A green and low-carbon building energy consumption assessment and carbon footprint refinement estimation method

Abstract

The green and low-carbon building energy consumption assessment and carbon footprint refined estimation method provided by the present invention belongs to the field of carbon emission reduction technology, and includes: obtaining the time-sharing energy consumption of the building and performing non-invasive load identification to obtain a load identification waveform, and calculating the corresponding load energy consumption data based on the load identification waveform and the load energy consumption duration; obtaining the first electric energy data of the photovoltaic storage system corresponding to the building, and the second electric energy data corresponding to the municipal power station, calculating and processing the carbon footprint of the photovoltaic storage system to obtain the first carbon footprint information, and decomposing the first carbon footprint information to obtain the second carbon footprint information per kilowatt-hour; performing an estimation process based on the second carbon footprint information, the first electric energy data, and the second electric energy data according to a preset estimation model to obtain an indirect carbon emission factor of the building; calculating the direct carbon emission factor of the equipment based on the load energy consumption data of the load, and performing a secondary estimation based on the indirect carbon emission factor and the direct carbon emission factor of the building to obtain the actual carbon emissions.

Description

Technical Field

[0001] The present invention relates to carbon emission reduction technology, and in particular to a green and low-carbon building energy consumption assessment and carbon footprint refinement estimation method. Background Art

[0002] Various electrical equipment are widely used in buildings, such as air conditioners, lighting, computers, home appliances, etc. These devices consume a lot of energy in daily use and directly or indirectly cause carbon emissions.

[0003] At present, there are many types of electrical equipment in buildings with different working hours, and the overall energy consumption structure of the building is complex. There is no good method in the existing technology to accurately evaluate the energy consumption and estimate the carbon footprint of buildings. Summary of the invention

[0004] Based on the above problems, the present invention is proposed to provide a method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings, which overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings is provided, comprising:

[0006] Obtain the time-sharing energy consumption of the building and perform non-intrusive load identification to obtain the load identification waveform. Based on the load identification waveform and the load energy consumption duration, calculate the corresponding load energy consumption data;

[0007] Obtaining first electric energy data of the photovoltaic storage system corresponding to the building and second electric energy data corresponding to the city power station, calculating and processing the carbon footprint of the photovoltaic storage system to obtain first carbon footprint information, and decomposing the first carbon footprint information to obtain second carbon footprint information per kilowatt-hour of electricity;

[0008] Based on the second carbon footprint information, the first electric energy data, and the second electric energy data, an estimation process is performed according to a preset estimation model to obtain an indirect carbon emission factor of the building;

[0009] The direct carbon emission factor of the equipment is calculated based on the load energy consumption data of the load, and the actual carbon emissions are obtained by performing a secondary estimation based on the indirect carbon emission factor and the direct carbon emission factor of the building.

[0010] Optionally, the time-sharing energy consumption of the building is acquired and non-intrusive load identification is performed to obtain a load identification waveform, and corresponding load energy consumption data is calculated based on the load identification waveform and the load energy consumption duration, including:

[0011] Acquire and identify time-sharing energy consumption based on a pre-configured non-intrusive load identification device, obtain a corresponding load identification waveform, and determine a corresponding start-up load based on the load identification waveform;

[0012] Obtain the load identification waveforms at different identification times within a preset time period and perform time statistics to obtain the load energy usage duration of each load;

[0013] The load identification waveforms at different identification times within the first preset time period are obtained and averaged to obtain the average load. The load sub-information of each load is obtained based on the average load and the load energy consumption duration. All the load sub-information is counted to obtain the load energy consumption data.

[0014] Optionally, the acquiring and identifying time-sharing energy consumption based on the pre-configured non-intrusive load identification device to obtain a corresponding load identification waveform, and determining a corresponding start-up load based on the load identification waveform includes:

[0015] Obtain all power supply endpoints in the power supply line distribution map of the building, and determine the identification node based on the building space corresponding to the power supply endpoint in the building;

[0016] A corresponding non-intrusive load identification device is configured at the identification node, and a corresponding space load is determined based on the attribute of the building space corresponding to each identification node;

[0017] A load identification waveform configured by a user for each spatial load is received, so that the corresponding non-intrusive load identification device performs spatial load identification processing based on the load identification waveform.

[0018] Optionally, the acquiring all power supply endpoints in the power supply line distribution diagram of the building, and determining the identification node based on the building space corresponding to the power supply endpoint in the building, includes:

[0019] Processing the building according to spatial division to obtain a plurality of building spaces, acquiring all power supply endpoints corresponding to each building space to obtain a first power supply endpoint set;

[0020] Traversing all power supply endpoints in the first power supply endpoint set in sequence and extending upward in a convergent manner according to the power supply line distribution map to determine a first convergence point in the power supply line distribution map within the first power supply endpoint set;

[0021] Extending downward based on the first convergence point again, if the first convergence point only completely includes all power supply endpoints in the first power supply endpoint set in the downward dimension and the number of power supply endpoints is greater than or equal to the number of established nodes, then the first convergence point is determined as an identification node;

[0022] A first structure tree is constructed based on the first aggregation point and the first power supply endpoint set, wherein the parent node of the first structure tree is the first aggregation point, the child node is the first power supply endpoint set, and the grandchild node is each power supply endpoint in the first power supply endpoint set.

[0023] Optionally, also include:

[0024] If the first aggregation point further includes all power supply endpoints in other first power supply endpoint sets in the downward dimension and the number of all power supply endpoints is greater than or equal to the number of established nodes, the corresponding first power supply endpoint sets are merged and the first aggregation point is used as the second aggregation point;

[0025] A corresponding second structure tree is constructed, wherein the parent node of the second structure tree is the second convergence point, the child node is each first power supply endpoint set, and the grandchild node is each power supply endpoint in each first power supply endpoint set.

[0026] Optionally, also include:

[0027] If all power supply endpoints are less than the number of established nodes, then determine the adjacent building spaces of the building space, and each building space has a preset adjacent building space;

[0028] The power supply endpoints corresponding to the current building space and the adjacent building space are extended upward in a convergent manner to determine a third convergence point in the power supply line distribution map within the first power supply endpoint set corresponding to the current building space and the adjacent building space;

[0029] A corresponding third structure tree is constructed, wherein the parent node of the third structure tree is the third convergence point, the child node is each first power supply endpoint set, and the grandchild node is each power supply endpoint in each first power supply endpoint set.

[0030] Optionally, the load identification waveforms at different identification times within a preset time period are obtained and averaged to obtain an average load, and the load sub-information of each load is obtained based on the average load and the load energy usage duration, including:

[0031] Obtaining load identification waveforms at different identification moments within a first preset time period, wherein the load identification waveforms include load identification waveforms, and extracting a load value at each time point in each load identification waveform;

[0032] Performing mean calculation based on the load value at each time point to obtain a first average load of the corresponding load identification waveform;

[0033] The first average load of all load identification waveforms within a preset time period is statistically calculated and then the average is calculated to obtain a second average load;

[0034] The load sub-information of each load is obtained by multiplying the second average load and the load energy usage duration of all load identification waveforms.

[0035] Optionally, the obtaining of first electric energy data of the photovoltaic storage system corresponding to the building and second electric energy data corresponding to the city power station, calculating and processing the carbon footprint of the photovoltaic storage system to obtain first carbon footprint information, and decomposing the first carbon footprint information to obtain second carbon footprint information per kilowatt-hour of electricity includes:

[0036] Acquire power supply information of the photovoltaic storage system of the building in a first preset time period to obtain first power data, and acquire power supply information of the mains in the first preset time period to obtain second power data;

[0037] The first carbon footprint information of the photovoltaic storage system is obtained by calculation and processing based on the production information and transportation information of the photovoltaic storage system equipment in the database, and the second carbon footprint information of the photovoltaic storage system is obtained based on the first carbon footprint information and the rated power generation of the photovoltaic storage system.

[0038] Optionally, performing an estimation process based on the second carbon footprint information, the first electric energy data, and the second electric energy data according to a preset estimation model to obtain an indirect carbon emission factor of the building includes:

[0039] The estimation model obtains a first electricity consumption value of the first electric energy data, and calculates the first carbon emission sub-information based on the first electricity consumption value and the second carbon footprint information;

[0040] The estimation model obtains the second electricity consumption value of the second electric energy data and the mains carbon emission factor to calculate the second carbon emission sub-information, and obtains the sum of the estimated carbon emissions based on the first carbon emission sub-information and the second carbon emission sub-information;

[0041] The sum of the first electricity consumption value and the second electricity consumption value is calculated to obtain a total electricity consumption value, and the indirect carbon emission factor of the building is obtained by comparing the total electricity consumption value with the sum of the estimated carbon emissions.

