Method, device, equipment, medium and product for determining building power supply mode of target region under carbon constraint

Abstract

The application relates to a method and device for determining a building power supply mode of a target area under carbon constraints, equipment, a medium and a product. The method comprises the following steps: obtaining energy consumption data and building area of target buildings in a preset time period, determining preset period sub-load sequences of the target buildings; determining multiple types of building prototypes associated with the target buildings based on the preset period sub-load sequences; determining a first flexible model of a target area model based on a first parameter set and a second flexible model of the target area model based on a second parameter set for the target area model corresponding to the target area of each type of building prototype; determining carbon constraint out-clearing of the target area under each type of power supply mode based on the three models; and adjusting the building power supply mode of the target area based on the carbon constraint out-clearing under each type of power supply mode. The method can quantify the carbon emission reduction contribution of each type of flexible resource under different power supply modes, thereby providing a decision basis for building power supply mode adjustment.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a method, apparatus, equipment, medium and product for determining the power supply mode of buildings in a target area under carbon constraints. Background Technology

[0002] With the advancement of urban decarbonization, the carbon emission constraints of urban electricity consumption have gradually become a focus of attention. Traditional technologies typically respond to urban carbon constraints through building flexibility assessments. However, existing building flexibility assessments often treat building demand response as a single adjustable load, lacking a refined distinction of resource flexibility characteristics. This crude, hybrid approach leads to problems such as confusion of resource value, insufficient exploitation of adjustment potential, and low adjustment efficiency. Clearly, as building clusters expand, traditional single-load treatment methods are no longer sufficient to meet the practical needs of refined scheduling and differentiated management under carbon constraints. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining the building power supply mode in a target area under carbon constraints, which can meet the needs of refined scheduling and differentiated management under carbon constraints, in order to address the above-mentioned technical problems.

[0004] In a first aspect, this application provides a method for determining the building power supply mode in a target area under carbon constraints, including:

[0005] The energy consumption data and building area of ​​the target building during a preset time period are obtained, and a preset periodic sub-load sequence for each target building is determined based on the energy consumption data and the building area.

[0006] Based on the preset periodic sub-load sequences, determine multiple types of building prototypes associated with the target building;

[0007] For each type of building prototype corresponding to a target area model, a first flexible model is determined based on a first set of parameters, and a second flexible model is determined based on a second set of parameters.

[0008] Based on each target region model and the first flexible model and the second flexible model corresponding to each target region model, the carbon constraint clearing of the target region under various electricity consumption modes is determined; the electricity consumption modes include three electricity consumption modes corresponding to the target region model, the first flexible model and the second flexible model respectively;

[0009] Based on the carbon constraint clearing under various power consumption modes, the power supply mode of buildings in the target area is adjusted.

[0010] In one embodiment, determining a second flexible model based on a second parameter set, for each type of building prototype corresponding to a target area model, includes:

[0011] For each type of building prototype corresponding to the target area model, the first flexible model of the target area model is adjusted based on the second parameter set to obtain the corresponding second flexible model.

[0012] In one embodiment, determining the preset periodic sub-load sequence for each target building based on the energy consumption data and the building area includes:

[0013] For each target building, acquire the energy consumption data corresponding to the same time period and the same energy consumption category within each preset cycle;

[0014] The effective data in each of the energy consumption data is determined, and the effective data is normalized based on the building area of ​​the relevant target building to obtain the preset periodic sub-load sequence of the target building.

[0015] In one embodiment, determining the multiple building prototypes associated with the target building based on each of the preset periodic sub-load sequences includes:

[0016] The preset periodic total load sequence for each target building is determined based on the preset periodic sub-load sequence;

[0017] For each target building, a feature vector corresponding to the target building is obtained based on the corresponding preset period sub-load sequence, the preset period total load sequence, and energy consumption category;

[0018] The feature vectors are processed by a preset clustering algorithm to obtain multiple types of building prototypes associated with the target building.

[0019] In one embodiment, the method further includes:

[0020] Obtain the total electricity consumption of the target building within the target area during a preset period and the total electricity consumption of the building prototype during the preset period;

[0021] Based on the participation ratio of the target building group of the target building, the total electricity consumption of the target building in the target area during a preset period, and the total electricity consumption of the building prototype during the preset period, the scaling factor of the building prototype of each type of the target building is determined.

[0022] Based on the scaling factor, the building prototypes of each type are enlarged to the target area to generate a target area model.

[0023] In one embodiment, adjusting the power supply mode of buildings in the target area based on carbon constraint clearing under various power consumption modes includes:

[0024] Obtain carbon constraint clearing under each of the aforementioned electricity consumption modes;

[0025] The target carbon constraint clearing is determined from each of the aforementioned carbon constraint clearings based on preset evaluation indicators;

[0026] Based on the target carbon constraint clearance, the power supply mode of buildings in the target area is adjusted.

[0027] Secondly, this application also provides a device for determining the building power supply mode in a target area under carbon constraints, comprising:

[0028] The data acquisition module acquires the energy consumption data and building area of ​​the target building within a preset time period, and determines the preset periodic sub-load sequence of each target building based on the energy consumption data and the building area.

[0029] The building segmentation module determines multiple building prototypes associated with the target building based on the preset periodic sub-load sequences.

[0030] The model building module determines a first flexible model of the target area model based on a first set of parameters, and a second flexible model of the target area model based on a second set of parameters, for each type of building prototype corresponding to the target area model.

[0031] The model evaluation module determines the carbon constraint clearing of the target region under various electricity consumption modes based on each target region model and the first flexible model and the second flexible model corresponding to each target region model; the electricity consumption modes include three electricity consumption modes corresponding to the target region model, the first flexible model and the second flexible model respectively.

[0032] The adjustment and improvement module adjusts the power supply mode of buildings in the target area based on the carbon constraint clearing under various power consumption modes.

[0033] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method steps in the first aspect.

[0034] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method steps of the first aspect.

[0035] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method steps in the first aspect.

