Building demand response method and device based on flexible load profile

Abstract

This application relates to the field of energy management technology, and in particular to a building demand response method and apparatus based on flexible load profiles. The method includes: acquiring aggregated electrical parameters at the main incoming line and branches meeting preset key conditions of a target building to identify the current load condition in the target building based on the aggregated electrical parameters; obtaining the responsiveness of the target building based on the matching results between the current load condition and a pre-built flexible load profile library; generating response priorities based on the current demand of the target building; generating a tiered response strategy based on the responsiveness and response priorities; and controlling the target building to execute the tiered response strategy. This application can form a building flexible load profile through non-intrusive data acquisition and identification, dynamically assess the responsiveness of different loads in the current time period, and generate a tiered response strategy by combining electricity price signals, carbon intensity signals, and demand response commands, thereby improving the accuracy, reliability, and low-carbon economic efficiency of the response strategy.

Description

Technical Field

[0001] This application relates to the field of energy management technology, and in particular to a building demand response method and apparatus based on flexible load profiles. Background Technology

[0002] With the advancement of time-of-use pricing, demand response, and green electricity consumption mechanisms, commercial buildings, office buildings, complexes, and industrial park buildings are gradually transforming from simple electricity consumers into flexible resources with certain regulatory capabilities.

[0003] In related technologies, building energy management systems typically rely on sub-meters, building automation systems, and manually configured rules to determine adjustable loads. The mainstream solutions can be divided into three categories: the first category is demand response control schemes based on building automation systems or sub-metering; the second category is building demand response / energy management schemes based on optimization models; and the third category is non-intrusive load identification or equipment profiling technology.

[0004] However, among the related technologies, demand response control schemes based on building automation systems or sub-metering rely heavily on equipment communication interfaces, sub-metering integrity, and manual configuration; building demand response / energy management schemes based on optimization models are somewhat out of touch with actual application scenarios, and the optimization results are prone to mismatch with on-site execution capabilities; non-intrusive load identification or equipment profiling technology building energy management schemes lack an evaluation process.

[0005] In summary, building energy management solutions in related technologies rely heavily on equipment ledgers and manual configuration. They generally treat load identification results as energy statistics, adjustable loads as fixed capacity, and demand response strategies as fixed rules or optimization plans. They fail to dynamically estimate the actual response capacity of buildings, do not form a flexible profile to guide responses, and lack an effect feedback mechanism, making it difficult to continuously improve response reliability and low-carbon economy. These issues urgently need to be addressed. Summary of the Invention

[0006] This application provides a building demand response method and apparatus based on flexible load profiling to address the problems in related technologies where building energy management solutions rely heavily on equipment ledgers and manual configuration. Generally, load identification results are treated as energy statistics, adjustable loads are regarded as fixed capacity, and demand response strategies are treated as fixed rules or optimization plans. These solutions fail to dynamically estimate the actual response capacity of the building, do not form a flexible profile that can guide the response, lack an effect feedback mechanism, and are difficult to continuously improve response reliability and low-carbon economy.

[0007] The first aspect of this application provides a building demand response method based on flexible load profiles, comprising the following steps: obtaining aggregated electrical parameters at the main incoming line and branch lines meeting preset important conditions of a target building, so as to identify the current load condition in the target building based on the aggregated electrical parameters; obtaining the responsiveness of the target building based on the matching results of the current load condition and a pre-built flexible load profile library; generating response priorities based on the current demand of the target building, generating a graded response strategy based on the responsiveness and the response priorities, and controlling the target building to execute the graded response strategy.

[0008] Optionally, in one embodiment of this application, obtaining the aggregated electrical parameters at the main incoming line and branch lines meeting preset important conditions of the target building, so as to identify the current load condition in the target building based on the aggregated electrical parameters, includes: collecting the active power, reactive power, voltage, current, power factor, and electrical energy at the main incoming line and the branch lines meeting preset important conditions; and identifying the current air conditioning load ratio, lighting load ratio, charging load ratio, and unknown load ratio in the target building based on the active power, reactive power, voltage, current, power factor, and electrical energy, so as to obtain the current load condition by combining the identification confidence level.

[0009] Optionally, in one embodiment of this application, obtaining the responsiveness of the target building based on the matching results of the current load conditions and a pre-built flexible load profile library includes: calculating the short-term reduction capacity of the air conditioning load of the target building based on the current air conditioning power, outdoor temperature, historical response records, and comfort sensitivity of the target building; calculating the dimmable capacity of the lighting load of the target building based on the lighting time period, lighting area, and preset authorization level of the target building; and calculating the transferable capacity of the charging load of the target building based on the historical parking time and delayability of vehicles in the target building.

[0010] Optionally, in one embodiment of this application, generating a response priority based on the current demand of the target building includes: determining the carbon-electricity weight of the target building based on at least one of the time-of-use electricity price, demand warning, demand response event, grid carbon emission factor, green electricity consumption instruction and building operation target; and determining the response priority based on the carbon-electricity weight.

[0011] Optionally, in one embodiment of this application, after controlling the target building to execute the graded response strategy, the method further includes: updating the power reduction capacity, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics corresponding to each type of load in the pre-built flexible load profile library based on the execution result of the graded response strategy; determining a new responsive profile corresponding to each type of load based on the updated power reduction capacity, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics corresponding to each type of load; and updating the pre-built flexible load profile library based on the new responsive profile to obtain an updated flexible load profile library.