[0042] Optionally, the direct carbon emission factor of the equipment is calculated based on the load energy consumption data, and the actual carbon emissions are obtained by secondary estimation based on the indirect carbon emission factor and the direct carbon emission factor of the building, including:

[0043] Based on the sum of estimated carbon emissions and load energy consumption data, the direct carbon emission factors of the equipment corresponding to all loads are calculated;

[0044] The factor difference between the building's indirect carbon emission factor and the direct carbon emission factor is calculated, and the actual carbon emissions of the building are obtained based on the factor difference.

[0045] Optionally, the calculating of the indirect carbon emission factor and the direct carbon emission factor of the building to obtain the factor difference, and obtaining the actual carbon emissions of the building based on the factor difference, includes:

[0046] If the absolute value of the factor difference is less than the preset value of the factor, the actual carbon emissions of the building are calculated by multiplying the electricity consumption value of the load energy consumption data and the building indirect carbon emission factor;

[0047] If the absolute value of the factor difference is greater than or equal to the factor preset value, a power supply network topology map corresponding to the building is generated, wherein the power supply network topology map includes a city power supply, a photovoltaic storage system power supply, a wire network, and a load;

[0048] The first actual carbon emission of the load in the building is calculated based on the electricity consumption value of the load energy consumption data and the building indirect carbon emission factor;

[0049] Based on the first electricity consumption value, the second electricity consumption value and the building indirect carbon emission factor, a second actual carbon emission of the electrical wire network in the building is obtained.

[0050] Optionally, also include:

[0051] Obtain the distance of the power supply network between the photovoltaic storage system and each load to obtain the corresponding first extra power loss distance, and obtain the distance of the power supply network between the mains and each load to obtain the corresponding second extra power loss distance;

[0052] Traversing the first extra energy loss distance and the second extra energy loss distance of each load in turn, and determining the minimum loss distance of each load, and counting the loads whose minimum loss distance is the first extra energy loss distance to obtain a first load set;

[0053] Based on the analysis and processing of the historical load energy consumption data of each load in the first load set, adjustment data of the photovoltaic energy storage system is obtained.

[0054] Optionally, the analyzing and processing of the load energy consumption data of each load in the first load set to obtain the adjustment data of the photovoltaic energy storage system includes:

[0055] Obtaining the historical load energy consumption data of each load in the first load set, and classifying and processing them in time sequence according to a preset length of time to obtain a corresponding load energy consumption sequence;

[0056] Counting the sum of load values ​​of all loads of the same time sequence in the load energy consumption sequence, and comparing the sum of load values ​​of all time sequences to determine the maximum sum of load values;

[0057] The sum of the maximum load values ​​is compared with the current rated load value of the photovoltaic storage system to obtain the adjusted load value of the photovoltaic storage system.

[0058] Optionally, comparing the sum of the maximum load values ​​with the current rated load value of the photovoltaic storage system to obtain an adjusted load value of the photovoltaic storage system includes:

[0059] If the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic storage system is less than or equal to the increase threshold, the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic storage system is calculated to obtain the adjusted load value;

[0060] If the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic energy storage system is greater than the increase threshold, the increase threshold is used as the load adjustment value.

[0061] Optionally, a root node is established, and the parent nodes of the first structure tree, the second structure tree, and the third structure tree are respectively combined and connected with the root node to obtain a total structure tree;

[0062] Based on the circuit lengths of the aggregation node and the PV storage system and / or the mains in the overall structure tree, a first connection length ratio of each aggregation node is obtained;

[0063] Obtaining the line length from each load in each first load set to the aggregation node, and obtaining the second connection length ratio of each load;

[0064] Obtain a first connection line length between a root node and a child node based on the first connection length ratio and the first reference length, obtain a second connection line length based on the second connection length ratio and the second reference length, and obtain a third connection line length by equally dividing the second connection line length;

[0065] Connecting the root node to the corresponding parent node via a first connection line length, connecting the parent node to the corresponding child node based on a third connection line length, and connecting the child node to the corresponding parent node based on the third connection line length;

[0066] Fill the carbon emission information of each non-intrusive load identification device into the corresponding parent node, fill the carbon emission information of each set into the child node, fill the carbon emission information of each device into the grandchild node, and divide the second actual carbon emission of the power line network equally according to the length ratio to obtain the carbon emission information of each connecting line.

[0067] Different from the prior art, the present invention proposes a green low-carbon building energy consumption assessment and carbon footprint refined estimation method, including: obtaining the time-sharing energy consumption of the building and performing non-invasive load identification to obtain the load identification waveform, and calculating the corresponding load energy consumption data based on the load identification waveform and the load energy consumption duration; obtaining the first electric energy data of the photovoltaic storage system corresponding to the building, and the second electric energy data corresponding to the municipal power station, calculating and processing the carbon footprint of the photovoltaic storage system to obtain the first carbon footprint information, and decomposing the first carbon footprint information to obtain the second carbon footprint information per kilowatt-hour; performing an estimation process based on the second carbon footprint information, the first electric energy data, and the second electric energy data according to a preset estimation model to obtain the indirect carbon emission factor of the building; calculating the direct carbon emission factor of the equipment based on the load energy consumption data of the load, and performing a secondary estimation based on the indirect carbon emission factor and the direct carbon emission factor of the building to obtain the actual carbon emissions. In the above manner, the present invention can perform accurate energy consumption assessment and carbon footprint estimation for buildings.

[0068] The present invention uses non-invasive load identification technology to monitor the power consumption of each electrical device in a building in real time, and obtain the load identification waveform and operating status of each device in different time periods. The non-invasive load identification device captures parameters such as current and voltage, generates a corresponding load identification waveform, and identifies the specific electrical equipment and its energy consumption data by comparing it with a known device waveform database. Through this method, the operating time and energy consumption data of each device can be accurately recorded, providing high-precision data support for energy consumption analysis and carbon emission calculations. In a specific embodiment, by configuring a non-invasive load identification device for the identification nodes of each power supply endpoint in the building, the load identification results in each time period are obtained, and the load energy consumption data of each device is calculated to ensure the accuracy and comprehensiveness of the energy consumption data, thereby effectively improving the accuracy and efficiency of energy consumption monitoring.

[0069] The present invention combines the data of the photovoltaic storage system and the mains system, calculates the carbon footprint of the first electric energy data of the photovoltaic storage system and the second electric energy data of the mains, obtains the carbon emission information of the photovoltaic storage system and the mains system respectively, and further calculates the carbon emissions per kilowatt-hour (second carbon footprint information). This comprehensive analysis method not only takes into account the carbon emissions of the photovoltaic storage system and the mains system in the process of power generation and transmission, but also refines the carbon footprint information per kilowatt-hour, making the carbon emission calculation more precise and accurate. In a specific embodiment, by analyzing the power supply data of the photovoltaic storage system and the mains system, combined with their production and transportation information, the carbon emissions of the photovoltaic storage system and the mains system in the process of power generation and transmission are calculated, and these carbon emissions are decomposed into each kilowatt-hour, thereby achieving a detailed assessment of the carbon footprint of different energy sources and providing a scientific basis for the carbon emission management of buildings.

[0070] The present invention analyzes the load energy consumption data of all electrical equipment in the building, calculates the direct carbon emission factor of each device, and performs a secondary estimate based on the overall building indirect carbon emission factor to obtain the actual carbon emissions. This method can not only comprehensively evaluate the overall carbon emissions of the building, but also identify high-energy consumption equipment and high-carbon emission areas, thereby providing targeted energy consumption optimization suggestions. In a specific embodiment, by performing a time series analysis on the historical energy consumption data of each device, the maximum load period is identified, and these data are compared with the rated load of the photovoltaic storage system to generate adjustment data, optimize the operation strategy of the photovoltaic storage system, improve energy utilization efficiency, and achieve the goal of reducing carbon emissions. In addition, a power supply network topology map is established to analyze the carbon emissions during power transmission. Through a comprehensive analysis of the power supply network and equipment, energy consumption management is further optimized to achieve the goal of low-carbon energy conservation. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flow chart of a method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings provided in an embodiment of the present application;

[0072] Figure 2 It is a schematic diagram of turning on a load in a load identification waveform provided in an embodiment of the present application;

[0073] Figure 3 is a schematic diagram of calculating average load provided in an embodiment of the present application;

[0074] Figure 4 It is a structural schematic diagram of a green and low-carbon building energy consumption assessment and carbon footprint refined estimation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0076] It should be noted that, if there is no conflict, the various features in the embodiments of the present invention can be combined with each other, and all are within the protection scope of the present invention. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order from the module division in the device schematic diagram or the order in the flow chart.

[0077] The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0078] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0079] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0080] See also Figure 1 , Figure 1 This is a flow chart of a method for evaluating energy consumption and refining carbon footprint of a green and low-carbon building provided in an embodiment of the present application, including steps S1 to S4, which are as follows:

[0081] S1. Obtain the time-sharing energy consumption of the building and perform non-intrusive load identification to obtain a load identification waveform. Based on the load identification waveform and the load energy consumption duration, the corresponding load energy consumption data is calculated.