[0036] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining the power supply mode of buildings in the target area under carbon constraints acquire energy consumption data and building area of ​​the target buildings over a preset time period, calculate the preset periodic sub-load sequence of the target buildings, and can accurately quantify the composition and variation patterns of the sub-loads of the target buildings in different periods. Then, based on the preset periodic sub-load sequence, the target buildings are divided into multiple types of building prototypes, effectively distinguishing the differentiated energy consumption time-series characteristics of various types of buildings, and achieving refined classification and representation of building groups. Furthermore, based on the building prototypes, the target area model, the first flexible model, and the second flexible model of the target area are determined, and the carbon constraint clearing of the three models under various power consumption modes is calculated. This study evaluated the carbon reduction potential and carbon constraint compliance of different flexible resource combination strategies under various electricity consumption modes. Finally, based on the carbon constraint clearing under various electricity consumption modes, a target carbon constraint clearing was selected. Based on the parameters of the target carbon constraint clearing model, the power supply mode of buildings in the target area was adjusted. This approach can fully tap the flexible adjustment potential of buildings while meeting carbon emission control requirements, achieving the goal of coordinated control of regional electricity load peaking and carbon emission control. It optimizes the power supply operation plan of building clusters in the target area before regional building electricity carbon emissions exceed the standard and electricity supply and demand become unbalanced. This is conducive to improving the foresight of regional energy low-carbon dispatch and can also achieve the effect of fully tapping building flexible resources and reducing the cost of regional low-carbon operation and control. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is an application environment diagram of a method for determining the building power supply mode in a target area under carbon constraints in one embodiment.

[0039] Figure 2 This is a flowchart illustrating a method for determining the building power supply mode in a target area under carbon constraints in one embodiment.

[0040] Figure 3 This is a flowchart illustrating the process of determining multiple building prototypes associated with a target building in one embodiment.

[0041] Figure 4This is a flowchart illustrating the process of determining a target region model in one embodiment;

[0042] Figure 5 A structural block diagram of a device for determining the building power supply mode of a target area under carbon constraints in one embodiment;

[0043] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0046] Urban building complexes possess a large amount of adjustable demand, including air conditioning loads, thermally inertial loads, deferred energy consumption, and non-critical service loads. These flexible resources can participate in the low-carbon operation of cities, but the mechanisms by which different types of building flexibility affect system costs and carbon constraints vary. Some flexibility only changes the timing of electricity consumption, keeping the total daily energy consumption constant, making it suitable for peak shifting and transferring during low-price periods; other flexibility allows for a certain degree of service degradation or net load reduction, which can directly reduce electricity consumption and emission constraint pressures.

[0047] In related technologies, building flexibility assessments often treat building demand response as a single adjustable load, or evaluate peak-shaving benefits only from the perspective of electricity price arbitrage, rarely distinguishing between "energy-neutral peak-shaving flexibility" and "limited net load reduction flexibility" in carbon-constrained city operation models. This leads to confusion regarding the value of the two types of resources, specifically: peak-shaving flexibility may reduce operating costs but not necessarily reduce endogenous carbon prices; while reduction flexibility may directly reduce marginal compliance costs when carbon constraints are tight.

[0048] The applicant's research revealed that while the building load clustering and typical daily / weekly modeling techniques employed in related technologies can extract representative load patterns from large amounts of building electricity consumption data, they are typically only used for load forecasting or simulation input, without further distinguishing the system value of different flexibility types. The building demand response peak-shifting scheduling technology used in related technologies achieves load transfer through air conditioning temperature settings, cold storage, or deferred loads, but often employs daily or periodic energy conservation models, primarily reflecting the economic value of peak shifting, and failing to characterize direct emission reduction and carbon price mitigation effects. The low-carbon demand response or load reduction technologies used in related technologies allow for partial load reduction to lower emissions, but often treat reduction and peak shifting together, without providing independent calculation processes for the costs, constraints, and values ​​of different types of flexibility. The city-level carbon clearing technology used in related technologies can calculate system costs and carbon prices, but typically treats building demand as total load or a single flexibility variable, failing to identify the differentiated impact of peak-shifting and reduction-type building flexibility on carbon constraints.

[0049] In summary, the applicant believes that the relevant technology has at least the following technical problems:

[0050] 1) The mixed types of flexible building resources make their value unclear. Energy neutrality shifting and net load reduction have different effects on system operation. If they are uniformly modeled as a single adjustable load, it is impossible to determine whether the cost reduction comes from time shifting or total load reduction.

[0051] 2) Building data is large in scale and highly heterogeneous, making direct modeling and calculation complex. The city has a large number of buildings with varying areas and energy consumption structures, requiring the compression of real-year hourly data into a representative prototype suitable for optimization, while preserving the shape of the total load and the adjustable air conditioning envelope as much as possible.

[0052] 3) The proportion of building participation lacks feasible scaling constraints. If the sample buildings are scaled up to the city scale at an arbitrary ratio, it may result in negative non-building loads at certain times, thus creating an unrealistic urban load structure.

[0053] 4) Peak-shifting flexibility lacks strict daily energy neutrality and ramping constraints. Without daily energy neutrality constraints, peak shifting may be mistakenly turned into reduction, and without ramping constraints, abrupt adjustments may occur that do not conform to the actual building control.

[0054] 5) Reduced flexibility lacks independent unsuitable costs and caps. Without setting separate net reduction variables and reduction costs, it is impossible to assess the marginal value of limited service degradation under carbon constraints.

[0055] 6) The experimental evaluation indicators are not sufficiently differentiated. Existing methods usually only compare total cost or total emissions, lacking a comprehensive assessment of carbon price changes, weekly savings, peak-shifting electricity, net reduction in electricity, and prototype reconfiguration error.