[0012] A second aspect of this application provides a building demand response device based on flexible load profiles, comprising: an identification module for acquiring aggregated electrical parameters at the main incoming line and branch lines meeting preset important conditions of a target building, so as to identify the current load condition in the target building based on the aggregated electrical parameters; a matching module for obtaining the responsiveness of the target building based on the matching results of the current load condition and a pre-built flexible load profile library; and a control module for generating response priorities based on the current demand of the target building, generating a graded response strategy based on the responsiveness and the response priorities, and controlling the target building to execute the graded response strategy.

[0013] Optionally, in one embodiment of this application, the identification module includes: a data acquisition unit, used to acquire the active power, reactive power, voltage, current, power factor, and electrical energy at the main incoming line and the branch line that meets preset important conditions; and an identification unit, used to identify the current air conditioning load ratio, lighting load ratio, charging load ratio, and unknown load ratio in the target building based on the active power, reactive power, voltage, current, power factor, and electrical energy, so as to obtain the current load condition by combining the identification confidence level.

[0014] Optionally, in one embodiment of this application, the matching module includes: a first calculation unit, configured to calculate the short-term reduction capacity of the air conditioning load of the target building based on the current power of the air conditioning, outdoor temperature, historical response records, and comfort sensitivity of the target building; a second calculation unit, configured to calculate the dimmable capacity of the lighting load of the target building based on the lighting time period, lighting area, and preset authorization level of the target building; and a third calculation unit, configured to calculate the transferable capacity of the charging load of the target building based on the historical parking time and delayability of vehicles in the target building.

[0015] Optionally, in one embodiment of this application, the control module includes: a first determining unit, configured to determine the carbon-electricity weight of the target building based on at least one of the target building's time-of-use electricity price, demand warning, demand response event, grid carbon emission factor, green electricity consumption instruction, and building operation target; and a second determining unit, configured to determine the response priority based on the carbon-electricity weight.

[0016] Optionally, in one embodiment of this application, it further includes: a first update module, configured to update the power reduction capacity, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics corresponding to each type of load in the pre-built flexible load profile library based on the execution result of the hierarchical response strategy after controlling the target building to execute the hierarchical response strategy; a determination module, configured to determine a new responsive profile corresponding to each type of load based on the updated power reduction capacity, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics corresponding to each type of load; and a second update module, configured to update the pre-built flexible load profile library based on the responsive profile to obtain an updated flexible load profile library.

[0017] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the building demand response method based on flexible load profiles as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described building demand response method based on flexible load profiles.

[0019] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the above-described building demand response method based on flexible load profiles.

[0020] This application's embodiments can collect aggregated electrical parameters at the building's main incoming line and key branch lines to identify various load categories and their operating behaviors. The identification results are then transformed into a "flexible load profile," recording not only power consumption but also response capability, response time, rebound characteristics, comfort risks, and historical success rates. Upon receiving electricity price, carbon intensity, or demand response commands, the system first assesses responsive resources based on the profile, then generates a tiered execution strategy, and writes the results back to the profile database after the response is completed. This achieves the further processing of non-intrusive detection results into a demand response capability profile, and integrates economic signals, carbon signals, comfort risks, and response reliability into the decision-making process. This ensures that the generated response strategy no longer relies on static capacity assumptions but is closer to the building's current actual adjustable capacity, thereby reducing risks such as insufficient response, comfort exceeding limits, and rebound peaks. Furthermore, this application can integrate electricity price and carbon intensity signals into the response score, simultaneously considering economic benefits and low-carbon operation goals. This addresses the problems in related technologies, such as the heavy reliance on equipment ledgers and manual configuration in building energy management solutions. Generally, load identification results are treated as energy statistics, adjustable loads are regarded as fixed capacity, and demand response strategies are treated as fixed rules or optimization plans. These solutions fail to dynamically estimate the actual response capacity of buildings, do not form a flexible profile to guide responses, lack an effect feedback mechanism, and are difficult to continuously improve response reliability and low-carbon economy.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a building demand response method based on flexible load profiles provided in an embodiment of this application; Figure 2 This is a schematic diagram of the framework of a building demand response system based on non-invasive flexible load profiling and carbon-electricity synergistic response capability assessment according to an embodiment of this application. Figure 3 This is a schematic diagram of the structure of a flexible load image library according to an embodiment of this application; Figure 4 This is a schematic diagram illustrating the carbon-electric synergistic response capability assessment of one embodiment of this application; Figure 5 This is a flowchart illustrating the execution of hierarchical licensing according to one embodiment of this application; Figure 6 This is a schematic diagram illustrating the response effect write-back and image update in one embodiment of this application; Figure 7A flowchart illustrating a building demand response method based on non-invasive flexible load profiling and carbon-electricity synergistic response capability assessment, according to one embodiment of this application. Figure 8 This is a schematic diagram of the building demand response device based on flexible load profile provided in the embodiments of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0023] Figure label: 10-Building demand response device based on flexible load profile; 100-Identification module, 200-Matching module and 300-Control module; 901-Memory, 902-Processor and 903-Communication interface. Detailed Implementation

