity date, then mark it as "Participating in the Activity".

[0102] By analyzing the implicit relationships between entities through the rule engine (such as combining the matching relationship between air conditioner operating time and electricity price time to deduce peak and valley electricity attributes, and associating reward rules based on the marking of air conditioner participation in activities), the new attributes derived by reasoning (such as power-on status, peak and valley identifier, and activity participation status) are added to the corresponding entities, and the association logic formed by reasoning is solidified into structured air conditioner load association rules.

[0103] S104. Based on the air conditioning load data map and air conditioning load association rules, form air conditioning load fusion knowledge.

[0104] In other words, the knowledge of air conditioning load integration includes air conditioning load data maps and air conditioning load association rules.

[0105] S2. Based on the characteristics of the first electricity consumption behavior that reflects that the air conditioner does not participate in energy-saving activities, several benchmark scenarios are set up, and the first air conditioner carbon emission of each benchmark scenario is determined according to the air conditioner load fusion knowledge.

[0106] It should be noted that the above-mentioned baseline scenarios are strictly limited to the project boundary (residential smart air conditioners included in the invitation or management model within the power grid operation area) and the project inclusion period (the analysis period of this invention). For the greenhouse gas emission sources in the baseline and project scenarios, only carbon dioxide (CO2) is considered as the main emission source for accounting. Methane (CH4) and nitrous oxide (N2O) are not included due to their extremely small emission proportions. This ensures that the carbon emission calculation is carried out within a framework with clear boundaries and reasonable selection of emission sources.

[0107] like Figure 3 The diagram shown is a flowchart illustrating step S2 of an embodiment of the present invention. (Refer to...) Figure 3 Step S2 in this embodiment of the invention includes:

[0108] S201. Based on the first electricity consumption behavior characteristic that reflects that the air conditioner does not participate in energy-saving activities, a first benchmark scenario, a second benchmark scenario, and a third benchmark scenario are set;

[0109] The first baseline scenario is one where air conditioners operate according to the average electricity consumption pattern of the power grid operating area. If there is a lack of electricity consumption information collection system data covering a sufficient sample size of the power grid operating area, it is impossible to accurately fit the regional average electricity consumption pattern, and this scenario setting is not applicable.

[0110] The second baseline scenario is where the air conditioner operates according to the user's historical electricity consumption patterns for the same period. If the electricity consumption information collection system does not retain historical data for the corresponding period, it is difficult to reconstruct the historical electricity consumption patterns, and this scenario is not applicable.

[0111] The third benchmark scenario is where the air conditioner operates according to a preset standard benchmark load curve. If detailed load data from the load interaction platform (such as equipment-level operating parameters and time-of-use electricity price-related load response data) is missing, the benchmark load curve cannot be calculated according to standard rules, and this scenario setting is not applicable.

[0112] S202. Determine the first air conditioning carbon emissions for the first benchmark scenario based on air conditioning load fusion knowledge;

[0113] Specifically, step S202 includes:

[0114] 1) Extract parameters from the first baseline scenario based on the air conditioning load data map to obtain the first parameter set;

[0115] The first parameter set includes the total electricity consumption of residents in the power grid operating area, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate, the total number of households in the power grid operating area, and the advancement coefficient. Among them, the comprehensive marginal emission factor of the power grid is obtained based on the marginal emission factor of power grid electricity and the marginal emission factor of power grid capacity.

[0116] Specifically, based on the association between "air conditioning" and "residents" entities in the air conditioning load data map, the total electricity consumption of residents in the power grid operating area and the total number of households in the power grid operating area (statistical information on household numbers associated with resident entities) are extracted; the power grid transmission and distribution loss rate is obtained through the "electricity price" entity attribute, and based on the association rules between "electricity price" and "emission factor" in the air conditioning load data map, the marginal emission factor of power grid electricity and the marginal emission factor of power grid capacity are extracted, and the comprehensive marginal emission factor of the power grid is calculated by combining preset weights; at the same time, based on the group characteristic data such as the proportion of high-efficiency energy-saving air conditioners in the power grid operating area, the advancement coefficient is extracted from the inference results of the air conditioning load data map, and finally a first parameter set containing the above parameters is formed.

[0117] 2) Based on the air conditioning load association rules, the total electricity consumption of residents in the power grid operating area, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate, the total number of households in the power grid operating area and the advanced coefficient are calculated to obtain the first air conditioning carbon emission of the first benchmark scenario.