[0056] This application provides a method for determining the building power supply mode in a target area under carbon constraints, which can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. For example, the data storage system of server 104 can store energy consumption data and building area of ​​each target building within a preset time period. Server 104 can obtain the energy consumption data and building area of ​​each target building within the preset time period, and perform fusion calculations on the energy consumption data and building area to obtain a preset periodic sub-load sequence for the target buildings. Based on the preset periodic sub-load sequence, it can determine multiple types of building prototypes for the target buildings. Furthermore, server 104 can determine a first flexible model based on a first parameter set and a second flexible model based on a second parameter set by corresponding the building prototypes to the target area model of the target area. Finally, server 104 can determine the carbon constraint clearing of the target area under various electricity consumption modes based on the target area model, the first flexible model, and the second flexible model, and adjust the power supply mode of the buildings in the target area based on the carbon constraint clearing under various electricity consumption modes. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, and low-altitude aircraft. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0057] In one exemplary embodiment, such as Figure 2 As shown, a method for determining the building power supply mode in a target area under carbon constraints is provided, which is then applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S201 to S205. Among them:

[0058] S201: Obtain the energy consumption data and building area of ​​the target building within a preset time period, and determine the preset periodic sub-load sequence of each target building based on the energy consumption data and building area;

[0059] The target buildings refer to all buildings within the target area whose loads are to be calculated. Target buildings include, but are not limited to, one or more of the following: office buildings, commercial buildings, hotel buildings, residential buildings, and educational buildings. There are multiple target buildings, each with its own building area and energy consumption data. Energy consumption data refers to the power output of the target buildings within a preset time period and energy category.

[0060] The preset cycle load sequence refers to the load data sequence formed by breaking down the total electrical load of the target building into at least one electrical item, and then arranging them sequentially at preset time intervals using a preset cycle as the time window. The electrical items include at least one or more of the following types: lighting and socket items, air conditioning items, power items, and special electrical items; the preset cycle is determined according to the energy consumption pattern of the target building, including but not limited to a week, a day, or a weekday / non-weekday cycle.

[0061] For example, the server processes the obtained energy consumption data and building area of ​​the target building within a preset time period to obtain a preset periodic sub-load sequence of the relevant target building, that is, a load data sequence of the relevant target buildings arranged in sequence according to a preset time interval.

[0062] S202: Based on the preset periodic sub-item load sequence, determine the multi-type building prototypes associated with the target building;

[0063] Among them, multi-type building prototypes refer to at least two building categories with different electricity consumption characteristics obtained after cluster analysis of the preset periodic load sequences of at least two target buildings. The similarity of the electricity consumption characteristics of multiple target buildings corresponding to each type of building prototype is greater than or equal to the corresponding preset similarity.

[0064] For example, the server categorizes the load patterns of the target building in each preset period according to the load sequence of each preset period, and determines the multi-type building prototypes associated with the target building based on the categorization results.

[0065] S203: For each type of building prototype corresponding to the target area, determine the first flexible model of the target area model based on the first parameter set, and determine the second flexible model of the target area model based on the second parameter set.

[0066] The target area refers to the city or city-level administrative division to be assessed for load, including but not limited to prefecture-level cities, municipal districts, county-level cities, or urban power supply areas designated by the power grid dispatching authority. The target area contains multiple target buildings, each with its own building area, building function type, and energy consumption data.

[0067] The target area model refers to a city-level building load data model generated by scaling down at least one type of building prototype obtained through clustering, based on the proportional relationship between the building area of ​​the corresponding building prototype and the total area of ​​buildings within the target area, and in conjunction with a preset target building participation ratio. The target area model receives the preset periodic total load sequence and component load sequence of each type of building prototype and outputs the total load sequence and component load sequence of the target area at each time point within the preset period.

[0068] The first flexible model refers to the control model constructed by introducing the first parameter set on the basis of the target area model to describe the peak-shifting flexible load. The peak-shifting flexible load refers to the load type whose electricity consumption time can be adjusted before and after a preset time, but the total electricity consumption remains unchanged. It can also be understood that by updating the target area model with the first parameter set, the corresponding target area model can be obtained.

[0069] The second flexible model refers to the control model constructed by introducing a second set of parameters on the basis of the target area model to describe the reduction-type flexible load. The reduction-type flexible load refers to the type of load whose power consumption can be directly reduced within a preset range, but the total power consumption after the reduction is reduced accordingly, and the reduction process is subject to the lower limit of equipment operation and the number of months of user comfort.

[0070] The first parameter set describes the time-based shifting capability of peak-shaving flexible loads. Parameters in the first parameter set may include, for example, upward adjustment variables, downward adjustment variables, net adjustment variables, adjustment boundaries, ramping constraints, daily energy neutrality constraints, and peak-shaving costs. The first flexible model uses daily energy neutrality as its core constraint. The second parameter set describes the power-based reduction capability of reduction flexible loads. Parameters in the second parameter set may include, for example, upward adjustment variables, downward adjustment variables, net adjustment variables, adjustment boundaries, ramping constraints, daily net reduction variables, maximum net reduction ratio, reduction inadequacy costs, and constraints on finite daily net reductions.

[0071] For example, the server updates the target area model by introducing a first set of parameters based on the correspondence between various types of building prototypes and the target area model, forming a first flexible model; and updates the target area model by introducing a second set of parameters, forming a second flexible model.

[0072] S204: Based on each target region model and the first and second flexible models corresponding to each target region model, determine the carbon constraint clearing of the target region under various electricity consumption modes; the electricity consumption modes include the three electricity consumption modes corresponding to the target region model, the first flexible model, and the second flexible model respectively.

[0073] Carbon-constrained clearing refers to the process of determining the optimal output scheme of each flexible resource through optimization calculations under given carbon emission quotas or carbon emission intensity limits, so that the system can achieve a certain goal while meeting carbon emission restrictions.

[0074] For example, the server determines the operating conditions of the target area under three power consumption modes based on the target area model and the first flexible model and the second flexible model corresponding to each target area model, and calculates and determines the carbon constraint clearing results under each power consumption mode according to the power consumption curve and the corresponding carbon emission factor under each operating condition.

[0075] S205: Based on carbon constraint clearance under various power consumption modes, adjust the power supply mode of buildings in the target area.