[0024] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0025] The following description, with reference to the accompanying drawings, describes a building demand response method and apparatus based on flexible load profiling according to embodiments of this application. Addressing the problems mentioned in the background section, this application provides a building demand response method based on flexible load profiling. In this method, aggregated electrical parameters are collected at the building's main incoming line and key branch lines to identify various load categories and their operating behaviors. The identification results are further transformed into a "flexible load profile," which records not only power consumption but also response capability, response time, rebound characteristics, comfort risk, and historical success rate. Upon receiving electricity price, carbon intensity, or demand response instructions, the method first assesses responsive resources based on the profile, then generates a tiered execution strategy, and finally writes the results back to the profile database after the response is completed. This approach enables the further processing of non-invasive detection results into a usable capacity profile for demand response. It integrates economic signals, carbon signals, comfort risks, and response reliability into decision-making, ensuring that the generated response strategy no longer relies on static capacity assumptions but more closely approximates the building's current actual adjustable capacity. This reduces risks such as insufficient response, comfort exceeding limits, and rebound peaks. Furthermore, this application can integrate electricity price and carbon intensity signals into the response score, simultaneously considering economic benefits and low-carbon operation goals. This addresses the problems in related technologies where building energy management solutions heavily rely on equipment ledgers and manual configuration. These solutions typically treat load identification results as energy statistics, adjustable loads as fixed capacity, and demand response strategies as fixed rules or optimization plans, failing to dynamically estimate the building's actual response capacity, lacking a flexible profile to guide responses, and lacking an effect feedback mechanism, thus hindering the continuous improvement of response reliability and low-carbon economic efficiency.

[0026] Before explaining the building demand response method based on flexible load profile in the embodiments of this application, the relevant technical principles and other related contents involved in the embodiments of this application will be explained first.

[0027] With the advancement of time-of-use pricing, demand response, and green electricity consumption mechanisms, commercial buildings, office buildings, mixed-use complexes, and industrial parks are gradually transforming from simple electricity consumers into flexible resources with a certain degree of regulation capability. Air conditioning systems, lighting systems, charging facilities, hot water equipment, office sockets, chiller auxiliary equipment, and some transferable loads within these buildings all possess varying degrees of adjustability potential. If these loads can be appropriately reduced, delayed, brought forward, or transferred without significantly impacting user comfort and business operations, peak loads can be lowered, demand costs reduced, renewable energy consumption capacity improved, and participation in grid demand response or carbon reduction services possible.

[0028] However, building demand response is not simply about "power rationing" or "uniformly reducing power." The adjustability of different loads varies greatly. For example, air conditioning loads have thermal inertia and can be moderately reduced in the short term, but may rebound after the response; the adjustability of lighting loads is limited by illuminance and occupant comfort; charging facilities have a delay-delay characteristic, but are constrained by user absence time and vehicle power demand; office sockets and data equipment are generally not suitable for direct reduction. If the energy management system does not understand the response duration, recovery characteristics, comfort risks, and user acceptability of various loads, problems such as insufficient response, response failure, comfort complaints, or rebound peak shifts may occur.

[0029] However, in existing buildings, problems such as incomplete sub-metering, inaccurate branch line records, closed equipment protocols, and frequent changes in end-point load distribution are common. Although energy management platforms can see the total meter power and a small amount of branch line power, they find it difficult to determine which adjustable devices constitute the load at a certain time period, and it is also difficult to continuously estimate the actual response capabilities of various loads under different weather conditions, time periods, population densities, and historical operating conditions.

[0030] Non-intrusive energy monitoring technology provides a new data source for addressing these issues. This technology can aggregate electrical parameters from the main incoming line or key branches, identifying load categories or load events such as air conditioning, lighting, charging, hot water, and office sockets through power changes, cycle characteristics, start-stop events, and operating modes. Compared to device-by-device access, non-intrusive methods offer lower retrofit costs and faster deployment, making them particularly suitable for demand-response retrofitting of existing buildings.

[0031] Meanwhile, carbon emission factors and electricity price signals are becoming important inputs for building dispatch. Electricity prices may be high at certain times, but the grid's carbon emission factor may not be at its highest; conversely, electricity prices may be low at certain times, but carbon intensity may be high. Therefore, building demand response needs to expand from the traditional "electricity price-driven" approach to a "carbon-electricity synergistic drive," forming a comprehensive decision-making process that considers economic costs, carbon emission reduction, comfort impacts, and response reliability.

[0032] Against this backdrop, there is an urgent need for a new building demand response technology solution: without large-scale modification of terminal equipment, using non-invasive detection to continuously form flexible load profiles, dynamically assess the responsive capacity, response duration, rebound risk, and comfort risk of each type of load in the current period, and combine electricity price, carbon intensity, and demand response instructions to generate a graded response strategy, enabling buildings to participate in demand response and low-carbon operation in an interpretable, verifiable, and rewritable manner.

[0033] Building energy management solutions in related technologies mainly fall into three categories.

[0034] The first category is demand response control schemes based on building automation systems or sub-metering. These schemes typically acquire equipment operating status and power consumption by connecting to air conditioning units, chilled water pumps, lighting controllers, charging pile systems, or sub-meters, and then perform peak shaving or off-peak control according to preset rules. For example, reducing the air conditioning set temperature when demand approaches its upper limit, delaying some charging load during peak pricing periods, and turning off some non-critical lighting during demand response events. While these schemes directly target controllable equipment, they are highly dependent on equipment communication interfaces, the integrity of sub-metering, and manual configuration.