[0082] Among them, the building represents a physical building that needs to be evaluated for energy consumption and estimated for carbon footprint, which can be specifically a residential, office building, shopping mall or other type of building; the time-sharing energy consumption represents the electricity consumption or energy consumption of the building in different time periods; the non-invasive load identification represents obtaining the power consumption data of each electrical equipment in the building, including but not limited to the time point and the corresponding electricity consumption; the load identification waveform represents the equipment type, working status and corresponding energy consumption of each electrical equipment; the load energy usage duration represents the total time that a single electrical equipment is used in a specific time period; the load energy consumption data represents the total energy consumption of all electrical equipment in a specific time period.

[0083] It should be noted that non-intrusive load identification (NILM) technology can be used to perform non-intrusive load identification on buildings, and the load energy consumption data obtained can provide basic data for the subsequent calculation of the direct carbon emission factor of the equipment.

[0084] In some embodiments, step S1 (obtaining the time-sharing energy consumption of the building and performing non-intrusive load identification to obtain a load identification waveform, and calculating the corresponding load energy consumption data based on the load identification waveform and the load energy consumption duration) specifically includes steps S11 to S13:

[0085] S11. Acquire and identify time-sharing energy consumption based on a pre-configured non-intrusive load identification device to obtain a corresponding load identification waveform, and determine a corresponding start-up load based on the load identification waveform.

[0086] Among them, pre-configuration means installing and setting up the non-intrusive load identification device, including the physical installation of the equipment, parameter setting, etc.; the non-intrusive load identification device is a device that identifies each electrical load by monitoring the overall energy consumption data of the building; the load identification waveform means a waveform of the load value of the electrical equipment generated by the non-intrusive load identification device that changes with time, and the waveform reflects the energy consumption characteristics of the electrical equipment when it is turned on or off; the turned-on load means the load value corresponding to the electrical equipment that is identified as being in the turned-on state at a specific moment.

[0087] Specifically, a non-intrusive load identification device is installed on the electric meter of the building. The non-intrusive load identification device can capture the load signal passing through the electric meter, and record the waveform data of each time point and the corresponding load value in real time according to the load signal. The waveform data is the load identification waveform. Each electrical device (TV, computer, range hood, etc.) will generate a unique load identification waveform when running. Therefore, the collected load identification waveform is compared with the known device waveform database to identify the specific electrical device in use. If the captured load identification waveform matches the standard waveform of the TV, it can be determined that the TV is running. Non-intrusive load identification technology is well known to those skilled in the art and will not be described in detail here.

[0088] See also Figure 2 , Figure 2 This is a schematic diagram of a load identification waveform for turning on a load provided in an embodiment of the present application. Most electrical devices use alternating current, so the load identification waveform is an alternating current waveform when the electrical device is turned on, but each electrical device corresponds to different power when working, so the load value will be different.

[0089] It should be noted that in order to accurately obtain and process energy consumption data, the non-intrusive load identification equipment must be correctly configured and calibrated.

[0090] The non-intrusive load identification device based on the pre-configuration acquires and identifies the time-sharing energy consumption, obtains the corresponding load identification waveform, and determines the corresponding start-up load based on the load identification waveform, including:

[0091] S111, obtaining all power supply endpoints in the power supply line distribution diagram of the building, and determining identification nodes based on the building spaces corresponding to the power supply endpoints in the building.

[0092] It is understandable that this solution will determine the power supply endpoints in the power supply line distribution map and use them as identification nodes. The power supply endpoints can be power access points in a building, such as power distribution boxes, etc., and the power supply endpoints may correspond to a room or an office area. The identification nodes are these specific rooms or areas.

[0093] In some embodiments, the step of obtaining all power supply endpoints in a power supply line distribution diagram of a building and determining identification nodes based on building spaces corresponding to the power supply endpoints in the building includes:

[0094] The building is processed according to the spatial division to obtain multiple building spaces, and all power supply endpoints corresponding to each building space are obtained to obtain a first power supply endpoint set. This solution divides the building into spaces, usually based on a building floor plan or using space management software. The divided building space can be a room, an office area, a floor, etc. All power supply endpoints in each building space are obtained to form a first power supply endpoint set. For example, a room may have multiple sockets, etc., which are the power supply endpoints of the room.

[0095] Traverse all power supply endpoints in the first power supply endpoint set in sequence and extend upward in a convergent manner according to the power supply line distribution map to determine the first convergence point in the power supply line distribution map within the first power supply endpoint set. This solution will traverse all power supply endpoints in the first power supply endpoint set, extend upward according to the power supply line distribution map, and find the parent power supply nodes of these endpoints. Determine the first convergence point of these power supply endpoints in the power supply line distribution map. For example, multiple sockets may converge to the same distribution box, and this distribution box is the first convergence point.

[0096] Based on the first convergence point, the downward dimension is extended again. If the first convergence point only completely includes all the power supply endpoints in the first power supply endpoint set and the number of power supply endpoints is greater than or equal to the number of established nodes, the first convergence point is determined as an identification node. At the same time, this solution extends downward from the first convergence point to check the power supply endpoints it covers. If the first convergence point extends downward and only completely includes all the power supply endpoints in the first power supply endpoint set, and the number of these endpoints is greater than or equal to the number of established nodes (usually determined by device capabilities or management requirements), the first convergence point is determined as an identification node.

[0097] A first structure tree is constructed based on the first convergence point and the first power supply endpoint set. The parent node of the first structure tree is the first convergence point, the child nodes are the first power supply endpoint set, and the grandchild nodes are each power supply endpoint in the first power supply endpoint set. Finally, this solution constructs a first structure tree based on the first convergence point and the first power supply endpoint set. The parent node of the first structure tree is the first convergence point, the child nodes are each sub-set in the first power supply endpoint set, and the grandchild nodes are each power supply endpoint. For example, there are multiple rooms (child nodes) under a distribution box (parent node), and each room has specific sockets, etc. (grandchild nodes).

[0098] Based on the above embodiment, it also includes:

[0099] If the first aggregation point also includes all the power supply endpoints in other first power supply endpoint sets in the downward dimension and the number of all the power supply endpoints is greater than or equal to the number of established nodes, the corresponding first power supply endpoint sets are merged and the first aggregation point is used as the second aggregation point. It can be understood that if a first aggregation point also includes all the power supply endpoints in other first power supply endpoint sets in the downward dimension and the total number of these power supply endpoints is greater than or equal to the number of established nodes, these power supply endpoint sets are merged. For example, a distribution box not only supplies power to one room, but also to other adjacent rooms, and the number of power supply endpoints in all rooms is sufficient. The common aggregation point of the merged power supply endpoint set is used as the second aggregation point.

[0100] Construct a corresponding second structure tree, in which the parent node of the second structure tree is the second aggregation point, the child node is each first power supply endpoint set, and the grandchild node is each power supply endpoint in each first power supply endpoint set. This solution constructs a second structure tree based on the second aggregation point and the merged power supply endpoint set. The parent node of the second structure tree is the second aggregation point, the child node is each first power supply endpoint set, and the grandchild node is each power supply endpoint in each first power supply endpoint set.

[0101] Based on the above embodiment, it also includes:

[0102] If all power supply endpoints are less than the number of nodes established, then determine the adjacent building spaces of the building space, and each building space has a preset adjacent building space. If the number of power supply endpoints of the current building space is less than the number of nodes established, then find its adjacent building space. Each building space will have preset adjacent building spaces, which can be determined through the building floor plan or space management system.

[0103] The power supply endpoints corresponding to the current building space and the adjacent building space are extended upward in a convergent manner to determine the third convergence point in the power supply line distribution map within the first power supply endpoint set corresponding to the current building space and the adjacent building space. This solution requires the power supply endpoints of the current building space and its adjacent building space to be extended upward in a convergent manner to find the third convergence point of these power supply endpoint sets in the power supply line distribution map.

[0104] Construct a corresponding third structure tree, in which the mother node of the third structure tree is the third convergence point, the child node is each first power supply endpoint set, and the grandchild node is each power supply endpoint in each first power supply endpoint set. Finally, construct a third structure tree based on the third convergence point and the power supply endpoint set. The mother node of the third structure tree is the third convergence point, the child node is each first power supply endpoint set, and the grandchild node is each power supply endpoint in each first power supply endpoint set. Through the above steps, this scheme can handle the merging of power supply endpoint sets and the situation of a small number of power supply endpoints, making the determination of identification nodes more flexible and accurate. The constructed multi-level structure tree can record and analyze energy consumption data in more detail, and support refined energy consumption management.

[0105] S112: configuring corresponding non-intrusive load identification devices at the identification nodes, and determining corresponding space loads based on the attributes of the building space corresponding to each identification node.