[0076] For example, the server determines a target power consumption mode that is more suitable for the target area based on the carbon constraint clearance results under various power consumption modes, and adjusts the power supply mode of the buildings in the target area according to the target power consumption mode, so that the buildings in the target area can operate in accordance with the adjustment parameters associated with the target carbon constraint clearance corresponding to the target power consumption mode.

[0077] In the aforementioned method for determining the power supply mode of buildings in the target area under carbon constraints, by acquiring the energy consumption data and building area of ​​the target buildings within a preset time period, the method calculates the preset periodic load sequence of the target buildings, which can accurately quantify the composition and variation patterns of the loads of the target buildings in different periods. Then, based on the preset periodic load sequence, the target buildings are clustered into multiple types of building prototypes, effectively distinguishing the differentiated energy consumption time-series characteristics of various types of buildings and achieving refined classification and representation of building groups. Furthermore, based on the building prototypes, the method determines the target area model, the first flexible model, and the second flexible model, and calculates the carbon constraint clearing of the three models under various power consumption modes, simultaneously taking into account the regional base load characteristics and the potential for differentiated flexible resource adjustment, achieving multi-subject, multi-scenario collaborative optimization and evaluation of electricity and carbon. Finally, by acquiring... This method involves carbon constraint clearing under various electricity consumption modes and selecting target carbon constraint clearing based on preset evaluation indicators. Based on the target electricity consumption mode corresponding to the target carbon constraint clearing, the power supply mode of buildings in the target area is adjusted. This fully leverages the flexible adjustment potential of buildings while meeting carbon emission control requirements, synergistically balancing regional electricity supply and demand, and achieving the goal of coordinated control of regional electricity load peak shaving and carbon emission control. The method provided in this application optimizes and assesses the power supply operation scheme of building clusters in the target area before regional building electricity carbon emissions exceed standards and electricity supply and demand become unbalanced. This fundamentally changes urban building energy management from simple load control to coordinated optimization of electricity and carbon emissions, improving the foresight of regional low-carbon energy dispatch and achieving the effect of fully leveraging building flexible resources and reducing regional low-carbon operation and management costs.

[0078] In an exemplary embodiment, the process of S203 above, which involves determining a second flexible model of the target area model based on a second parameter set for each type of building prototype corresponding to the target area, includes: adjusting a first flexible model of the target area model based on a second parameter set for each type of building prototype corresponding to the target area, thereby obtaining a corresponding second flexible model.

[0079] For example, after the server obtains the target area model, it introduces a first parameter set based on the target area model, and constructs a peak-shifting flexible load control model based on the first parameter set, forming a first flexible model. The first parameter set may include, for example, an upward adjustment variable, a downward adjustment variable, a net adjustment variable determined by the upward and downward adjustment variables, ramp-up constraints, daily energy neutrality constraints, and a peak-shifting cost function.

[0080] Specifically, in setting the upward adjustment variable Adjusting variables ≥0, the resulting net adjustment variable is = , with scaled-down, segmented adjustable load prototype Based on the benchmark, and according to the upward adjustment ratio and the reduction ratio The flexible adjustment envelope is constructed using the following formula:

[0081] ,

[0082] The above-mentioned climbing constraints can be determined based on the following formula:

[0083] ,

[0084] In settings This represents the daily index within the week. For the first Based on the set of hours included in a day, the above applies to each day. The applied diurnal energy neutrality constraint can be determined based on the following formula:

[0085] ;

[0086] The above peak shifting costs The determination of can be based on the following formula:

[0087]

[0088] The server, based on the first flexible model, introduces a second parameter set to adjust the first flexible model; it then adjusts and updates the first flexible model using parameters from the second parameter set, which are included outside the first parameter set, to form the corresponding second flexible model. The second parameter set includes, in addition to the upward adjustment variables, downward adjustment variables, net adjustment variables, adjustment boundaries, and ramp-up constraints already included in the first parameter set, net demand reduction constraints, reduction amount constraints, and reduction-related costs.

[0089] Specifically, in setting the first Architectural prototypes and representatives of the week sky, For this type of building in the Net load reduction per day, in MWh; For the first Based on the set of hours contained in the day, the daily energy neutrality constraint is replaced with:

[0090]

[0091] when This indicates that the downstream electricity consumption on that day is greater than the upstream electricity consumption. This results in a net reduction in demand.

[0092] In settings For the first The maximum daily net reduction ratio of the building prototype, For the first part after scaling architectural prototypes in time period Based on the baseline adjustable load prototype, the above reduction constraints can be achieved using the following formula:

[0093]

[0094] In settings For the first Based on the service or comfort loss cost corresponding to the net reduction in electricity consumption per unit of the building prototype, expressed in RMB / MWh; the above-mentioned cost reduction in discomfort can be achieved using the following formula:

[0095]

[0096] in, Index for architectural prototypes. For daily index, Indexed by hour. For the first The day's hourly collection. and Corresponding to different discomfort channels, the former measures time-shifting effort, while the latter measures net service reduction, and therefore do not constitute duplicate measurement.

[0097] In this embodiment, the server can adjust the first flexible model corresponding to the target area model through the second parameter set to obtain the corresponding second flexible model. Based on the target area model, the first flexible model and the second flexible model corresponding to the target area, it is possible to realize a multi-scenario dynamic description of the flexible adjustment capability of the building group. This fundamentally changes the flexible resource modeling method from a fixed single model to a parameter-adjustable model, which is conducive to improving the scenario adaptability of regional energy simulation and can also improve the reliability of demand response and carbon constraint clearing calculations.

[0098] It should be noted that, in the case of "adjusting the target region model based on the second parameter set to obtain the corresponding second flexible model," the second parameter set specifically includes, for example, the following: upward adjustment variables, downward adjustment variables, net adjustment variables, adjustment boundaries, ramp-up constraints, net demand reduction constraints, reduction amount constraints, and reduction-adaptive cost functions. In the case of "adjusting the first flexible model based on the second parameter set to obtain the corresponding second flexible model," the second parameter set specifically includes: replacing the daily energy neutrality constraint in the first parameter set with a net demand reduction constraint, as well as reduction amount constraints and reduction-adaptive cost functions.