[0035] The second category is building demand response or energy management solutions based on optimization models. These solutions typically aim to minimize electricity costs, peak loads, comfort constraints, or carbon emissions. They incorporate air conditioning, energy storage, photovoltaic, charging loads, and flexible loads into a unified optimization model, generating scheduling plans through model predictive control, mixed-integer optimization, heuristic algorithms, or rule-based optimization. While these solutions possess strong theoretical optimization capabilities, in actual buildings, accurate knowledge of the set of adjustable equipment, equipment response models, and constraint parameters is crucial; otherwise, the optimization results may not match the actual on-site execution capabilities.

[0036] The third category is non-intrusive load identification or equipment profiling technology. This type of solution identifies equipment categories, operating status, or energy consumption profiles based on total power or branch aggregated power, providing users with energy consumption monitoring, anomaly identification, and energy-saving suggestions. This type of solution can reduce equipment access costs, but it usually focuses on identification or statistics, with less emphasis on further evaluation of "whether the equipment can currently participate in demand response, how much it can respond, how long it can last, whether there will be a rebound after the response, and whether it will cause comfort risks."

[0037] These technical solutions all exist, including building demand response control, optimized scheduling, and non-intrusive identification. However, they lack a connecting link for "flexible capacity assessment." These technologies typically treat load identification results as performance statistics and demand response strategies as fixed rules or optimization plans. They fail to continuously refine the load behavior identified through non-intrusive identification into a responsive capacity profile, nor do they utilize electricity price signals, carbon emission signals, and load response reliability together for tiered execution decisions.

[0038] Specifically, demand response solutions based on building automation or sub-metering typically require prior knowledge of the list of controllable equipment, branch circuit affixes, and equipment communication interfaces. In existing buildings, frequent changes in end-point loads, incomplete records, mixed branch circuit connections, and inconsistent equipment protocols are common, making it difficult for the platform to obtain a complete list of responsive resources at low cost. Relying solely on manual configuration results in high maintenance costs for response resources and is prone to discrepancies between the policy's target and the actual situation on-site.

[0039] Building demand response or energy management solutions based on optimization models typically treat adjustable loads as fixed capacity or fixed constraints. For example, they might assume that a certain type of air conditioning can be reduced by a fixed percentage, or that a certain type of charging load can be delayed for a fixed time. However, in real buildings, load response capabilities vary with weather, time of day, occupancy density, equipment operating phase, and historical control actions. Without dynamically estimating response capabilities, problems such as planned adjustable capacity exceeding actual capacity, insufficient response, recovery rebound, or exceeding comfort limits can easily occur.

[0040] Non-intrusive load identification solutions primarily output profiles of equipment type, power, or energy consumption, lacking flexible profiles for demand response. In other words, even if the system knows a certain type of load is running, it may not know whether it can be reduced, how long the reduction will last, whether it will rebound, whether it will affect user experience, or whether it is suitable for participation in electricity price response or carbon emission reduction response. This limits the conversion of non-intrusive detection results into building dispatch control.

[0041] Moreover, demand response decisions in these technologies often prioritize electricity prices or peak control, neglecting carbon emission factors. With increasing demands for green energy consumption and carbon management, buildings not only need to reduce electricity purchases when prices are high, but also need to appropriately reduce electricity consumption or shift loads when grid carbon intensity is high. Considering only economic signals without considering carbon signals may fail to achieve low-carbon operation; conversely, considering only carbon signals while ignoring response costs and comfort impacts may lead to a decline in user experience.

[0042] The response systems in related technologies also lack a mechanism for writing back the response results. After a demand response ends, the platform often only records the actual power reduction, but does not further update the response success rate, rebound characteristics, and comfort impact of various loads. This results in the next response still using the old rules, making it difficult to continuously improve response reliability.

[0043] Based on this, this application designs a building demand response method based on non-intrusive flexible load profiling and carbon-electricity synergistic response capability assessment. This application no longer simply focuses on identifying equipment power or solving a simple scheduling optimization problem. Instead, it considers multiple aspects of building flexible loads, including "responsiveness, reliable response, low-risk response, and write-back response." Through non-intrusive data collection and identification, a building flexible load profile is formed. The respondable capacity, duration, rebound risk, and comfort risk of different loads in the current period are dynamically assessed. Combined with electricity price signals, carbon intensity signals, and demand response instructions, a graded response strategy is generated, thereby improving the accuracy, reliability, and low-carbon economy of the response strategy.

[0044] Furthermore, the embodiments of this application can also update the flexible load profile with the actual response deviation, rebound power (which can be determined by calculating the average value of the power rebound amplitude and reduction amount within a certain period of time after the response ends in the historical response record), user comfort feedback and execution success rate through the response effect write-back mechanism, so that the building can gradually form a response capability model that conforms to its own equipment and user habits in long-term operation, thereby forming a complete technical closed loop.

[0045] Specifically, Figure 1 This is a flowchart illustrating a building demand response method based on flexible load profiling, provided as an embodiment of this application.

[0046] like Figure 1 As shown, this building demand response method based on flexible load profiles includes the following steps: In step S101, the aggregated electrical parameters of the main incoming line and the branch lines that meet the preset important conditions of the target building are obtained, so as to identify the current load conditions in the target building based on the aggregated electrical parameters.