[0106] This solution configures corresponding non-intrusive load identification devices at each identification node. These devices can identify power usage by monitoring parameters such as current and voltage without directly contacting the circuit. Based on the attributes of the building space corresponding to each identification node (such as purpose, etc.), the load of the space is determined. For example, an office may have computers, printers, lighting equipment, etc. The total load of these devices is the load of the space.

[0107] S113, receiving the load identification waveform configured by the user for each spatial load, so that the corresponding non-intrusive load identification device performs spatial load identification processing based on the load identification waveform.

[0108] Finally, the solution receives the load identification waveform configured by the user for each spatial load. These waveforms are usually predefined and are used to represent the power consumption characteristics of different devices or combinations of devices. The non-intrusive load identification device compares and processes the actual energy consumption data based on the received load identification waveform to identify the specific device and its operating status.

[0109] S12. Obtain load identification waveforms at different identification times within a preset time period and perform time statistics to obtain the load energy usage duration of each load.

[0110] Among them, the preset time period represents a specific time range pre-defined in energy consumption monitoring and analysis, such as an hour, a day, a week, etc.; the identification time represents the specific time point for load identification within a specific time period; time statistics means accumulating the working hours of each electrical equipment within a certain time period, and the total duration obtained is the load energy consumption duration. For example, August 8, 2024 is a preset time period. During this time period, it is turned on from 8 am to 9 am and from 3 pm to 4 pm. The load energy consumption duration is the sum of the two working hours, that is, 2 hours.

[0111] S13, obtaining the load identification waveforms at different identification times within the first preset time period and performing mean calculation to obtain the average load, obtaining the load sub-information of each load based on the average load and the load energy consumption duration, and obtaining the load energy consumption data by counting all the load sub-information.

[0112] Among them, the first preset time period is a working time period within the preset time period, which can be specifically one hour, such as 11:00 am to 12:00 pm on August 7, 2024; the average load represents the average value of the load values ​​corresponding to all identification moments (time points) within the first preset time period, indicating the average power consumption status of the equipment within the time period; the load sub-information represents the overall energy consumption data of a single load within the preset time period; the load energy consumption data represents the energy data consumed by the power load of all electrical equipment in the building.

[0113] See also Figure 3 , Figure 3 1 is a schematic diagram of calculating an average load provided by an embodiment of the present application. The two first preset time periods (working time periods) are time period A and time period B, respectively. The load value at each time point of time period A is averaged to obtain a first average load a. The load value at each time point of time period B is averaged to obtain a first average load b. The second average load is the average of the first average load a and the first average load b.

[0114] In some embodiments, step S13 (obtaining load identification waveforms at different identification times within the first preset time period and performing mean calculation to obtain an average load, and obtaining load sub-information of each load based on the average load and load energy usage duration) specifically includes steps S131 to S134:

[0115] S131. Obtain load identification waveforms at different identification times within a first preset time period, wherein the load identification waveforms include load identification waveforms, and extract the load value at each time point in each load identification waveform.

[0116] Among them, the load identification waveform represents a curve of the charge value changing with time. The load identification waveform can be used to determine the various working time periods of the electrical equipment within the preset time period. For example, if the TV is turned on from 11:00 to 11:10 and from 11:50 to 12:00, then 11:00 to 11:10 and 11:50 to 12:00 are the two working time periods corresponding to the TV; the time point is the identification moment, and the load value represents the power value of the electrical equipment at a specific time point in the load identification waveform.

[0117] Specifically, within a first preset time period (working time period), load identification is performed multiple times to obtain a load identification waveform, which includes power values ​​of different electrical equipment at various time points.

[0118] Example: The preset time period is August 7, 2024, and 3:00 p.m. to 4:00 p.m. is the first preset time period. The load identification waveform of the refrigerator is recorded, and the energy consumption data every 10 minutes is extracted from the load identification waveform, which are: 0.1kW, 0kW, 0kW, 0.2kW, 0.1kW respectively. The above load values ​​reflect the power consumption of the refrigerator at each time point.

[0119] It should be noted that obtaining specific energy consumption data of electrical equipment at different time points can provide raw data for subsequent mean calculation and energy consumption analysis.

[0120] S132, performing mean calculation based on the load value at each time point to obtain a first average load of the corresponding load identification waveform.

[0121] The first average load represents the average value of the load values ​​corresponding to all time points in a certain working time period (first preset time period).

[0122] Example: Calculate the average load value of the above refrigerator, that is: (0.1 + 0.1 + 0.1 + 0.2 + 0.1 + 0.1) / 6 = 0.1167kW. This average value is the first average load of the refrigerator from 3:00 to 4:00 p.m. on August 7, 2024.

[0123] S133, calculating the first average loads of all load identification waveforms within a preset time period and then calculating the average value to obtain a second average load.

[0124] The second average load represents the average value of all first average loads within a preset time period.

[0125] For example: In the preset time period of August 7, 2024, the refrigerator is also in a working time period from 7:00 pm to 8:00 pm (the first preset time period). If the first average load of the refrigerator from 7:00 pm to 8:00 pm is 0.1 kW, then the second average load is (0.1167 + 0.1) / 2 = 0.10835 kW.

[0126] It should be noted that the second average load represents a typical energy consumption level in the entire preset time period.

[0127] S134. Obtain load sub-information of each load by multiplying the load energy usage durations of all load identification waveforms based on the second average load.

[0128] The load energy consumption duration indicates the total time that a certain electrical device is turned on and consumes energy within a preset time period; and the load sub-information indicates the overall energy consumption data of a single electrical device within a preset time period.

[0129] For example, the preset time period is August 7, 2024. Through the load identification waveform, it can be known that the refrigerator has two working time periods, namely 3:00 to 4:00 p.m. and 7:00 to 8:00 p.m., so the corresponding load energy consumption time of the refrigerator is 2 hours, and the load sub-information of the refrigerator is 0.10835kW×2 hours=0.2167kW;

[0130] If the second average load of the fan is 0.2kW and the load energy consumption duration is 0.5 hours, the load sub-information of the fan is 0.2kW × 0.5 hours = 0.1kWh.

[0131] S2. Obtain first electric energy data of the photovoltaic storage system corresponding to the building and second electric energy data corresponding to the city power station, calculate and process the carbon footprint of the photovoltaic storage system to obtain first carbon footprint information, and decompose the first carbon footprint information to obtain second carbon footprint information per kilowatt-hour.

[0132] Among them, the photovoltaic storage system represents a system composed of solar photovoltaic (PV) components and energy storage devices (such as batteries); the first electric energy data represents the electric energy data generated and supplied by the photovoltaic storage system, reflecting the total amount of electricity provided by the photovoltaic power generation and energy storage system to the building; the city power represents the electricity provided by the public power grid, usually provided by the power company; the second electric energy data represents the electric energy data obtained from the city power, reflecting the total amount of electricity obtained by the building from the public power grid; the first carbon footprint information represents the carbon footprint information generated by the photovoltaic storage system, that is, the total amount of carbon emissions generated by the photovoltaic storage system during the power generation and power transmission process; the kilowatt-hour represents electricity per kilowatt-hour, which is usually used to represent the unit of electricity consumption or production. The total carbon emissions in the first carbon footprint information are refined to per kilowatt-hour, and the carbon emissions corresponding to each kilowatt-hour of electricity can be obtained. The carbon emissions corresponding to each kilowatt-hour of electricity is the second carbon footprint information of the kilowatt-hour.

[0133] It should be noted that the photovoltaic storage system provides electricity to buildings by combining photovoltaic power generation with energy storage technology.

[0134] In some embodiments, step S2 (obtaining first electric energy data of the photovoltaic storage system corresponding to the building and second electric energy data corresponding to the city power station, calculating and processing the carbon footprint of the photovoltaic storage system to obtain first carbon footprint information, and decomposing the first carbon footprint information to obtain second carbon footprint information per kilowatt-hour) specifically includes steps S21 to S22:

[0135] S21, obtaining power supply information of the photovoltaic storage system of the building in a first preset time period to obtain first power data, and obtaining power supply information of the mains in the first preset time period to obtain second power data.

[0136] Among them, the power supply information represents the amount of electric energy and related power supply parameters provided by the photovoltaic storage system or the mains power system in the building within a specific time period; the first electric energy data represents the electric energy data generated and supplied by the photovoltaic storage system, reflecting the total amount of electric energy provided to the building by the photovoltaic power generation and energy storage system; the second electric energy data represents the electric energy data obtained from the mains, reflecting the total amount of electric energy obtained by the building from the public power grid.

[0137] For example: from 9:00 a.m. to 12:00 p.m. on August 7, 2024, the photovoltaic storage system provided 10 kilowatt-hours (kWh) of electricity to the building. The above information is the power supply information of the photovoltaic storage system, and the first electricity data is 10 kilowatt-hours (kWh); the city power provided 20 kilowatt-hours (kWh) of electricity to the building from 9:00 a.m. to 12:00 p.m. on August 7, 2024. The above information is the power supply information of the city power, and the second electricity data is 20 kilowatt-hours (kWh).