[0118] In one embodiment, the computational processing is characterized by the following formula to calculate the first air conditioning carbon emissions for a first baseline scenario:

[0119]

[0120] in, This represents the carbon emissions from the first air conditioning unit in the first baseline scenario, expressed in tons of CO2 equivalent. This represents the total electricity consumption of residents within the power grid operating area, expressed in kilowatt-hours. This represents the comprehensive marginal emission factor of the power grid, expressed in tons of carbon dioxide per megawatt-hour. This indicates the power grid transmission and distribution loss rate, expressed as a percentage. This represents the total number of households in the power grid operating area, expressed in units of households. This represents the coefficient of advancement.

[0121] In another embodiment, computational processing is performed based on an artificial intelligence model to calculate the first air conditioning carbon emissions for a first baseline scenario:

[0122] By analyzing the parameter relationships in the first baseline scenario of the air conditioning load data map, an artificial intelligence model is constructed with the first parameter set as input and the first air conditioning carbon emission of the first baseline scenario as output. During the training phase, the carbon emission calculation result obtained based on the above formula is used as a label, and the gradient boosting tree algorithm is used to optimize the model parameters, enabling the model to learn the complex mapping relationship between multiple parameters and carbon emissions. During online computation, the first parameter set extracted in real time is input into the trained model, and the model output value is used as the first air conditioning carbon emission of the first baseline scenario.

[0123] S203. Determine the first air conditioning carbon emissions of the second benchmark scenario based on air conditioning load fusion knowledge;

[0124] Specifically, step S203 includes:

[0125] 1) Extract parameters from the second baseline scenario based on the air conditioning load data map to obtain the second parameter set;

[0126] The second parameter set includes the user's historical residential electricity consumption for the corresponding period, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate, the meteorological factor correlation correction coefficient, and the work-rest characteristic correlation correction coefficient. Among them, the comprehensive marginal emission factor of the power grid is obtained based on the power grid electricity marginal emission factor and the power grid capacity marginal emission factor.

[0127] Specifically, based on the association between the "residential" entity and the "date and time period" attributes in the air conditioning load data map, the residential electricity consumption of the corresponding period in the same historical period is extracted (matching the historical electricity consumption records of the project's period). The power grid transmission and distribution loss rate is obtained through the "electricity price" entity attribute, and based on the association rules between "electricity price" and "emission factor" in the air conditioning load data map, the marginal emission factor of power grid electricity and the marginal emission factor of power grid capacity are extracted, and the comprehensive marginal emission factor of the power grid is calculated by combining the preset weights.

[0128] Furthermore, based on the implicit correlation inference results of "air conditioning operating power and daily maximum / low temperature" and "residents' activity period and weekends / holidays" in the air conditioning load data map, meteorological factor correlation correction coefficients and work and rest characteristic correlation correction coefficients are extracted respectively, and finally a second parameter set containing the above parameters is formed.

[0129] 2) Based on the air conditioning load association rules, the user's historical electricity consumption, grid comprehensive marginal emission factor, grid transmission and distribution loss rate, meteorological factor association correction coefficient and work and rest characteristic association correction coefficient are calculated and processed to obtain the first air conditioning carbon emission of the second benchmark scenario.

[0130] In one embodiment, the computational processing is characterized by the following formula to calculate the first air conditioning carbon emissions in the second baseline scenario:

[0131]

[0132] in, This represents the carbon emissions from the first air conditioning unit in the second baseline scenario, expressed in tons of CO2 equivalent. This represents the user's residential electricity consumption for the corresponding period in the historical data collection, expressed in kilowatt-hours. This represents the comprehensive marginal emission factor of the power grid, expressed in tons of carbon dioxide per megawatt-hour. This indicates the power grid transmission and distribution loss rate, expressed as a percentage. Indicates the correlation correction coefficient for meteorological factors. This represents the correction coefficient related to work and rest characteristics.

[0133] In another embodiment, computational processing is performed based on an artificial intelligence model to calculate the first air conditioning carbon emissions in the second baseline scenario:

[0134] By analyzing the parameter relationships in the second baseline scenario of the air conditioning load data map, an artificial intelligence model is constructed with the second parameter set as input and the first air conditioning carbon emission of the second baseline scenario as output. During the training phase, the carbon emission calculation result obtained based on the above formula is used as a label, and the gradient boosting tree algorithm is used to optimize the model parameters, enabling the model to learn the complex mapping relationship between multiple parameters and carbon emissions. During online computation, the second parameter set extracted in real time is input into the trained model, and the model output value is used as the first air conditioning carbon emission of the second baseline scenario.