[0047] It is understood that the target building here can be understood as the building object to be subject to demand response management, that is, the implementation object of the building demand response method based on flexible load profiling in this application embodiment. It includes, but is not limited to, single buildings, building complexes, building parks, and local power management units such as floors, areas or circuits within buildings.

[0048] Preset critical conditions here can be understood as pre-defined standards or rules used to screen out critical branches from the building's power distribution network. For example, in this application embodiment, certain screening rules can be pre-defined based on factors such as branch load capacity, load type adjustability, and contribution to total power consumption. Branches that meet the conditions are critical branches.

[0049] In other words, the embodiments of this application can designate branches in the target building that meet certain important conditions as key branches of the target building, thereby installing acquisition units and / or prioritizing load monitoring on these branches.

[0050] In the embodiments of this application, these key branches include, but are not limited to, circuits that contribute significantly to the total load or have high participation response value, such as air conditioning power branches, lighting branches, and charging facility branches.

[0051] It should be noted that the specific preset conditions can be set and adjusted by those skilled in the art according to actual needs and application scenarios. The embodiments in this application are only illustrative and do not impose specific limitations.

[0052] In actual implementation, this application can obtain aggregated electrical parameters at the main incoming line and these key branches in the target building, thereby identifying the current load condition of the target building, that is, the current load category and its operating status in the target building.

[0053] In this context, the main incoming line refers to the primary power supply point used by the building to draw power from the external power grid, typically corresponding to the incoming terminal of the building's main distribution cabinet. Aggregated electrical parameters can be understood as a summary of various electrical parameters obtained from collection points on the main incoming line or key branches.

[0054] Optionally, in one embodiment of this application, the aggregated electrical parameters at the main incoming line and branch lines meeting preset important conditions of the target building are obtained to identify the current load conditions in the target building based on the aggregated electrical parameters. This includes: collecting the active power, reactive power, voltage, current, power factor, and electrical energy at the main incoming line and branch lines meeting preset important conditions; and identifying the current air conditioning load ratio, lighting load ratio, charging load ratio, and unknown load ratio in the target building based on the active power, reactive power, voltage, current, power factor, and electrical energy, so as to obtain the current load conditions by combining the identification confidence level.

[0055] In some embodiments, the aggregated electrical parameters obtained by this application include, but are not limited to, data such as the active power, reactive power, voltage, current, power factor, and electrical energy of the target building.

[0056] Based on these aggregated electrical parameter data, embodiments of this application can identify load categories such as the current air conditioning load percentage, lighting load percentage, charging load percentage, and unknown load percentage in the target building, as well as their corresponding operating states, and use the identified information as the current load condition of the target building.

[0057] For example, Figure 2 This is a schematic diagram illustrating the framework of a building demand response system based on non-invasive flexible load profiling and carbon-electricity synergistic response capability assessment, according to one embodiment of this application. Figure 2 As shown, the system includes, but is not limited to, an aggregated electrical parameter acquisition unit, a non-intrusive load status identification unit, a flexible load profile library, a response capability assessment unit, a carbon-electricity synergistic signal analysis unit, a hierarchical response strategy generation unit, an execution control and authorization unit, a response effect write-back unit, and a user interaction and operation and maintenance interface.

[0058] The aggregated electrical parameter acquisition unit is installed in key circuits such as the building's main incoming line, floor distribution cabinets, air conditioning power branches, lighting branches, and charging facility branches. It is used to collect data such as active power, reactive power, voltage, current, power factor, and electrical energy. This unit eliminates the need to install individual sensors for each device, effectively reducing retrofit costs.

[0059] The non-intrusive load condition identification unit (NINDI) is used to identify load categories and operating states from aggregated electrical parameters. This unit can output estimated power and operating status for loads such as air conditioning loads, lighting loads, charging loads, power equipment loads, and unknown loads. It should be noted that the output of the NINDI is not the final control command, but rather the data basis for flexible load profiling and response capability assessment.

[0060] In practical applications, the system can be deployed in advance on a building energy management server, edge gateway, or cloud-edge collaboration platform.

[0061] Then, in this embodiment of the application, the aggregated electrical parameters of the main incoming line and key branch lines in the building can be collected in real time using the aggregated electrical parameter acquisition unit in the system, and preprocessing operations such as clock synchronization, outlier removal and baseline separation can be performed.

[0062] Here, clock synchronization refers to the unified calibration of the local clocks of the acquisition units at the main incoming line and each branch line to ensure that the data from all acquisition points belong to the same time base timestamp and that data from different sources are aligned in time.

[0063] Anomaly removal here refers to filtering or thresholding the collected electrical parameter data sequence to remove sampling points that are outside the reasonable range or do not conform to physical laws (such as negative power values, sudden change amplitude exceeding the set threshold, etc.) to avoid affecting subsequent calculations.

[0064] Baseline separation here refers to decomposing the acquired actual power curve into two parts: baseline load and event component. The baseline load is mainly used to characterize the normal power consumption trend during the period that is not affected by response control, while the event component is mainly used to characterize the deviation caused by response actions or load fluctuations.