[0138] S22. Calculate and process the first carbon footprint information of the photovoltaic storage system based on the production information and transportation information of the photovoltaic storage system equipment in the database, and obtain the second carbon footprint information of the photovoltaic storage system based on the first carbon footprint information and the rated power generation of the photovoltaic storage system.

[0139] Among them, the production information includes information such as the power generation materials of the power generation equipment of the photovoltaic storage system during the power generation process; the transportation information includes the transmission path of electricity from the power generation point to the load point and its loss; the first carbon footprint information represents the total carbon emissions generated by the photovoltaic storage system during the power generation and power transmission process; the rated power generation includes but is not limited to the total power generation of the photovoltaic storage system; the second carbon footprint information per kilowatt-hour represents the carbon emissions corresponding to each kilowatt-hour of electricity generated by the photovoltaic storage system.

[0140] For example: the photovoltaic storage system generates a total of 1,000 kWh of electricity in one day. The carbon emissions during the power generation process are 40 kg of carbon dioxide. 5 kg of carbon dioxide will be generated during the transmission process from the photovoltaic storage system to the building load. The first carbon footprint information = carbon emissions from power generation + carbon emissions from transmission = 40 kg of carbon dioxide + 5 kg of carbon dioxide = 45 kg of carbon dioxide, that is, the total carbon emissions generated by the photovoltaic storage system during the power generation and transmission process are 45 kg of carbon dioxide. The second carbon footprint information = the first carbon footprint information / total power generation of the photovoltaic storage system = 45 kg of carbon dioxide / 1,000 kWh = 0.045 kg of carbon dioxide / kWh.

[0141] It should be noted that there will be energy loss during the power transmission process, and this part of the loss will also increase carbon emissions and therefore needs to be taken into account.

[0142] S3. Perform an estimation process based on the second carbon footprint information, the first electric energy data, and the second electric energy data according to a preset estimation model to obtain an indirect carbon emission factor of the building.

[0143] The estimation model represents a mathematical model for calculating the carbon emissions of a building; the building indirect carbon emission factor represents the ratio of the total carbon emissions of a building to the total electricity consumption in a specific time period, reflecting the carbon emissions corresponding to each kilowatt-hour of electricity.

[0144] In some embodiments, step S3 (based on the second carbon footprint information, the first electric energy data, and the second electric energy data, performing an estimation process according to a preset estimation model to obtain an indirect carbon emission factor of the building) specifically includes steps S31 to S33:

[0145] S31. An estimation model obtains a first electricity consumption value of first electric energy data, and calculates first carbon emission sub-information based on the first electricity consumption value and second carbon footprint information.

[0146] Among them, the first electricity consumption value represents the amount of electricity data obtained from the photovoltaic storage system, usually the electricity consumption in a certain period of time; the first carbon emission sub-information represents the carbon emissions generated by the building obtaining electricity from the photovoltaic storage system.

[0147] Specifically, the estimation model obtains the electricity consumption of the photovoltaic storage system, multiplies the electricity consumption by the second carbon footprint information of the photovoltaic storage system, and calculates the carbon emissions generated by the photovoltaic storage system, that is, the first carbon emission sub-information.

[0148] It should be noted that the first electricity consumption value reflects the electricity obtained by the building from the photovoltaic storage system during a specific time period and can be used to calculate the carbon emissions related to the photovoltaic storage system.

[0149] S32. The estimation model obtains the second electricity consumption value of the second electric energy data and the mains carbon emission factor to calculate the second carbon emission sub-information, and obtains the sum of the estimated carbon emissions based on the first carbon emission sub-information and the second carbon emission sub-information.

[0150] Among them, the second electricity consumption value represents the amount of electricity data obtained from the mains, usually the electricity consumption in a certain time period; the mains carbon emission factor represents the carbon emissions generated by the building obtaining electricity from the mains; the second carbon emission sub-information represents the carbon emissions generated by the building obtaining electricity from the mains; the sum of the estimated carbon emissions represents the total carbon emissions of the building in a specific time period, specifically the sum of the first carbon emission sub-information and the second carbon emission sub-information.

[0151] Specifically, the estimation model obtains the electricity consumption of the mains, multiplies the electricity consumption by the mains carbon emission factor, calculates the carbon emissions generated by the mains power supply, that is, the second carbon emission sub-information, adds the first carbon emission sub-information and the second carbon emission sub-information, and obtains the total carbon emissions of the building in a specific time period.

[0152] It should be noted that the second electricity consumption value reflects the electricity obtained by the building from the mains during a specific time period, and can be used to calculate the carbon emissions related to the mains. The mains carbon emission factor can be adjusted according to actual conditions, and the present invention does not impose specific restrictions on this.

[0153] S33. Calculate the sum of the first electricity consumption value and the second electricity consumption value to obtain a total electricity consumption value, and obtain an indirect carbon emission factor of the building based on the total electricity consumption value and the sum of the estimated carbon emissions.

[0154] The total electricity consumption value represents the total electricity consumption of the building in a specific time period; and the building indirect carbon emission factor represents the carbon emissions corresponding to each kilowatt-hour of electricity consumed by the building.

[0155] Specifically, the electricity consumption of the photovoltaic storage system and the mains electricity is added together to obtain the total electricity consumption value, and the sum of the estimated carbon emissions is divided by the total electricity consumption value to calculate the indirect carbon emission factor of the building.

[0156] It should be noted that for calculating the indirect carbon emission factor of a building, the electricity consumption of photovoltaic power generation and municipal electricity needs to be considered comprehensively, because buildings rely on both energy sources in actual operation, that is, the carbon footprint of all sources of electricity is taken into account.

[0157] S4. The direct carbon emission factor of the equipment is calculated based on the load energy consumption data of the load, and a secondary estimation is performed based on the indirect carbon emission factor and the direct carbon emission factor of the building to obtain the actual carbon emissions.

[0158] Among them, the load represents all electrical equipment in the building, and the load energy consumption data represents the energy data consumed by the power load of all electrical equipment in the building; the direct carbon emission factor represents the ratio of carbon emissions to energy consumption, reflecting the carbon emission intensity of the equipment when using electricity; the building indirect carbon emission factor represents the indirect carbon emission factor of the building as a whole, reflecting the carbon emission intensity when the building uses electricity from different sources; the actual carbon emissions represent the total carbon emissions of the building during actual operation, calculated based on the direct carbon emission factor of the equipment and the indirect carbon emission factor of the building.

[0159] In some embodiments, step S4 (calculating the direct carbon emission factor of the equipment based on the load energy consumption data, and performing secondary estimation based on the indirect carbon emission factor and the direct carbon emission factor of the building to obtain the actual carbon emissions) specifically includes steps S41 to S42:

[0160] S41. Based on the sum of the estimated carbon emissions and the load energy consumption data, the direct carbon emission factors of the equipment corresponding to all loads are calculated.

[0161] Specifically, the load energy consumption data of each device in the building is combined with the sum of the estimated carbon emissions calculated previously to calculate the direct carbon emission factor of each device, which represents the carbon emissions corresponding to each unit of energy consumption in the actual operation of the device.

[0162] It should be noted that the direct carbon emission factor of the equipment is calculated based on the load energy consumption data. It reflects the carbon emissions corresponding to the electricity directly consumed by a single device during use. If the total electricity consumption is 1,000 kWh, but due to line losses, only 900 kWh of electricity actually reaches each device. Then, the direct carbon emission factor will be calculated based on this 900 kWh of electricity, because this is the actual electricity consumed by the equipment.

[0163] S42. Calculate the factor difference between the building's indirect carbon emission factor and the direct carbon emission factor, and obtain the actual carbon emissions of the building based on the factor difference.

[0164] The factor difference represents the difference between the indirect carbon emission factor and the direct carbon emission factor of the building, and is used to measure the difference between the carbon emissions on equipment and the overall carbon emissions of the building.

[0165] It should be noted that the indirect carbon emission factor of the building takes into account the energy loss generated by the entire building during the power transmission process. For example, the 100-degree electricity loss mentioned above is part of the indirect carbon emissions, and these 100 degrees are the factor difference. Because this part of the electricity is not actually used by the equipment, it still causes carbon emissions to the environment. Therefore, the indirect carbon emission factor considers the carbon emissions in the entire power transmission.

[0166] In some embodiments, step S42 (calculating the indirect carbon emission factor and the direct carbon emission factor of the building to obtain the factor difference, and obtaining the actual carbon emissions of the building based on the factor difference) specifically includes steps S421 to S424:

[0167] S421. If the absolute value of the factor difference is less than the preset value of the factor, the actual carbon emissions of the building are calculated by multiplying the electricity consumption value of the load energy consumption data and the building indirect carbon emission factor.