[0135] S204. Determine the first air conditioning carbon emissions in the third benchmark scenario based on the knowledge of air conditioning load fusion.

[0136] Specifically, step S204 includes:

[0137] 1) Extract parameters from the third baseline scenario based on the air conditioning load data map to obtain the third parameter set;

[0138] The third parameter set includes the baseline residential load at each time point during the accounting period, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate, and the total number of households in the power grid operating area. Among them, the comprehensive marginal emission factor of the power grid is obtained based on the marginal emission factor of the power grid's electricity consumption and the marginal emission factor of the power grid's capacity.

[0139] Specifically, based on the "analysis period" attribute of the "operation" entity and the time dimension correlation of "air conditioning-related electricity price" in the air conditioning load data map, and in accordance with the user baseline load calculation principles in GB / T32127-2015, the hourly baseline load of residents within the accounting period is extracted (matching the hourly load records at the start and end times); the power grid transmission and distribution loss rate is obtained through the "electricity price" entity attribute, and based on the correlation rules between "electricity price" and "emission factor" in the air conditioning load data map, the marginal emission factor of power grid electricity and the marginal emission factor of power grid capacity are extracted, and the comprehensive marginal emission factor of the power grid is calculated by combining preset weights; at the same time, based on the household number statistics of the "resident" entity, the total number of households in the power grid operation area is extracted, and finally a third parameter set containing the above parameters is formed.

[0140] 2) Based on the air conditioning load association rules, the baseline load of residents, the comprehensive marginal emission factor of the power grid, the power grid transmission and distribution loss rate and the total number of households in the power grid operation area are calculated and processed to obtain the first air conditioning carbon emission of the third benchmark scenario.

[0141] In one embodiment, the computational processing is characterized by the following formula to calculate the first air conditioning carbon emissions in the third baseline scenario:

[0142]

[0143] in, This represents the carbon emissions from the first air conditioning unit in the third baseline scenario, expressed in tons of CO2 equivalent. Indicates the period included Residential baseline load at any given time, in kilowatts. This represents the comprehensive marginal emission factor of the power grid, expressed in tons of carbon dioxide per megawatt-hour. This indicates the power grid transmission and distribution loss rate, expressed as a percentage. This represents the total number of households in the power grid operating area, expressed in units of households. Indicates the starting point of the recognition period. This indicates the end point of the accounting period.

[0144] In another embodiment, computational processing is performed based on an artificial intelligence model to calculate the first air conditioning carbon emissions in a third baseline scenario:

[0145] By analyzing the parameter relationships in the third baseline scenario of the air conditioning load data map, an artificial intelligence model is constructed with the third parameter set as input and the first air conditioning carbon emission of the third baseline scenario as output. During the training phase, the carbon emission calculation result obtained based on the above formula is used as a label, and the gradient boosting tree algorithm is used to optimize the model parameters, enabling the model to learn the complex mapping relationship between multiple parameters and carbon emissions. During online computation, the real-time extracted third parameter set is input into the trained model, and the model output value is used as the first air conditioning carbon emission of the third baseline scenario.

[0146] S3. Set up project scenarios based on the characteristics of the second electricity consumption behavior that reflects the participation of air conditioners in energy-saving activities, and determine the second air conditioner carbon emissions of the project scenarios based on the knowledge of air conditioner load fusion.

[0147] like Figure 4 The diagram shown is a flowchart illustrating step S3 of an embodiment of the present invention. (Refer to...) Figure 4 Step S3 in this embodiment of the invention includes:

[0148] S301. Set up project scenarios based on the second electricity consumption behavior characteristics that reflect the participation of air conditioners in energy-saving activities;

[0149] The project scenario is the operation of an air conditioner participating in an energy-saving campaign.

[0150] S302. Extract parameters from the project scenario based on the air conditioning load data map to obtain the fourth parameter set;

[0151] The fourth parameter set includes the electricity consumption of air conditioning participating in energy-saving activities, the comprehensive marginal emission factor of the power grid, and the power grid transmission and distribution loss rate. Among them, the comprehensive marginal emission factor of the power grid is obtained based on the marginal emission factor of power grid electricity and the marginal emission factor of power grid capacity.