[0065] Additionally, embodiments of this application can also label sample data based on intraday time period, weather, holidays, and building usage status for subsequent identification and profile matching. That is, embodiments of this application can classify preprocessed data by adding tags, assigning certain attributes to each data point to facilitate understanding the conditions under which the data was collected. Tag content includes, but is not limited to: Intraday timeframes: daytime / nighttime, peak / off-peak / valley, etc.; Weather: temperature, humidity, sunshine, rain, etc.; Holidays: Weekdays / Rest Days / Statutory Holidays; Building occupancy status: Fully occupied / Partially occupied / Vacant, Meeting / Office / Business, etc.

[0066] When the load behavior of a building varies greatly under different conditions (for example, the air conditioning power is much higher at 35°C in summer than at 25°C in spring), the embodiments of this application can improve the accuracy of identification and the reliability of assessment by retrieving historical data under the same conditions through these pre-labeled data tags during subsequent identification and profile matching.

[0067] Next, this embodiment of the application can utilize the non-intrusive load status identification unit in the system to analyze aggregated electrical parameters in order to estimate the current main load categories and their operating status in the building. For example, it can identify the current proportion of air conditioning load, lighting load, charging load, and unknown load (parts in the building that are difficult to identify clearly; in this embodiment of the application, the unknown load channel is retained and not included in the automatic response resources), and provide identification confidence levels, etc., and combine this information to form the current load condition of the building.

[0068] It should be noted that the load identification method in this application embodiment can be adaptively set and adjusted by those skilled in the art according to actual conditions. The non-intrusive identification model can employ event detection, rule classification, machine learning models, or deep learning models; the input features can be active power, or reactive power, current, voltage, power factor, or harmonic summaries can be added. The embodiments in this application are for illustrative purposes only and do not impose specific limitations.

[0069] Step S102: Based on the matching results of the current load conditions and the pre-built flexible load profile library, the responsiveness of the target building is obtained.

[0070] In some embodiments, after obtaining the current load condition of the target building, this application can match the identified current load condition with a pre-built flexible load profile library to obtain the current response capability package of the target building, that is, determine the current responsive capability (current adjustable resource) of the target building.

[0071] Here, the pre-built flexible load profile library can be understood as a pre-established structured database used to describe the response characteristics of various loads in a building.

[0072] Figure 3 This is a schematic diagram of the structure of a flexible load image library according to an embodiment of this application. Figure 3 As shown, the pre-built profile library contains pre-set, callable, and updatable response characteristic parameters for each type of load (air conditioning, lighting, charging, sockets, power equipment, unknown load), including but not limited to parameters such as base power, power reduction, power transfer, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics.

[0073] In this embodiment of the application, the flexible load profile library can be pre-established, but is not limited to, through offline data collection and model initialization.

[0074] For example, after the system is deployed but before it is officially put into operation, this application can first use the aggregated electrical parameter acquisition unit to continuously collect aggregated electrical parameter data of the main incoming line and key branch lines of the target building under typical operating conditions. These data need to cover weekdays, rest days, different seasons and different time periods. Similarly, these data are preprocessed by clock synchronization, outlier removal and baseline separation.

[0075] Then, in this embodiment of the application, a non-intrusive load status identification unit can be used to perform cluster analysis on these preprocessed data, decompose the power curves into air conditioning, lighting, charging, power equipment and unknown loads, and statistically analyze the power level, running time and start-stop patterns of each type of load to form an initial base power and operating characteristic baseline.

[0076] Based on this, technical professionals in this field can set default response parameters for each type of load. For example, for air conditioning loads, the power reduction, continuous response time, and minimum recovery time can be set by combining the equipment's base power (such as rated power) and the empirical threshold of human comfort sensitivity; for lighting loads, the dimming depth and user authorization level can be set according to the attributes of the area and natural lighting conditions; for charging loads, the transferable power and the range of delay levels can be set according to the parking space usage characteristics and user travel habits.

[0077] For example, the short-term reduction capacity of the air conditioning load can be calculated, but is not limited to, in the following way: Let the current power be P. n Comfort sensitivity is S n (Normalized values ​​range from 0 to 1), historical response success rate is H ac The base adjustable ratio R base Temperature correction factor k T (This can be obtained from the building's current outdoor temperature. Specific temperature correction rules can be determined by those skilled in the art based on actual conditions. This embodiment is merely illustrative and not intended to impose specific limitations.) Therefore, the capacity A=P can be reduced temporarily. n ×R base ×k T ×Hac×(1-S n If the calculation result is negative or exceeds the maximum power reduction limit for that type of load in the image library, then the boundary value is taken.

[0078] Meanwhile, based on experience or existing theories, technical personnel in this field can set statistical parameters such as the rebound power coefficient, historical response success rate, and energy consumption compensation characteristics of each type of load to industry experience values ​​or neutral values ​​(e.g., the success rate is set to 0.5 and the rebound coefficient is set to 0.1).

[0079] Finally, after the system is officially launched, the existing profile database can be dynamically revised based on the actual results. Profiles can be created by equipment category, floor, area, circuit, or tenant.

[0080] In other words, the pre-built flexible load profile library in this application embodiment refers to the "pre-built" profile library for the current moment. From the perspective of runtime, the profile library is constantly updated.