[0168] Among them, the preset value of the factor represents a pre-set threshold for judging the level of loss, which can be adjusted according to actual conditions; the electricity consumption value represents the amount of electricity consumed by the building in a certain period of time; the actual carbon emissions represent the total carbon emissions of the building during actual operation, calculated based on the direct carbon emission factor of the equipment and the indirect carbon emission factor of the building.

[0169] Specifically, when the factor difference is small, the transportation loss is ignored, indicating that the carbon emissions on the equipment are basically consistent with the carbon emissions of the building as a whole. Therefore, the load energy consumption data and the building indirect carbon emission factor can be directly used to calculate the actual carbon emissions of the building. For example, if the loss is 10 degrees at 1000 degrees, the loss is not high and can be ignored.

[0170] S422. If the absolute value of the factor difference is greater than or equal to the factor preset value, a power supply network topology map corresponding to the building is generated, and the power supply network topology map includes a mains power supply, a photovoltaic storage system power supply, a wire network, and a load.

[0171] The power supply network topology diagram represents a diagram describing the power supply system of a building, including key components such as the mains power supply, the photovoltaic storage system power supply, the wire network (transformer, actual wires, etc.) and the load.

[0172] Specifically, when the factor difference is large, it means that there is a significant difference between the carbon emissions on the equipment and the overall carbon emissions of the building. At this time, it is necessary to generate a power grid topology map to analyze the power distribution and its impact on carbon emissions in more detail.

[0173] S423. Calculate the first actual carbon emission of the load in the building based on the electricity consumption value of the load energy consumption data and the building indirect carbon emission factor.

[0174] The first actual carbon emissions represent the carbon emissions of the equipment during actual operation.

[0175] Specifically, after generating the power grid topology, the carbon emissions of each device are calculated first by multiplying the load energy consumption data with the building indirect carbon emission factor.

[0176] S424. Calculate based on the first electricity consumption value, the second electricity consumption value and the building indirect carbon emission factor to obtain a second actual carbon emission of the electrical wire network in the building.

[0177] Among them, the second actual carbon emissions represent the carbon emissions of the power line network.

[0178] Specifically, by analyzing the carbon emissions of electricity transmission (i.e., the wire network) inside the building, the carbon emissions of the wire network are calculated by using electricity data and the building indirect carbon emission factor.

[0179] It should be noted that the carbon emissions of the building’s equipment and the power line network were calculated separately, taking into account the carbon emissions of the building’s equipment and the transmission losses of the power line network.

[0180] In some embodiments, the green low-carbon building energy consumption assessment and carbon footprint refinement estimation method further includes steps A1 to A3:

[0181] A1. Obtain the distance of the power supply network between the photovoltaic storage system and each load to obtain the corresponding first extra power loss distance, and obtain the distance of the power supply network between the mains and each load to obtain the corresponding second extra power loss distance.

[0182] Among them, the first additional energy loss distance represents the distance of the power supply network between the photovoltaic storage system and each load, which can be used to estimate the energy loss caused by distance during the transmission of electricity; the second additional energy loss distance represents the distance of the power supply network between the mains and each load, which can be used to evaluate the energy loss when the mains is supplied.

[0183] Specifically, the power supply distances between the photovoltaic storage system and the mains and each load are measured and recorded. These distances can be used to estimate the losses during power transmission. Generally, the longer the distance, the greater the loss.

[0184] A2. Traverse the first extra energy loss distance and the second extra energy loss distance of each load in turn, determine the minimum loss distance of each load, and count the loads whose minimum loss distance is the first extra energy loss distance to obtain a first load set.

[0185] Among them, the minimum loss distance means the power supply path with the minimum power loss between each load selected after traversing the distance between the photovoltaic storage system and the city power supply.

[0186] Specifically, by comparing the power supply path lengths of the photovoltaic storage system and the city power, a power supply source with the least power loss is selected for each load. After determining the minimum loss distance, it is counted which loads have the lowest loss from the photovoltaic storage system, and these loads are grouped as the first load set.

[0187] It should be noted that the minimum loss distance can determine the optimal power supply path, reduce unnecessary power loss, and improve energy utilization efficiency.

[0188] A3. Analyze and process the historical load energy consumption data of each load in the first load set to obtain adjustment data of the photovoltaic energy storage system.

[0189] Among them, the first load set represents the load set whose statistical results show that the lowest loss distance comes from the photovoltaic storage system; the adjustment data represents the optimized power supply strategy data provided for the photovoltaic storage system after analyzing the historical energy consumption data of the first load set.

[0190] Specifically, the historical energy consumption data of each load in the first load set is analyzed to determine the best power supply strategy for the photovoltaic storage system and generate adjustment data.

[0191] It should be noted that the first load set is used to further analyze and optimize the use of the photovoltaic storage system to ensure that the photovoltaic storage system is used to power low-loss loads as much as possible; the adjustment data is used to adjust the power supply strategy of the photovoltaic storage system to ensure that load demand is met in the best way and maximize energy efficiency and carbon emission reduction effects.

[0192] In some embodiments, step A3 (obtaining adjustment data of the photovoltaic energy storage system based on analysis and processing of historical load energy consumption data of each load in the first load set) specifically includes steps A31 to A33:

[0193] A31. Obtain historical load energy consumption data of each load in the first load set, and classify and process the data in a time sequence according to a preset length to obtain a corresponding load energy consumption sequence.

[0194] Among them, historical load energy consumption data represents the record of electric energy consumption of each load in a certain period of time in the past, usually recorded in time series; time series classification processing represents classifying and arranging the load energy consumption data according to a preset time length (such as hours, days, weeks) to form a time series data; the load energy consumption sequence represents a sequence of load energy consumption data arranged in chronological order, reflecting the energy consumption changes of the load at different time points.

[0195] Specifically, the historical energy consumption data of each load is collected, and then these data are classified and sorted by time period to form a time series data set, which can identify the energy consumption pattern of the load, especially the energy consumption distribution at different time points, and provide basic data for subsequent analysis.

[0196] A32. Count the sum of the load values ​​of all loads in the same time series within the energy consumption sequence, and compare the sum of the load values ​​of all time series to determine the maximum sum of the load values.

[0197] Among them, the same time series means the energy consumption records of different loads at the same time point in the energy consumption sequence, and the data of the same time series is used to analyze the total energy consumption of multiple loads at a specific time point; the sum of the load values ​​means that this refers to the sum of the energy consumption values ​​of all loads in the same time series, which reflects the total energy consumption at a certain time point and can be used to identify the peak period of energy consumption.

[0198] Specifically, the total energy consumption of all loads at the same time point is calculated, and the total energy consumption is compared to find the largest value. By identifying the sum of the maximum load values, the time point when the load consumed the most power in history can be determined, providing a basis for adjusting the photovoltaic storage system.

[0199] A33. Compare the sum of the maximum load values ​​with the current rated load value of the photovoltaic storage system to obtain the adjusted load value of the photovoltaic storage system.

[0200] Among them, the current rated load value of the photovoltaic storage system represents the maximum load that the photovoltaic storage system can currently withstand, that is, its maximum output power under normal operating conditions. The rated load value is used to compare with the sum of the maximum load values ​​of the loads to evaluate whether the capacity of the photovoltaic storage system can meet the demand; the adjusted load value represents a new load output target set for the photovoltaic storage system based on the analysis results, which is usually used to adjust the system operation strategy to adapt to the load demand. The adjusted load value is used to optimize the operation of the photovoltaic storage system to ensure that it meets the actual electricity demand without overloading.

[0201] Specifically, by adjusting the load setting of the photovoltaic storage system, it is ensured that the system can cope with possible high load conditions in the future, avoid overload operation, and improve the stability and efficiency of the system.

[0202] In some embodiments, step A33 (comparing the sum of the maximum load values ​​with the current rated load value of the photovoltaic storage system to obtain the adjusted load value of the photovoltaic storage system) specifically includes steps A331 to A332:

[0203] A331. If the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic storage system is less than or equal to the increase threshold, the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic storage system is calculated to obtain the adjusted load value.

[0204] Among them, the sum of the maximum load values ​​indicates the total energy consumption values ​​of different loads at a certain point in time in the historical energy consumption data, representing the maximum load that the system may need to bear at that point in time. The sum of the maximum load values ​​is used to evaluate the load demand under extreme conditions and help determine the adjustment strategy of the photovoltaic storage system; the current rated load value indicates the maximum output power that the photovoltaic storage system can withstand under normal operating conditions, which can be compared with the actual demand to determine whether the system needs to be adjusted; the difference indicates the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic storage system, which is used to determine whether the system can withstand the load under the existing configuration or whether load adjustment is needed.

[0205] Specifically, if the difference between the sum of the maximum load values ​​and the rated load value does not exceed the increase threshold, it is considered that the photovoltaic storage system can safely bear this load. In this case, the difference is used as the new adjusted load value to increase the load carrying capacity of the system, which can ensure that the photovoltaic storage system can increase its load carrying capacity appropriately without overloading, thereby better adapting to possible future load requirements.