[0152] Specifically, based on the "air conditioning associated activity" relationship (many-to-one) between the "air conditioning" entity and the "activity" entity in the air conditioning load data map, and combined with the knowledge reasoning rules (if air conditioning.time period = activity.time period and air conditioning.date = activity.date, then it is marked as "participating in the activity"), the operating data of air conditioning equipment participating in the energy-saving activity is filtered out. The power consumption of air conditioning participating in the energy-saving activity is calculated through the "operating power" and "time period" attributes of the "air conditioning" entity (the total power consumption of this type of air conditioner during the statistical project inclusion period).

[0153] Furthermore, the power grid transmission and distribution loss rate is extracted from the "electricity price" entity attribute in the air conditioning load data map, and the marginal emission factor of power grid electricity and marginal emission factor of power grid capacity are extracted according to the association rules between "electricity price" and "emission factor". The comprehensive marginal emission factor of power grid is calculated by combining the preset weights (weight of marginal emission factor of power grid electricity and weight of marginal emission factor of power grid capacity).

[0154] Finally, the above parameters are integrated to form a fourth parameter set, which includes the power consumption of air conditioning participating in energy-saving activities, the comprehensive marginal emission factor of the power grid, and the power grid transmission and distribution loss rate.

[0155] S303. Based on the air conditioning load association rules, the power consumption of air conditioning participating in energy-saving activities, the comprehensive marginal emission factor of the power grid, and the power grid transmission and distribution loss rate are calculated to obtain the second air conditioning carbon emission of the project scenario.

[0156] In one embodiment, the following formula is used to characterize the computational processing to calculate the second air conditioning carbon emissions for the project scenario:

[0157]

[0158] in, This indicates the carbon emissions from the second air conditioning unit in the project scenario, expressed in tons of CO2 equivalent. This indicates the electricity consumption of the air conditioner participating in energy-saving activities, expressed in megawatt-hours (MWh). This represents the comprehensive marginal emission factor of the power grid, expressed in tons of carbon dioxide per megawatt-hour. This indicates the power grid transmission and distribution loss rate, expressed as a percentage.

[0159] In another embodiment, computational processing is performed based on an artificial intelligence model to calculate the second air conditioning carbon emissions for the project scenario:

[0160] By analyzing the parameter relationships in the air conditioning load data map of the project scenarios, an artificial intelligence model is constructed with the fourth parameter set as input and the second air conditioning carbon emission of the project scenario as output. During the training phase, the carbon emission calculation result obtained based on the above formula is used as a label, and the gradient boosting tree algorithm is used to optimize the model parameters, enabling the model to learn the complex mapping relationship between multiple parameters and carbon emissions. During online computation, the fourth parameter set extracted in real time is input into the trained model, and the model output value is used as the second air conditioning carbon emission of the project scenario.

[0161] S4. Based on the carbon emissions of each first and second air conditioner, obtain the carbon emission reduction of air conditioners, and integrate the carbon emission reduction of air conditioners with the air conditioner electricity consumption and billing data obtained from the analysis of air conditioner load fusion knowledge to form the air conditioner load analysis result.

[0162] like Figure 5 The diagram shown is a flowchart illustrating step S4 of an embodiment of the present invention. (Refer to...) Figure 5 Step S4 of this embodiment includes:

[0163] S401. Perform extreme value screening on each first air conditioner carbon emission to obtain the maximum air conditioner carbon emission in the benchmark scenario, and perform differential calculation on the maximum air conditioner carbon emission in the benchmark scenario and the second air conditioner carbon emission to obtain the air conditioner carbon emission reduction.

[0164] Specifically, this embodiment uses the following formula to calculate the carbon emission reduction of air conditioning:

[0165]

[0166] in, This represents the carbon emission reduction from air conditioning for a single resident, expressed in tons of CO2 equivalent. This represents the maximum carbon emission reduction from air conditioning in the baseline scenario, expressed in tons of CO2 equivalent. This indicates the carbon emissions of the second air conditioning unit in the project scenario, expressed in tons of carbon dioxide equivalent.

[0167] It should be noted that the calculation logic for individual resident carbon emission reductions can be further extended to the total emission reduction accounting of the entire project.

[0168] Specifically, when extended to the project cycle... During this period, if it is an invitation-only mode, the total number of residents who responded to the invitation task needs to be counted, and the emission reduction per household needs to be accumulated according to the following formula to calculate the project's emission reduction under this mode:

[0169]

[0170] in, Indicates the first The emission reductions for projects adopting the energy-saving activity invitation model during this period are expressed in tons of carbon dioxide equivalent. Indicates the first Per-household emission reduction, expressed in tons of carbon dioxide equivalent. This represents the total number of residents who responded to the invitation to participate in the energy-saving campaign, expressed in households.