[0081] Optionally, in one embodiment of this application, the responsiveness of the target building is obtained based on the matching results of the current load conditions and a pre-built flexible load profile library, including: calculating the short-term reduction capacity of the air conditioning load of the target building based on the current air conditioning power, outdoor temperature, historical response records, and comfort sensitivity of the target building; calculating the dimmable capacity of the lighting load of the target building based on the lighting time period, lighting area, and preset authorization level of the target building; and calculating the transferable capacity of the charging load of the target building based on the historical parking time and delay level of vehicles in the target building.

[0082] In some embodiments, when matching the identification result (current load) with a pre-built flexible load profile library to determine the currently adjustable resources, this application may, but is not limited to, implement the matching according to load category.

[0083] Specifically, this application can calculate the response capacity package (i.e., capacity package) that the target building can currently participate in based on the current load conditions and a pre-built flexible load profile library. This package includes, but is not limited to, response capacities for various types of loads (such as capacity that can be reduced, capacity that can be delayed, capacity that requires manual confirmation, etc.), corresponding durations, recovery times, risk levels, and confidence levels (i.e., reliability). This capacity package represents the current responsiveness of the target building.

[0084] For example, for air conditioning load, this application can estimate the short-term reduction capacity of the air conditioning load based on the current power of the target building, the outdoor temperature (relative to the air conditioner, i.e., the ambient temperature of the target building), historical response records, and comfort sensitivity.

[0085] For lighting load, this application can estimate the dimmable capacity of the lighting load based on the lighting time period, lighting area and preset authorization level of the target building.

[0086] For charging loads, this application can estimate the transferable capacity of the charging load based on the historical parking duration and delayability of vehicles in the target building.

[0087] In practical applications, the response capability assessment unit can generate a current response capability package based on the input current load conditions and flexible load profile library, including capacity, duration, recovery time, risk level, and confidence level.

[0088] For example, the system can calculate the current responsive capacity based on the current load status and profile parameters, and output it in tiers of low risk, medium risk, and high risk. Each tier of capacity is accompanied by the duration, recovery time, and confidence level: "80 kW can be reduced in the next 30 minutes for low risk, 130 kW for medium risk, and 180 kW for high risk, but authorization is required."

[0089] The risk level can be determined by a combination of factors, including but not limited to comfort sensitivity, historical response success rate, and rebound power coefficient. For example, a load with a comfort sensitivity ≥ 0.7 and a historical response success rate ≤ 0.5 is classified as high risk; a load with a comfort sensitivity ≥ 0.5 and a historical success rate ≤ 0.7 is classified as medium risk; and all other cases are classified as low risk. This information is used to control the response depth and execution authorization method during subsequent strategy generation.

[0090] Step S103: Generate response priorities based on the current needs of the target building, generate a hierarchical response strategy based on the responsiveness and response priorities, and control the target building to execute the hierarchical response strategy.

[0091] As one possible approach, considering that buildings have different needs at different times, this application can generate response priorities based on the current needs of the target building.

[0092] Then, this application can generate a certain hierarchical response strategy based on the response priority for the target building to execute.

[0093] Optionally, in one embodiment of this application, generating a response priority based on the current demand of the target building includes: determining the carbon-electricity weight of the target building based on at least one of the target building's time-of-use electricity price, demand warning, demand response event, grid carbon emission factor, green electricity consumption instruction and building operation target; and determining the response priority based on the carbon-electricity weight.

[0094] In some embodiments, this application may determine the current demand of a target building based on at least one of the target building’s time-of-use electricity price, demand warning, demand response event, grid carbon emission factor, green electricity consumption instruction and building operation target, thereby determining a carbon-electricity weight based on the current demand and determining a response priority based on the carbon-electricity weight.

[0095] For example, this application can determine the current demand of the target building based on the target building's time-of-use electricity price, demand warning, demand response events, grid carbon emission factor, green electricity consumption instructions and building operation targets, such as "priority to reduce electricity costs", "priority to reduce carbon emissions", "priority to comfort" or "priority to all aspects", and then dynamically adjust the carbon-electricity weight according to the current demand and convert the carbon-electricity weight into response priority.

[0096] In other words, the embodiments of this application can transform electricity price, demand, grid carbon intensity and demand response events into a unified response priority, avoiding the limitations of making one-sided decisions based solely on electricity price or solely on carbon signals.

[0097] Then, in this embodiment of the application, candidate strategies can be generated based on the obtained building responsiveness (response capability package) and response priority (carbon electricity weight), and loads with high confidence, low comfort impact, low rebound risk and high carbon electricity benefit can be selected for response according to risk ranking.

[0098] Figure 4 This is a schematic diagram illustrating the carbon-electric synergistic response capability assessment of one embodiment of this application. Figure 4 As shown, in practical applications, this application can use the carbon-electricity collaborative signal analysis unit to analyze relevant information such as time-of-use electricity price, demand warning, demand response events, grid carbon emission factor, green electricity consumption instructions and building operation targets of the target building. If the current period is a high electricity price period, the weight of economic response will be increased; if the grid carbon intensity is high, the weight of low carbon response will be increased; if the user sets comfort priority, the adjustment range of comfort-sensitive load will be reduced.

[0099] Figure 5 This is a flowchart illustrating the execution of a hierarchical licensing mechanism according to one embodiment of this application. Figure 5 As shown, in this embodiment of the application, after generating a hierarchical response strategy, a hierarchical response strategy generation unit can generate first-level, second-level, and third-level response strategies based on the response capability assessment results and the carbon-electricity synergy signal. First-level responses are typically low-risk strategies, such as lighting fine-tuning and charging delays; second-level responses are medium-risk strategies, such as short-term air conditioner load reduction and partial socket load shifting; third-level responses are high-intensity strategies, requiring user authorization or maintenance confirmation.