[0206] A332: If the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic energy storage system is greater than the increase threshold, the increase threshold is used as the load adjustment value.

[0207] Among them, the increase threshold represents a preset limit, which indicates the maximum load that the photovoltaic storage system can increase. If the limit is not exceeded, it is considered that the system can operate safely. It is used to limit the adjustment range of the photovoltaic storage system to prevent excessive adjustment from causing system instability or overload; the adjusted load value represents the load value calculated according to the difference or directly set, which is used to adjust the operating parameters of the photovoltaic storage system to cope with load demand, and to optimize the operating strategy of the photovoltaic storage system to ensure its safety and efficiency when under load.

[0208] Specifically, if the difference exceeds the increase threshold, the system will choose to use the increase threshold as the load adjustment value instead of directly using the difference. This can avoid system instability or overload due to excessive load adjustment. By limiting the range of adjusted load values, the photovoltaic storage system is prevented from exceeding its safe operating range during the adjustment process, thereby ensuring the stability and safety of the system.

[0209] By analyzing the difference between the maximum load value and the current rated load value of the photovoltaic storage system, the load setting of the photovoltaic storage system is adjusted to ensure that the system can meet the load demand to the maximum extent under the premise of safety, while avoiding system instability or overload caused by excessive adjustment.

[0210] In this embodiment, a method for green and low-carbon building energy consumption assessment and refined carbon footprint estimation is proposed, including: obtaining the time-sharing energy consumption of the building and performing non-invasive load identification to obtain a load identification waveform, and calculating the corresponding load energy consumption data based on the load identification waveform and the load energy usage duration; obtaining the first electric energy data of the photovoltaic storage system corresponding to the building, and the second electric energy data corresponding to the municipal power station, calculating and processing the carbon footprint of the photovoltaic storage system to obtain the first carbon footprint information, and decomposing the first carbon footprint information to obtain the second carbon footprint information per kilowatt-hour; performing an estimation process based on the second carbon footprint information, the first electric energy data, and the second electric energy data according to a preset estimation model to obtain an indirect carbon emission factor of the building; calculating the direct carbon emission factor of the equipment based on the load energy consumption data of the load, and performing a secondary estimation based on the indirect carbon emission factor and the direct carbon emission factor of the building to obtain the actual carbon emissions. The embodiment of the present invention can perform accurate energy consumption assessment and carbon footprint estimation for buildings.

[0211] like Figure 4 FIG. 1 is a schematic diagram of a green low-carbon building energy consumption assessment and carbon footprint refinement estimation system provided by an embodiment of the present invention. The green low-carbon building energy consumption assessment and carbon footprint refinement estimation system includes:

[0212] The energy consumption calculation module is used to obtain the time-sharing energy consumption of the building and perform non-intrusive load identification to obtain the load identification waveform. The corresponding load energy consumption data is calculated based on the load identification waveform and the load energy consumption duration.

[0213] The information acquisition module is used to obtain the first electric energy data of the photovoltaic storage system corresponding to the building and the second electric energy data corresponding to the municipal power station, calculate and process the carbon footprint of the photovoltaic storage system to obtain the first carbon footprint information, and decompose the first carbon footprint information to obtain the second carbon footprint information per kilowatt-hour.

[0214] The first estimation module is used to perform an estimation process based on the second carbon footprint information, the first electric energy data, and the second electric energy data according to a preset estimation model to obtain an indirect carbon emission factor of the building.

[0215] The second estimation module is used to calculate the direct carbon emission factor of the equipment based on the load energy consumption data, and perform secondary estimation based on the indirect carbon emission factor and direct carbon emission factor of the building to obtain the actual carbon emissions.

[0216] It should be noted that the above-mentioned green and low-carbon building energy consumption assessment and carbon footprint refined estimation system can execute the green and low-carbon building energy consumption assessment and carbon footprint refined estimation method provided in the embodiment of the present invention. For technical details and beneficial effects that are not fully described in the embodiment of the green and low-carbon building energy consumption assessment and carbon footprint refined estimation system, please refer to the green and low-carbon building energy consumption assessment and carbon footprint refined estimation method provided in the embodiment of the present invention.

[0217] Based on the above embodiment, it also includes:

[0218] Establish a root node, combine and connect the parent nodes of the first structure tree, the second structure tree, and the third structure tree with the root node to obtain a total structure tree. This solution will create a total root node, combine and connect the parent nodes of the first structure tree, the second structure tree, and the third structure tree constructed previously with the root node to form a comprehensive total structure tree.

[0219] Based on the circuit lengths between the aggregation node and the photovoltaic storage system and / or the mains in the overall structure tree, the first connection length ratio of each aggregation node is obtained. This solution calculates the circuit lengths between the aggregation node and the photovoltaic storage system and / or the mains to obtain the first connection length ratio of each aggregation node.

[0220] The line length from each load in each first load set to the aggregation node is obtained to obtain the second connection length ratio of each load. At the same time, this solution obtains the line length from each load in each first load set to the aggregation node to obtain the second connection length ratio of each load.

[0221] The first connection line length between the root node and the child node is obtained based on the first connection length ratio and the first reference length, the second connection line length is obtained based on the second connection length ratio and the second reference length, and the second connection line length is evenly divided to obtain the third connection line length. This solution calculates the first connection line length between the root node and the child node based on the first connection length ratio and the preset first reference length. The second connection line length is calculated based on the second connection length ratio and the preset second reference length. The second connection line length is evenly divided to obtain the third connection line length for connecting the child node and the corresponding parent node.

[0222] The root node is connected to the corresponding parent node through the first connection line length, the parent node is connected to the corresponding child node based on the third connection line length, and the child node is connected to the corresponding parent node based on the third connection line length. Using the calculated first connection line length, the root node is connected to the corresponding parent node in the first, second, and third structure trees. Based on the third connection line length, the parent node is connected to the corresponding child node in the first, second, and third structure trees.

[0223] Fill the carbon emission information of each non-intrusive load identification device into the corresponding parent node, fill the carbon emission information of each set into the child node, fill the carbon emission information of each device into the grandchild node, and divide the second actual carbon emission of the power line network according to the length ratio to obtain the carbon emission information of each connecting line. Finally, this scheme will fill the carbon emission information of each non-intrusive load identification device into the corresponding parent node. Fill the carbon emission information of each set into the child node. Fill the carbon emission information of each device into the grandchild node. Divide the second actual carbon emission of the power line network according to the length ratio of each connecting line to obtain the carbon emission information of each connecting line.

[0224] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that each embodiment can be implemented by means of software plus a general hardware platform, or by hardware. It can be understood by those skilled in the art that all or part of the processes in the above embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0225] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may also be combined, the steps may be implemented in any order, and there are many other changes in different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings, characterized in that: include: Obtain the time-sharing energy consumption of the building and perform non-intrusive load identification to obtain the load identification waveform. Based on the load identification waveform and the load energy consumption duration, calculate the corresponding load energy consumption data; Obtaining first electric energy data of the photovoltaic storage system corresponding to the building and second electric energy data corresponding to the city power station, calculating and processing the carbon footprint of the photovoltaic storage system to obtain first carbon footprint information, and decomposing the first carbon footprint information to obtain second carbon footprint information per kilowatt-hour of electricity; Based on the second carbon footprint information, the first electric energy data, and the second electric energy data, an estimation process is performed according to a preset estimation model to obtain an indirect carbon emission factor of the building; The direct carbon emission factor of the equipment is calculated based on the load energy consumption data, and the actual carbon emissions are obtained by secondary estimation based on the indirect carbon emission factor and direct carbon emission factor of the building; The method of obtaining the time-sharing energy consumption of the building and performing non-intrusive load identification to obtain a load identification waveform, and calculating corresponding load energy consumption data based on the load identification waveform and the load energy consumption duration includes: Acquire and identify time-sharing energy consumption based on a pre-configured non-intrusive load identification device, obtain a corresponding load identification waveform, and determine a corresponding start-up load based on the load identification waveform; Obtain the load identification waveforms at different identification times within a preset time period and perform time statistics to obtain the load energy usage duration of each load; Obtain load identification waveforms at different identification times within a first preset time period and perform mean calculation to obtain an average load, obtain load sub-information of each load based on the average load and load energy consumption duration, and obtain load energy consumption data by counting all load sub-information; The step of acquiring load identification waveforms at different identification times within the first preset time period and performing mean calculation to obtain an average load, and obtaining load sub-information of each load based on the average load and load energy usage duration includes: Obtaining load identification waveforms at different identification moments within a first preset time period, wherein the load identification waveforms include load identification waveforms, and extracting a load value at each time point in each load identification waveform; Performing mean calculation based on the load value at each time point to obtain a first average load of the corresponding load identification waveform; The first average load of all load identification waveforms within a preset time period is statistically calculated and then the average is calculated to obtain a second average load; Based on the second average load and the multiplication of the load energy usage duration of all load identification waveforms, the load sub-information of each load is obtained; The load energy consumption data based on the load is used to calculate the direct carbon emission factor of the equipment, and the actual carbon emissions are obtained by secondary estimation based on the indirect carbon emission factor and the direct carbon emission factor of the building, including: Based on the sum of estimated carbon emissions and load energy consumption data, the direct carbon emission factors of the equipment corresponding to all loads are calculated; Calculate the factor difference between the building's indirect carbon emission factor and direct carbon emission factor, and obtain the building's actual carbon emissions based on the factor difference; The calculation of the indirect carbon emission factor and the direct carbon emission factor of the building to obtain the factor difference, and obtaining the actual carbon emissions of the building based on the factor difference, includes: If the absolute value of the factor difference is less than the preset value of the factor, the actual carbon emissions of the building are calculated by multiplying the electricity consumption value of the load energy consumption data and the building indirect carbon emission factor; If the absolute value of the factor difference is greater than or equal to the factor preset value, a power supply network topology map corresponding to the building is generated, wherein the power supply network topology map includes a city power supply, a photovoltaic storage system power supply, a wire network, and a load; The first actual carbon emission of the load in the building is calculated based on the electricity consumption value of the load energy consumption data and the building indirect carbon emission factor; Based on the first electricity consumption value, the second electricity consumption value and the building indirect carbon emission factor, a second actual carbon emission of the electrical wire network in the building is obtained.

2. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to claim 1, characterized in that: The non-intrusive load identification device based on the pre-configuration acquires and identifies the time-sharing energy consumption, obtains the corresponding load identification waveform, and determines the corresponding start-up load based on the load identification waveform, including: Obtain all power supply endpoints in the power supply line distribution map of the building, and determine the identification node based on the building space corresponding to the power supply endpoint in the building; A corresponding non-intrusive load identification device is configured at the identification node, and a corresponding space load is determined based on the attribute of the building space corresponding to each identification node; A load identification waveform configured by a user for each spatial load is received, so that the corresponding non-intrusive load identification device performs spatial load identification processing based on the load identification waveform.

3. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to claim 2, characterized in that: The step of obtaining all power supply endpoints in the power supply line distribution diagram of the building and determining identification nodes based on the building spaces corresponding to the power supply endpoints in the building includes: Processing the building according to spatial division to obtain a plurality of building spaces, acquiring all power supply endpoints corresponding to each building space to obtain a first power supply endpoint set; Traversing all power supply endpoints in the first power supply endpoint set in sequence and extending upward in a convergent manner according to the power supply line distribution map to determine a first convergence point in the power supply line distribution map within the first power supply endpoint set; Extending downward based on the first convergence point again, if the first convergence point only completely includes all power supply endpoints in the first power supply endpoint set in the downward dimension and the number of power supply endpoints is greater than or equal to the number of established nodes, then the first convergence point is determined as an identification node; A first structure tree is constructed based on the first aggregation point and the first power supply endpoint set, wherein the parent node of the first structure tree is the first aggregation point, the child node is the first power supply endpoint set, and the grandchild node is each power supply endpoint in the first power supply endpoint set.

4. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to claim 3, characterized in that: Also includes: If the first aggregation point further includes all power supply endpoints in other first power supply endpoint sets in the downward dimension and the number of all power supply endpoints is greater than or equal to the number of established nodes, the corresponding first power supply endpoint sets are merged and the first aggregation point is used as the second aggregation point; A corresponding second structure tree is constructed, wherein the parent node of the second structure tree is the second convergence point, the child node is each first power supply endpoint set, and the grandchild node is each power supply endpoint in each first power supply endpoint set.

5. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to any one of claims 3 or 4, characterized in that: Also includes: If the first convergence point further includes all power supply endpoints in other first power supply endpoint sets in the downward dimension and the number of all power supply endpoints is less than the number of established nodes, then determine the adjacent building spaces of the building space, and each building space has a preset adjacent building space; The power supply endpoints corresponding to the current building space and the adjacent building space are extended upward in a convergent manner to determine a third convergence point in the power supply line distribution map within the first power supply endpoint set corresponding to the current building space and the adjacent building space; A corresponding third structure tree is constructed, wherein the parent node of the third structure tree is the third convergence point, the child node is each first power supply endpoint set, and the grandchild node is each power supply endpoint in each first power supply endpoint set.

6. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to claim 1, characterized in that: The method of obtaining first electric energy data of the photovoltaic storage system corresponding to the building and second electric energy data corresponding to the city power station, calculating and processing the carbon footprint of the photovoltaic storage system to obtain first carbon footprint information, and decomposing the first carbon footprint information to obtain second carbon footprint information per kilowatt-hour of electricity includes: Acquire power supply information of the photovoltaic storage system of the building in a first preset time period to obtain first power data, and acquire power supply information of the mains in the first preset time period to obtain second power data; The first carbon footprint information of the photovoltaic storage system is obtained by calculation and processing based on the production information and transportation information of the photovoltaic storage system equipment in the database, and the second carbon footprint information of the photovoltaic storage system is obtained based on the first carbon footprint information and the rated power generation of the photovoltaic storage system.

7. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to claim 1, characterized in that: The method of performing an estimation process based on the second carbon footprint information, the first electric energy data, and the second electric energy data according to a preset estimation model to obtain an indirect carbon emission factor of the building includes: The estimation model obtains a first electricity consumption value of the first electric energy data, and calculates the first carbon emission sub-information based on the first electricity consumption value and the second carbon footprint information; The estimation model obtains the second electricity consumption value of the second electric energy data and the mains carbon emission factor to calculate the second carbon emission sub-information, and obtains the sum of the estimated carbon emissions based on the first carbon emission sub-information and the second carbon emission sub-information; The sum of the first electricity consumption value and the second electricity consumption value is calculated to obtain a total electricity consumption value, and the indirect carbon emission factor of the building is obtained by comparing the total electricity consumption value with the sum of the estimated carbon emissions.

8. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to claim 1, characterized in that: Also includes: Obtain the distance of the power supply network between the photovoltaic storage system and each load to obtain the corresponding first extra power loss distance, and obtain the distance of the power supply network between the mains and each load to obtain the corresponding second extra power loss distance; Traversing the first extra energy loss distance and the second extra energy loss distance of each load in turn, and determining the minimum loss distance of each load, and counting the loads whose minimum loss distance is the first extra energy loss distance to obtain a first load set; Based on the analysis and processing of the historical load energy consumption data of each load in the first load set, adjustment data of the photovoltaic energy storage system is obtained.

9. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to claim 8, characterized in that: The analyzing and processing of the load energy consumption data of each load in the first load set to obtain the adjustment data of the photovoltaic energy storage system includes: Obtaining the historical load energy consumption data of each load in the first load set, and classifying and processing them in time sequence according to a preset length of time to obtain a corresponding load energy consumption sequence; Counting the sum of load values ​​of all loads of the same time sequence in the load energy consumption sequence, and comparing the sum of load values ​​of all time sequences to determine the maximum sum of load values; The sum of the maximum load values ​​is compared with the current rated load value of the photovoltaic storage system to obtain the adjusted load value of the photovoltaic storage system.

10. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to claim 9, characterized in that: The step of comparing the sum of the maximum load values ​​with the current rated load value of the photovoltaic storage system to obtain an adjusted load value of the photovoltaic storage system includes: If the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic storage system is less than or equal to the increase threshold, the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic storage system is calculated to obtain the adjusted load value; If the difference between the sum of the maximum load values ​​and the current rated load value of the photovoltaic energy storage system is greater than the increase threshold, the increase threshold is used as the load adjustment value.

11. A method for evaluating energy consumption and refining carbon footprint of green and low-carbon buildings according to claim 5, characterized in that: Establish a root node, and combine and connect the parent nodes of the first structure tree, the second structure tree, and the third structure tree with the root node to obtain a total structure tree; Based on the circuit lengths of the aggregation node and the PV storage system and / or the mains in the overall structure tree, a first connection length ratio of each aggregation node is obtained; Obtaining the line length from each load in each first load set to the aggregation node, and obtaining the second connection length ratio of each load; Obtain a first connection line length between a root node and a child node based on the first connection length ratio and the first reference length, obtain a second connection line length based on the second connection length ratio and the second reference length, and obtain a third connection line length by equally dividing the second connection line length; Connecting the root node to the corresponding parent node via a first connection line length, connecting the parent node to the corresponding child node based on a third connection line length, and connecting the child node to the corresponding parent node based on the third connection line length; Fill the carbon emission information of each non-intrusive load identification device into the corresponding parent node, fill the carbon emission information of each set into the child node, fill the carbon emission information of each device into the grandchild node, and divide the second actual carbon emission of the power line network equally according to the length ratio to obtain the carbon emission information of each connecting line.

Images