[0171] If it is a managed care model, the total number of residents who respond to the managed care agreement is counted, and the emission reduction of the project under this model is calculated by summing the data using the following formula:

[0172]

[0173] in, Indicates the first The emission reductions for projects adopting the energy-saving activity outsourcing model are expressed in tons of CO2 equivalent. Indicates the first Per-household emission reduction, expressed in tons of carbon dioxide equivalent. This represents the total number of residents who responded to the energy-saving activity management agreement, expressed in households.

[0174] Ultimately, the total emission reduction of the project is determined by combining the emission reductions under the invitation mode and the emission reductions under the trusteeship mode. This achieves hierarchical accounting from individual household carbon emission reduction to the total emission reduction of the project, ensuring that the emission reduction accounting covers residents with different participation modes and is consistent with the diverse operational scenarios of invitation and trusteeship in actual business.

[0175] S402. Extract and correlate indicators from the knowledge of air conditioning load fusion to obtain air conditioning electricity consumption and billing data, and integrate the air conditioning carbon emission reduction and air conditioning electricity consumption and billing data to form air conditioning load analysis results.

[0176] The electricity consumption and billing data for air conditioners includes the electricity consumption and cost of air conditioners at different times and the electricity consumption and cost of air conditioners in different tiered electricity price ranges.

[0177] Specifically, based on the relationship between the "air conditioner" entity and the "time period" and "electricity price" entities in the air conditioner load data map, the operating power and duration of the air conditioner in different time periods (peak and valley periods) are extracted. Combined with the time-of-use electricity price and tiered electricity price threshold information of the "electricity price" entity, the electricity consumption is calculated by multiplying power and time, and the electricity cost is calculated by multiplying the electricity consumption and the corresponding time period electricity price. Thus, the electricity consumption and electricity cost of the air conditioner in different time periods are obtained. Similarly, based on the electricity threshold of the tiered electricity price range, the electricity consumption of the air conditioner in each tier range is counted, and the electricity cost is calculated by multiplying by the corresponding tiered electricity price. Thus, the electricity consumption and electricity cost of different tiered electricity price ranges are generated.

[0178] Furthermore, the carbon emission reductions from air conditioning calculated through correlation are integrated according to preset data integration rules, such as taking residential households as units, linking the carbon emission reductions of a single household with the household's electricity consumption and billing details, and integrating the two types of data in a structured manner to form the final air conditioning load analysis results.

[0179] like Figure 6 The diagram shown is a schematic representation of the air conditioning load analysis results according to an embodiment of the present invention. (Refer to...) Figure 6 The air conditioning load analysis results are output in the form of a residential air conditioning load analysis report, which integrates the carbon emission reduction of a single household (such as the emission reduction value in this period) with electricity consumption and billing data (including the peak and valley electricity consumption and amount of basic electricity charges, and statistics of each level of tiered electricity charges), and supplements information such as equipment serial number, analysis period, power supply / data / analysis unit, etc., and presents them in a structured report format, so that residents can clearly see the distribution of electricity consumption time periods, electricity cost composition and carbon emission reduction results, and also provide an intuitive data carrier for the power grid to carry out load management and energy conservation guidance.

[0180] This invention discloses an air conditioning load analysis method based on data fusion and carbon emission reduction accounting. It refines electricity consumption and billing data through a detailed breakdown of air conditioning load data maps, and calculates the carbon emission difference between baseline and project scenarios. For the first time, it quantitatively correlates time-of-use electricity consumption, tiered electricity pricing, and carbon emission reduction for individual air conditioning units. This breaks through the limitation of traditional electricity consumption data only reflecting total amounts, providing data-driven guidance for energy-saving behaviors and promoting a shift from passive response to proactive optimization in residential electricity use. By identifying carbon emission differences in air conditioning load under different scenarios, targeted demand response plans can be developed, enabling flexible control of air conditioning load, effectively suppressing peak-valley differences in electricity consumption, reducing the impact of extreme loads on power grid equipment, and improving the economy and reliability of power grid operation. Furthermore, by deeply coupling air conditioning load data with carbon emission reduction accounting, it promotes the transformation of the power grid from a traditional power supplier to a comprehensive energy service provider, facilitating the intelligent upgrading of the energy system.