[0099] In an exemplary embodiment, the process of determining the preset periodic sub-load sequence of each target building based on energy consumption data and building area in S201 includes: for each target building, acquiring energy consumption data corresponding to the same time period and the same energy consumption category within each preset period; determining the valid data in each energy consumption data, and normalizing multiple valid data based on the building area of ​​the relevant target buildings to obtain the preset periodic sub-load sequence of the target buildings.

[0100] For example, after the server obtains the energy consumption data and building area of ​​each target building in a preset time period, it obtains the energy consumption data of each target building in the same time period and the same energy consumption category in each preset cycle, normalizes the retained buildings by area, and averages the normalized energy consumption data to obtain the preset cycle sub-load sequence.

[0101] Specifically, in the settings For architecture In the Zhou, Di Hours, energy consumption category power, For building area, For architecture The valid weekly set retained after data preprocessing; This indicates summation and traversal of buildings. All valid weeks, Based on the number of effective weeks. After area normalization, the building is in the [number]th week. Hours, energy consumption category The component load sequences are calculated as follows:

[0102] ,

[0103] in, =1,2, ,168 indicates that the preset period is a typical week. Through the above processing, the preset period sub-load sequence of the target building is obtained.

[0104] In this embodiment, for each target building, energy consumption data corresponding to the same time period and the same energy category within each preset cycle is obtained. The building area of ​​the target building is normalized by the obtained energy consumption data to obtain the preset cycle sub-load sequence of the target building. This realizes the horizontal comparability of energy consumption characteristics of buildings of different sizes, accurately restores the real electricity consumption time sequence characteristics of various loads, eliminates the load value gap caused by the difference in building size, and intuitively and objectively represents the time sequence change law of various energy consumption per unit area.

[0105] In one exemplary embodiment, such as Figure 3 As shown, the process of determining multiple building prototypes associated with the target building based on the preset periodic sub-item load sequence, performed in S202 above, may include S301-S303, wherein:

[0106] S301: Determine the preset period total load sequence for each target building based on the preset period sub-load sequence;

[0107] Among them, the preset cycle total load sequence refers to the time sequence data of the overall power load of the building at each moment, arranged in chronological order according to a preset cycle as the time range, which is obtained by summing the sub-items load at each moment within the corresponding cycle.

[0108] For example, the server adds up the sub-load values ​​of all energy consumption categories of the building at the same time to obtain the total load at that time; it then iterates through all preset period sub-load sequences at all times, arranges them in chronological order, and generates a preset period total load sequence.

[0109] S302: For each target building, based on the corresponding preset period sub-item load sequence, preset period total load sequence and energy consumption category, obtain the feature vector corresponding to the target building;

[0110] For example, the server concatenates the total load sequence of the target building after area normalization with the sub-load sequence of the target building according to time and energy consumption category to obtain a one-dimensional ordered numerical group. This one-dimensional numerical group is the feature vector corresponding to the target building, which is used to characterize the time-series energy consumption characteristics of the target building.

[0111] S303: Based on a preset clustering algorithm, feature vectors are processed to obtain multi-type building prototypes associated with the target building.

[0112] For example, after standardizing the feature vector, the server uses a clustering algorithm to cluster the building's energy consumption features. The goal is to iteratively divide the data by minimizing the squared distance between samples within a cluster and the cluster center. The clustering algorithm used may include K-Means. Multiple cluster numbers are set, and the profile coefficient and prototype curve corresponding to each cluster number are calculated. The cluster number K is determined based on the criteria of high profile coefficient and clear physical meaning of prototype curve, thereby dividing the building into multiple types of target building prototypes.

[0113] In this embodiment, a preset period total load sequence is obtained by accumulating the preset period sub-load sequences at each time point. The preset period total load sequence and sub-load sequences are then subjected to area normalization and time-series splicing to obtain the feature vector corresponding to the target building. Based on the feature vector, the building is clustered and divided, which realizes the accurate extraction and refined classification of the time-series energy consumption characteristics of different buildings and effectively distinguishes the differences in the electricity load patterns of various types of buildings.

[0114] In one exemplary embodiment, such as Figure 4 As shown, the method for determining the building power supply mode in a target area under carbon constraints provided in this application embodiment further includes S401-S403, wherein:

[0115] S401: Obtain the total electricity consumption of the target building within the target area during a preset period and the total electricity consumption of the building prototype during the preset period.

[0116] S402: Based on the participation ratio of the target building group, the total electricity consumption of the target building in the target area during the preset period, and the total electricity consumption of the building prototype during the preset period, determine the scaling factor of each type of building prototype of the target building.

[0117] Among them, the participation rate of the target building complex This refers to the proportion of the building load represented by the prototype building to the target building's total electricity consumption within a preset period. The effective range for the participation proportion of the target building group is 0 ≤ ≤ ;in, The maximum feasible participation ratio is calculated using the hourly load of the target building, the hourly load of the building prototype, the total electricity consumption of the target building, and the total electricity consumption of the building prototype. It is used to ensure that the residual non-building load is not negative at any time after scaling.

[0118] Specifically, the maximum feasible participation ratio can be achieved based on the following formula:

[0119]

[0120] in, This represents the hourly load of the target building in the city at time t. This indicates the total electricity consumption of the target building within a preset period. This indicates the total electricity consumption of the building prototype within a preset period. This represents the sum of the hourly loads of various building prototypes at time t.

[0121] Wherein, scaling factor The scaling factor represents the scaling ratio of the target building's electricity consumption relative to the building's prototype electricity consumption. The formula for calculating the scaling factor is:

[0122]

[0123] Specifically, the total baseline load and adjustable load of the k-th type of building at the urban scale can be realized based on the following formula:

[0124] ,

[0125] in, For the total load prototype, This is a prototype of a sub-item adjustable load.

[0126] S403: Based on the scaling factor, enlarge the prototypes of various types of buildings to the target area to generate a model of the target area.

[0127] For example, the server calculates the scaling factor for each type of building prototype by statistically analyzing the total electricity consumption of the target building within the target area and the total electricity consumption of the building prototype within the preset period, inputting the participation ratio of the target building group and calculating the scaling factor according to the scaling factor calculation formula. The server then multiplies the total load sequence and sub-load sequence of each type of building prototype within the preset period by the corresponding scaling factor to obtain the amplified total load sequence and amplified sub-load sequence of each type of building within the target area within the preset period. The server then superimposes the amplified total load sequences of each type of building to generate the total load distribution of the target area model within the preset period, i.e., the target area model.

[0128] In this embodiment, by obtaining the total electricity consumption of the target building in the target area during a preset period and the total electricity consumption of the building prototype during the preset period, the scaling factor of the building prototype is calculated based on the total electricity consumption, and the load sequence of the building prototype is magnified to the target area level, thus realizing the construction of the target area model. At the same time, the load time sequence characteristics and sub-load structure of the building prototype are preserved, reflecting the total load level and flexible load composition of the target area at each moment within the preset time, providing a data basis for subsequent carbon constraint clearing.

[0129] In an exemplary embodiment, the method for determining the power supply mode of buildings in a target area under carbon constraints provided in this application further includes: obtaining carbon constraint clearing under various power consumption modes; determining the target carbon constraint clearing from each carbon constraint clearing based on preset evaluation indicators; and adjusting the power supply mode of buildings in the target area based on the target carbon constraint clearing.

[0130] Among them, the preset evaluation index is a quantitative standard used to compare the clearing results of the first flexible model and the second flexible model. By comparing the indexes of the first flexible model and the second flexible model, the system value of the two flexible models under carbon constraints is evaluated. The preset evaluation index includes at least one of the following: weekly cost savings, carbon price change, peak shifting power, carbon price reduction, system savings improvement and net power reduction.

[0131] In this process, after obtaining the carbon constraint clearing results from the first and second flexible models, the clearing results that meet the requirements are selected based on the evaluation indicators. The power supply mode corresponding to the obtained results is the final implementation plan.

[0132] For example, the server obtains the carbon constraint clearing results of the target area model, the first flexible model, and the second flexible model. It uses the system cost, carbon price, and carbon emissions in the target area model results as a comparison benchmark. The server compares the carbon constraint clearing results of the first flexible model and the second flexible model with the benchmark values ​​and calculates the changes in each evaluation index. The server selects the target carbon constraint clearing based on the preset evaluation indexes and generates a power supply mode adjustment scheme based on the adjustment parameters output by the flexible model corresponding to the target carbon constraint clearing. The server then adjusts the power supply mode of the buildings in the target area.

[0133] In this embodiment, by acquiring the carbon constraint clearing results of three models—the target area model, the first flexible model, and the second flexible model—and using the clearing result of the target area model as a benchmark, a target carbon constraint clearing is selected from the carbon constraint clearing results of the first flexible model and the second flexible model using preset evaluation indicators. Based on the adjustment parameters output by the flexible model corresponding to the target carbon constraint clearing, a power supply mode adjustment scheme is formed. This realizes the adjustment of the power supply mode of buildings in the target area based on the target carbon constraint clearing, so that the flexible adjustment potential of buildings can be fully explored under the premise of meeting carbon emission control requirements, and the goal of coordinated control of regional electricity load peak shaving and carbon emission can be achieved.

[0134] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0135] Based on the same inventive concept, this application also provides a device for determining the building power supply mode under carbon constraints in a target area, used to implement the method for determining the building power supply mode under carbon constraints described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for determining the building power supply mode under carbon constraints provided below can be found in the limitations of the method for determining the building power supply mode under carbon constraints described above, and will not be repeated here.

[0136] In one exemplary embodiment, such as Figure 5 As shown, a device for determining the building power supply mode in a target area under carbon constraints is provided, comprising: a data acquisition module 501, a building segmentation module 502, a model construction module 503, a model evaluation module 504, and an adjustment and improvement module 505, wherein:

[0137] The data acquisition module 501 is used to acquire the energy consumption data and building area of ​​the target building during a preset time period, and to determine the preset periodic sub-load sequence of each target building based on the energy consumption data and building area.

[0138] Building segmentation module 502 is used to determine multiple types of building prototypes associated with the target building based on each preset periodic sub-load sequence.

[0139] The model building module 503 is used to determine a first flexible model of the target area model based on a first set of parameters and a second flexible model of the target area model based on a second set of parameters for the target area model corresponding to the target area of ​​each type of building prototype.

[0140] The model evaluation module 504 is used to determine the carbon constraint clearing of the target area under various electricity consumption modes based on the target area model and the first flexible model and the second flexible model corresponding to each target area model. The electricity consumption modes include three electricity consumption modes corresponding to the target area model, the first flexible model and the second flexible model respectively.

[0141] The adjustment and improvement module 505 is used to adjust the power supply mode of buildings in the target area based on carbon constraint clearance under various power consumption modes.

[0142] In an exemplary embodiment, the model building module 503 is further configured to: adjust the first flexible model of the target area model based on the second parameter set for the target area model corresponding to the target area of ​​each type of building prototype, thereby obtaining the corresponding second flexible model.

[0143] In an exemplary embodiment, the data acquisition module 501 is further configured to: acquire energy consumption data corresponding to the same time period and the same energy consumption category within each preset cycle for each target building; determine the valid data in each energy consumption data, and normalize the multiple valid data based on the building area of ​​the relevant target building to obtain the preset cycle sub-load sequence of the target building.

[0144] In an exemplary embodiment, the building segmentation module 502 is further configured to: determine the preset periodic total load sequence of each target building based on the preset periodic sub-load sequence; for each target building, obtain the feature vector corresponding to the target building based on the corresponding preset periodic sub-load sequence, preset periodic total load sequence and energy consumption category; and process the feature vector based on a preset clustering algorithm to obtain multi-type building prototypes associated with the target buildings.