[0181] like Figure 7 The diagram shown is a structural schematic of an air conditioning load analysis system based on data fusion and carbon emission reduction accounting according to an embodiment of the present invention. (Refer to...) Figure 7 An embodiment of the present invention provides an air conditioning load analysis system based on data fusion and carbon emission reduction accounting, comprising:

[0182] Data fusion processing module 1 is used to acquire air conditioning-related data from different dimensions and to fuse all air conditioning-related data to obtain air conditioning load fusion knowledge.

[0183] The benchmark carbon emission determination module 2 is used to set several benchmark scenarios based on the first electricity consumption behavior characteristics that reflect that the air conditioner does not participate in energy-saving activities, and to determine the first air conditioner carbon emission of each benchmark scenario according to the air conditioner load fusion knowledge.

[0184] Project carbon emission determination module 3 is used to set up project scenarios based on the characteristics of the second electricity consumption behavior that reflects the participation of air conditioning in energy-saving activities, and to determine the second air conditioning carbon emission of the project scenario based on the air conditioning load fusion knowledge;

[0185] The analysis result generation module 4 is used to obtain the air conditioning carbon emission reduction based on the first and second air conditioning carbon emissions, and to integrate the air conditioning carbon emission reduction with the air conditioning electricity consumption and billing data obtained from the analysis of air conditioning load fusion knowledge to form the air conditioning load analysis result.

[0186] It should be noted that each module in the aforementioned air conditioning load analysis system based on data fusion and carbon emission reduction accounting can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module. For specific limitations regarding the air conditioning load analysis system based on data fusion and carbon emission reduction accounting, please refer to the limitations of the air conditioning load analysis method based on data fusion and carbon emission reduction accounting mentioned above; both have the same function and role, and will not be repeated here.

[0187] In summary, this invention provides an air conditioning load analysis method and system based on data fusion and carbon emission reduction accounting. By refining the breakdown of electricity consumption and billing data according to air conditioning load data maps and calculating the carbon emission difference between baseline and project scenarios, it for the first time quantitatively correlates the time-of-use electricity consumption, tiered electricity pricing, and carbon emission reduction of individual air conditioning units. This breaks through the limitation of traditional electricity consumption data only reflecting the total amount, providing data-driven guidance for energy-saving behaviors and promoting a shift from passive response to proactive optimization in residential electricity use. By identifying the carbon emission differences of air conditioning load under different scenarios, targeted demand response plans can be formulated, achieving flexible control of air conditioning load, effectively suppressing peak-valley differences in electricity consumption, reducing the impact of extreme loads on power grid equipment, and improving the economy and reliability of power grid operation. Furthermore, by deeply coupling air conditioning load data with carbon emission reduction accounting, it promotes the transformation of the power grid from a traditional power supplier to a comprehensive energy service provider, facilitating the intelligent upgrading of the energy system.

[0188] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0189] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. An air conditioner load analysis method based on data fusion and carbon emission reduction accounting, characterized in that, The method comprises the following steps: acquiring air conditioner related data in different dimensions, and performing fusion processing on all the air conditioner related data to obtain air conditioner load fusion knowledge; setting a plurality of benchmark scenarios based on first electricity consumption behavior characteristics reflecting that the air conditioner does not participate in power saving activities, and determining first air conditioner carbon emissions of each benchmark scenario according to the air conditioner load fusion knowledge; setting a project scenario based on second electricity consumption behavior characteristics reflecting that the air conditioner participates in power saving activities, and determining second air conditioner carbon emissions of the project scenario according to the air conditioner load fusion knowledge; obtaining air conditioner carbon emission reduction based on each first air conditioner carbon emission and second air conditioner carbon emission, and integrating the air conditioner carbon emission reduction and air conditioner electricity consumption and billing data obtained by analyzing the air conditioner load fusion knowledge to form air conditioner load analysis results; The method for obtaining air conditioner carbon emission reduction based on each first air conditioner carbon emission and second air conditioner carbon emission, and integrating the air conditioner carbon emission reduction and air conditioner electricity consumption and billing data obtained by analyzing the air conditioner load fusion knowledge to form air conditioner load analysis results comprises: performing extreme value screening on each first air conditioner carbon emission to obtain a benchmark scenario maximum air conditioner carbon emission, and performing difference operation on the benchmark scenario maximum air conditioner carbon emission and the second air conditioner carbon emission to obtain air conditioner carbon emission reduction; performing index extraction and correlation calculation on the air conditioner load fusion knowledge to obtain air conditioner electricity consumption and billing data, and integrating the air conditioner carbon emission reduction and the air conditioner electricity consumption and billing data to form air conditioner load analysis results, wherein the air conditioner electricity consumption and billing data includes electricity consumption and electricity charges of the air conditioner in different time periods and electricity consumption and electricity charges of the air conditioner in different step electricity price intervals.