[0145] In an exemplary embodiment, the above-described method for determining the power supply mode of buildings in a target area under carbon constraints is further used to: obtain the total power consumption of the target building in the target area during a preset period and the total power consumption of the building prototypes during the preset period; determine the scaling factor of each type of building prototype of the target building based on the participation ratio of the target building group, the total power consumption of the target building in the target area during the preset period, and the total power consumption of the building prototypes during the preset period; and scale up each type of building prototype to the target area based on the scaling factor to generate a target area model.

[0146] In an exemplary embodiment, the above-described method for determining the power supply mode of buildings in a target area under carbon constraints is further used to: obtain carbon constraint clearing under various power consumption modes; determine the target carbon constraint clearing from each carbon constraint clearing based on preset evaluation indicators; and adjust the power supply mode of buildings in the target area based on the target carbon constraint clearing.

[0147] The modules in the aforementioned carbon-constrained target area building power supply mode determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0148] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data such as energy consumption, building area, and carbon clearance of the target building over a preset time period. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining the power supply mode of buildings in a target area under carbon constraints.

[0149] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0150] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: acquiring energy consumption data and building area of ​​target buildings over a preset time period, and determining a preset periodic sub-load sequence for each target building based on the energy consumption data and building area; determining multiple types of building prototypes associated with the target buildings based on each preset periodic sub-load sequence; determining a first flexible model of the target area model adjusted based on a first parameter set and a second flexible model of the target area model adjusted based on a second parameter set for each type of building prototype corresponding to the target area; determining carbon constraint clearing of the target area under various electricity consumption modes based on each target area model and the first and second flexible models corresponding to each target area model; the electricity consumption modes include three electricity consumption modes corresponding to the target area model, the first flexible model, and the second flexible model, respectively; and adjusting the power supply mode of the buildings in the target area based on the carbon constraint clearing under various electricity consumption modes.

[0151] In one embodiment, when the processor executes the computer program, it further performs the following steps: for each type of building prototype corresponding to the target area model of the target area, adjusts the first flexible model of the target area model based on the second parameter set to obtain the corresponding second flexible model.

[0152] In one embodiment, when the processor executes the computer program, it further performs the following steps: for each target building, acquire energy consumption data corresponding to the same time period and the same energy consumption category within each preset period; determine the valid data in each energy consumption data, and normalize the multiple valid data based on the building area of ​​the relevant target building to obtain the preset period sub-load sequence of the target building.

[0153] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the preset periodic total load sequence of each target building based on the preset periodic sub-load sequence; for each target building, obtaining the feature vector corresponding to the target building based on the corresponding preset periodic sub-load sequence, preset periodic total load sequence and energy consumption category; and processing the feature vector based on a preset clustering algorithm to obtain multi-type building prototypes associated with the target buildings.

[0154] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining the total electricity consumption of the target building in the target area during a preset period and the total electricity consumption of the building prototype in the preset period; determining the scaling factor of each type of building prototype of the target building based on the participation ratio of the target building group, the total electricity consumption of the target building in the target area during the preset period, and the total electricity consumption of the building prototype in the preset period; and scaling up each type of building prototype to the target area based on the scaling factor to generate a target area model.

[0155] In one embodiment, when the processor executes the computer program, it further performs the following steps: adjusting the power supply mode of buildings in the target area based on carbon constraint clearing under various power consumption modes, including: obtaining carbon constraint clearing under various power consumption modes; determining the target carbon constraint clearing from each carbon constraint clearing based on preset evaluation indicators; and adjusting the power supply mode of buildings in the target area based on the target carbon constraint clearing.

[0156] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it performs the following steps: acquiring energy consumption data and building area of ​​target buildings over a preset time period, and determining a preset periodic sub-load sequence for each target building based on the energy consumption data and building area; determining multiple types of building prototypes associated with the target buildings based on each preset periodic sub-load sequence; determining a first flexible model of the target area model adjusted based on a first parameter set and a second flexible model of the target area model adjusted based on a second parameter set for each type of building prototype corresponding to the target area; determining carbon constraint clearing of the target area under various electricity consumption modes based on each target area model and the first and second flexible models corresponding to each target area model; the electricity consumption modes include three electricity consumption modes corresponding to the target area model, the first flexible model, and the second flexible model, respectively; and adjusting the power supply mode of the buildings in the target area based on the carbon constraint clearing under various electricity consumption modes.

[0157] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: for each type of building prototype corresponding to the target area model of the target area, adjusts the first flexible model of the target area model based on the second parameter set to obtain the corresponding second flexible model.

[0158] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: for each target building, acquire energy consumption data corresponding to the same time period and the same energy consumption category within each preset period; determine the valid data in each energy consumption data, and normalize the multiple valid data based on the building area of ​​the relevant target building to obtain the preset period sub-load sequence of the target building.

[0159] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the preset periodic total load sequence of each target building based on the preset periodic sub-load sequence; for each target building, obtaining the feature vector corresponding to the target building based on the corresponding preset periodic sub-load sequence, preset periodic total load sequence and energy consumption category; and processing the feature vector based on a preset clustering algorithm to obtain multi-type building prototypes associated with the target buildings.

[0160] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining the total electricity consumption of the target building in the target area during a preset period and the total electricity consumption of the building prototype in the preset period; determining the scaling factor of each type of building prototype of the target building based on the participation ratio of the target building group, the total electricity consumption of the target building in the target area during the preset period, and the total electricity consumption of the building prototype in the preset period; and scaling up each type of building prototype to the target area based on the scaling factor to generate a target area model.

[0161] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: adjusting the power supply mode of buildings in the target area based on carbon constraint clearing under various power consumption modes, including: obtaining carbon constraint clearing under various power consumption modes; determining the target carbon constraint clearing from each carbon constraint clearing based on preset evaluation indicators; and adjusting the power supply mode of buildings in the target area based on the target carbon constraint clearing.