2. The air conditioning load analysis method based on data fusion and carbon emission reduction accounting according to claim 1, characterized in that, The method for obtaining air conditioner related data in different dimensions, and performing fusion processing on all the air conditioner related data to obtain air conditioner load fusion knowledge comprises: accessing a plurality of power service association platforms to respectively acquire air conditioner load data, power grid operation data and resident activity data; performing associated fusion and structured modeling on the air conditioner load data, the power grid operation data and the resident activity data to obtain an air conditioner load data graph; performing rule reasoning on the air conditioner load data graph to obtain air conditioner load association rules; forming air conditioner load fusion knowledge based on the air conditioner load data graph and the air conditioner load association rules.

3. The air conditioning load analysis method based on data fusion and carbon emission reduction accounting of claim 2, wherein, The method for performing associated fusion and structured modeling on the air conditioner load data, the power grid operation data and the resident activity data to obtain an air conditioner load data graph comprises: performing preprocessing on the air conditioner load data, the power grid operation data and the resident activity data to obtain a standardized data set; performing entity recognition and attribute extraction on the standardized data set based on a preset ontology relationship to obtain a multi-source entity set; performing homologous entity alignment on the multi-source entity set to obtain an inter-entity mapping relationship; constructing an air conditioner load data graph containing hierarchical structure and associated attributes by taking entities as nodes and the inter-entity mapping relationship as edges.

4. The air conditioning load analysis method based on data fusion and carbon emission reduction accounting according to claim 2, characterized in that, The first electricity consumption behavior characteristic reflecting that the air conditioner does not participate in power saving activities is used to set a plurality of benchmark scenes, and a first air conditioner carbon emission of each benchmark scene is determined according to the air conditioner load fusion knowledge, comprising: The first electricity consumption behavior characteristic reflecting that the air conditioner does not participate in power saving activities is used to set a first benchmark scene, a second benchmark scene and a third benchmark scene; The first air conditioner carbon emission of the first benchmark scene is determined based on the air conditioner load fusion knowledge, wherein the first benchmark scene is a scene in which the air conditioner operates in the average power consumption mode of the power grid operation area; The first air conditioner carbon emission of the second benchmark scene is determined based on the air conditioner load fusion knowledge, wherein the second benchmark scene is a scene in which the air conditioner operates in the historical same period power consumption mode of the user; The first air conditioner carbon emission of the third benchmark scene is determined based on the air conditioner load fusion knowledge, wherein the third benchmark scene is a scene in which the air conditioner operates in a preset standard benchmark load curve.

5. The air conditioning load analysis method based on data fusion and carbon emission reduction accounting according to claim 4, characterized in that, The first air conditioner carbon emission of the first benchmark scene is determined based on the air conditioner load fusion knowledge, comprising: Parameters of the first benchmark scene are extracted based on the air conditioner load data graph to obtain a first parameter set, wherein the first parameter set includes total residential power consumption of the power grid operation area, a power grid comprehensive marginal emission factor, a power grid transmission and distribution loss rate, total number of residential households in the power grid operation area and an advanced coefficient, and the power grid comprehensive marginal emission factor is obtained based on a power grid power quantity marginal emission factor and a power grid capacity marginal emission factor; The first air conditioner carbon emission of the first benchmark scene is obtained by performing operation processing on the total residential power consumption of the power grid operation area, the power grid comprehensive marginal emission factor, the power grid transmission and distribution loss rate, the total number of residential households in the power grid operation area and the advanced coefficient based on the air conditioner load association rules.

6. The air conditioning load analysis method based on data fusion and carbon emission reduction accounting according to claim 4, characterized in that, The first air conditioner carbon emission of the second benchmark scene is determined based on the air conditioner load fusion knowledge, comprising: Parameters of the second benchmark scene are extracted based on the air conditioner load data graph to obtain a second parameter set, wherein the second parameter set includes user historical same period corresponding accounting period residential power consumption, a power grid comprehensive marginal emission factor, a power grid transmission and distribution loss rate, a meteorological factor associated correction coefficient and a work and rest feature associated correction coefficient, and the power grid comprehensive marginal emission factor is obtained based on a power grid power quantity marginal emission factor and a power grid capacity marginal emission factor; The first air conditioner carbon emission of the second benchmark scene is obtained by performing operation processing on the user historical same period corresponding accounting period residential power consumption, the power grid comprehensive marginal emission factor, the power grid transmission and distribution loss rate, the meteorological factor associated correction coefficient and the work and rest feature associated correction coefficient based on the air conditioner load association rules.

7. The air conditioning load analysis method based on data fusion and carbon emission reduction accounting according to claim 4, characterized in that, The first air conditioner carbon emission of the third benchmark scene is determined based on the air conditioner load fusion knowledge, comprising: extract parameters of the third reference scene based on the air conditioner load data graph, to obtain a third parameter set, wherein the third parameter set comprises a resident baseline load at each time point in the accounting period, a power grid comprehensive marginal emission factor, a power grid transmission and distribution loss rate, and a total number of resident households in a power grid operation area, and the power grid comprehensive marginal emission factor is obtained based on a power grid electricity quantity marginal emission factor and a power grid capacity marginal emission factor; perform operation processing on the resident baseline load at each time point in the accounting period, the power grid comprehensive marginal emission factor, the power grid transmission and distribution loss rate, and the total number of resident households in the power grid operation area based on the air conditioner load association rule, to obtain a first air conditioner carbon emission of the third reference scene.

8. The air conditioning load analysis method based on data fusion and carbon emission reduction accounting according to claim 2, characterized in that, The project scene is set based on the second electricity consumption behavior characteristic reflecting the participation of the air conditioner in the power saving activity, and the second air conditioner carbon emission of the project scene is determined according to the air conditioner load fusion knowledge, which comprises: The project scene is set based on the second electricity consumption behavior characteristic reflecting the participation of the air conditioner in the power saving activity, wherein the project scene is an operation scene of the air conditioner participating in the power saving activity; extract parameters of the project scene based on the air conditioner load data graph, to obtain a fourth parameter set, wherein the fourth parameter set comprises an air conditioner power consumption participating in the power saving activity, a power grid comprehensive marginal emission factor, and a power grid transmission and distribution loss rate, and the power grid comprehensive marginal emission factor is obtained based on a power grid electricity quantity marginal emission factor and a power grid capacity marginal emission factor; perform operation processing on the air conditioner power consumption participating in the power saving activity, the power grid comprehensive marginal emission factor, and the power grid transmission and distribution loss rate based on the air conditioner load association rule, to obtain the second air conditioner carbon emission of the project scene.

9. An air conditioner load analysis system based on data fusion and carbon emission reduction accounting, characterized by, It comprises: a data fusion processing module configured to acquire air conditioner related data in different dimensions and perform fusion processing on all the air conditioner related data to obtain air conditioner load fusion knowledge; a reference carbon emission determination module configured to set a plurality of reference scenes based on a first electricity consumption behavior characteristic reflecting non-participation of an air conditioner in a power saving activity, and determine a first air conditioner carbon emission of each reference scene according to the air conditioner load fusion knowledge; a project carbon emission determination module configured to set a project scene based on a second electricity consumption behavior characteristic reflecting participation of an air conditioner in a power saving activity, and determine a second air conditioner carbon emission of the project scene according to the air conditioner load fusion knowledge; an analysis result generation module configured to obtain an air conditioner carbon emission reduction based on each first air conditioner carbon emission and the second air conditioner carbon emission, and integrate the air conditioner carbon emission reduction and air conditioner electricity consumption and billing data obtained by analyzing the air conditioner load fusion knowledge to form an air conditioner load analysis result; the air conditioner carbon emission reduction is obtained based on each first air conditioner carbon emission and the second air conditioner carbon emission, and the air conditioner carbon emission reduction and air conditioner electricity consumption and billing data obtained by analyzing the air conditioner load fusion knowledge are integrated to form an air conditioner load analysis result, which comprises: extreme value screening is performed on each first air conditioner carbon emission to obtain a reference scene maximum air conditioner carbon emission, and difference operation is performed on the reference scene maximum air conditioner carbon emission and the second air conditioner carbon emission to obtain an air conditioner carbon emission reduction; The air conditioner load fusion knowledge is subjected to index extraction and correlation calculation to obtain air conditioner power consumption and billing data, and the air conditioner carbon emission reduction amount and the air conditioner power consumption and billing data are integrated to form air conditioner load analysis results, wherein the air conditioner power consumption and billing data include power consumption and electricity charges of the air conditioner in different time periods and power consumption and electricity charges of the air conditioner in different step electricity price intervals.

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