[0162] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps: acquiring energy consumption data and building area of ​​target buildings over a preset time period, and determining a preset periodic sub-load sequence for each target building based on the energy consumption data and building area; determining multiple types of building prototypes associated with the target buildings based on each preset periodic sub-load sequence; determining a first flexible model of the target area model adjusted based on a first parameter set and a second flexible model of the target area model adjusted based on a second parameter set for each type of building prototype corresponding to the target area; determining carbon constraint clearing of the target area under various electricity consumption modes based on each target area model and the first and second flexible models corresponding to each target area model; the electricity consumption modes include three electricity consumption modes corresponding to the target area model, the first flexible model, and the second flexible model, respectively; and adjusting the power supply mode of the buildings in the target area based on the carbon constraint clearing under various electricity consumption modes.

[0163] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: for each type of building prototype corresponding to the target area model of the target area, adjusts the first flexible model of the target area model based on the second parameter set to obtain the corresponding second flexible model.

[0164] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: for each target building, acquire energy consumption data corresponding to the same time period and the same energy consumption category within each preset period; determine the valid data in each energy consumption data, and normalize the multiple valid data based on the building area of ​​the relevant target building to obtain the preset period sub-load sequence of the target building.

[0165] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the preset periodic total load sequence of each target building based on the preset periodic sub-load sequence; for each target building, obtaining the feature vector corresponding to the target building based on the corresponding preset periodic sub-load sequence, preset periodic total load sequence and energy consumption category; and processing the feature vector based on a preset clustering algorithm to obtain multi-type building prototypes associated with the target buildings.

[0166] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining the total electricity consumption of the target building in the target area during a preset period and the total electricity consumption of the building prototype in the preset period; determining the scaling factor of each type of building prototype of the target building based on the participation ratio of the target building group, the total electricity consumption of the target building in the target area during the preset period, and the total electricity consumption of the building prototype in the preset period; and scaling up each type of building prototype to the target area based on the scaling factor to generate a target area model.

[0167] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: adjusting the power supply mode of buildings in the target area based on carbon constraint clearing under various power consumption modes, including: obtaining carbon constraint clearing under various power consumption modes; determining the target carbon constraint clearing from each carbon constraint clearing based on preset evaluation indicators; and adjusting the power supply mode of buildings in the target area based on the target carbon constraint clearing.

[0168] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0169] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0170] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0171] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the building power supply mode in a target area under carbon constraints, characterized in that, The method includes: The energy consumption data and building area of ​​the target building during a preset time period are obtained, and a preset periodic sub-load sequence for each target building is determined based on the energy consumption data and the building area. Based on the preset periodic sub-load sequences, determine multiple types of building prototypes associated with the target building; For each type of building prototype corresponding to a target area model, a first flexible model is determined based on a first set of parameters, and a second flexible model is determined based on a second set of parameters. Based on each target region model and the first flexible model and the second flexible model corresponding to each target region model, the carbon constraint clearing of the target region under various electricity consumption modes is determined; the electricity consumption modes include three electricity consumption modes corresponding to the target region model, the first flexible model and the second flexible model respectively; Based on the carbon constraint clearing under various power consumption modes, the power supply mode of buildings in the target area is adjusted.

2. The method according to claim 1, characterized in that, The determination of a second flexible model based on a second parameter set, for each type of building prototype corresponding to a target area model, includes: For each type of building prototype corresponding to the target area model, the first flexible model of the target area model is adjusted based on the second parameter set to obtain the corresponding second flexible model.

3. The method according to claim 1, characterized in that, The step of determining the preset periodic sub-load sequence for each target building based on the energy consumption data and the building area includes: For each target building, acquire the energy consumption data corresponding to the same time period and the same energy consumption category within each preset cycle; The effective data in each of the energy consumption data is determined, and the effective data is normalized based on the building area of ​​the relevant target building to obtain the preset periodic sub-load sequence of the target building.

4. The method according to claim 1, characterized in that, The step of determining multiple building prototypes associated with the target building based on each of the preset periodic sub-load sequences includes: The preset periodic total load sequence for each target building is determined based on the preset periodic sub-load sequence; For each target building, a feature vector corresponding to the target building is obtained based on the corresponding preset period sub-load sequence, the preset period total load sequence, and energy consumption category; The feature vectors are processed by a preset clustering algorithm to obtain multiple types of building prototypes associated with the target building.

5. The method according to claim 1, characterized in that, The method further includes: Obtain the total electricity consumption of the target building within the target area during a preset period and the total electricity consumption of the building prototype during the preset period; Based on the participation ratio of the target building group of the target building, the total electricity consumption of the target building in the target area during a preset period, and the total electricity consumption of the building prototype during the preset period, the scaling factor of the building prototype of each type of the target building is determined. Based on the scaling factor, the building prototypes of each type are enlarged to the target area to generate a target area model.

6. The method according to any one of claims 1 to 5, characterized in that, The adjustment of the power supply mode for buildings in the target area based on carbon constraint clearing under various power consumption modes includes: Obtain carbon constraint clearing under each of the aforementioned electricity consumption modes; The target carbon constraint clearing is determined from each of the aforementioned carbon constraint clearings based on preset evaluation indicators; Based on the target carbon constraint clearance, the power supply mode of buildings in the target area is adjusted.

7. A device for determining the building power supply mode in a target area under carbon constraints, characterized in that, The device includes: The data acquisition module is used to acquire the energy consumption data and building area of ​​the target building during a preset time period, and to determine the preset periodic sub-load sequence of each target building based on the energy consumption data and the building area. The building segmentation module is used to determine multiple types of building prototypes associated with the target building based on the preset periodic sub-load sequences. The model building module is used to determine a first flexible model of the target area model based on a first set of parameters and a second flexible model of the target area model based on a second set of parameters for each type of building prototype corresponding to the target area. The model evaluation module is used to determine the carbon constraint clearing of the target area under various electricity consumption modes based on each target area model and the first flexible model and the second flexible model corresponding to each target area model; the electricity consumption modes include three electricity consumption modes corresponding to the target area model, the first flexible model and the second flexible model respectively; An adjustment and improvement module is used to adjust the power supply mode of buildings in the target area based on carbon constraint clearing under various power consumption modes.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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