[0100] Finally, the execution control and authorization unit can convert the response strategy into equipment control commands, building automation commands, energy storage scheduling commands, or user prompts, and execute the control according to the strategy level. For high-risk or high-impact strategies (strategies requiring confirmation), the system can first send an authorization request to the user or maintenance personnel through the user terminal or operation and maintenance interface; for low-risk strategies (automatic authorization strategies), the system can automatically execute according to preset permissions and directly issue control; for strategies with excessively high risk, the system only provides suggestions and does not execute them automatically.

[0101] Additionally, the policy can be automatically distributed to the building automation system or manually executed via prompts on the user terminal; the authorization method can adopt fixed permissions, dynamic confirmation, tenant-level authorization, or operation and maintenance review, as long as the execution intensity can be controlled according to the risk level. The embodiments in this application are only illustrative and do not impose specific limitations.

[0102] Therefore, the embodiments of this application can realize automatic execution, user-confirmed execution, or prohibition of execution based on load type and risk level, avoiding miscontrol of critical loads and high comfort-sensitive loads.

[0103] It should be noted that the "carbon-electricity synergy" in the embodiments of this application is not limited to real-time carbon emission factors, but may also include regional carbon intensity, green electricity consumption priority, carbon emission constraints, carbon quota costs, or internal corporate carbon management indicators.

[0104] Furthermore, the “tiered authorization” in this application embodiment is not limited to manual confirmation, but also includes automatic authorization based on user preset policies, tenant-level authorization, property-level authorization and operation and maintenance-level authorization.

[0105] Optionally, in one embodiment of this application, after controlling the target building to execute the graded response strategy, the method further includes: updating the power reduction capacity, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics corresponding to each type of load in the pre-built flexible load profile library based on the execution result of the graded response strategy; determining a new responsive profile corresponding to each type of load based on the updated power reduction capacity, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics corresponding to each type of load; and updating the pre-built flexible load profile library based on the responsive profile to obtain the updated flexible load profile library.

[0106] In other embodiments, after each target building response is completed, this application can calculate the actual response amount, deviation, rebound and comfort impact of that response and write the results back to the flexible load profile library.

[0107] Then, this embodiment of the application can combine historical operation records and current response records to generate a database that integrates the base power, reduceable power, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics of the current response record. This is equivalent to updating the original parameters such as base power, reduceable power, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics in the profile library. Then, based on these updated parameter information, a new responsive profile corresponding to each type of load can be generated again (different from the responsive profile corresponding to each type of load before the response is executed, which is equivalent to updating the original responsive profile of each type of load). The new responsive profile corresponding to each type of load is then written back to the profile library, ultimately realizing the dynamic change of the profiles in the profile library with seasons, time periods, and user authorization levels.

[0108] In the event of multiple failed responses from a certain type of load, the embodiments of this application can reduce its subsequent confidence level; in the event of long-term stable responses from a certain type of load, the embodiments of this application can increase its available capacity weight.

[0109] Figure 6 This is a schematic diagram illustrating the response effect write-back and image update according to one embodiment of this application. Figure 6 As shown, in practical applications, this application can use the response effect write-back unit to record the actual reduction amount, duration, rebound power, comfort complaints, equipment abnormalities and user cancellations after execution and write them back to the profile library to update the flexible load profile library, transforming the result of one response into experience for the next response, making subsequent response capability assessments more closely resemble real buildings.

[0110] It should be noted that the flexible load profile in this embodiment can be represented by table fields, vectors, cluster centers, state transition models, or hierarchical labels; the profile update can also be achieved using recursive averaging, sliding windows, forgetting factors, or incremental learning. The specific profile representation and update methods can be set and adjusted by those skilled in the art according to actual circumstances. This embodiment is merely illustrative and does not impose specific limitations.

[0111] This application embodiment can obtain load categories and operating status through non-invasive detection without large-scale modification of terminal equipment, and further form a flexible load profile. Since the profile includes the responsiveness ratio, duration, rebound characteristics, comfort sensitivity, and historical success rate, the response strategy generated by the system no longer relies on static capacity assumptions, but is closer to the current actual adjustable capacity of the building, thereby reducing the risks of insufficient response, comfort exceedance, and rebound peaks.

[0112] Furthermore, this application embodiment can integrate electricity price signals and carbon intensity signals into the response score, simultaneously considering economic benefits and low-carbon operation goals. Through hierarchical authorization execution and response effect write-back mechanism, this application embodiment can continuously revise the profile parameters based on actual execution results, making subsequent demand response strategies increasingly aligned with the building's own equipment characteristics and user acceptance. Therefore, this application embodiment can achieve a better comprehensive balance between response reliability, user comfort, economy, and carbon emission reduction effects.

[0113] The following is a detailed explanation of the building demand response method based on flexible load profile in this application, using a specific embodiment.

[0114] Figure 7 This is a flowchart illustrating a building demand response method based on non-invasive flexible load profiling and carbon-electricity synergistic response capability assessment, according to one embodiment of this application. Figure 7 As shown: The first step involves the system continuously collecting aggregated electrical parameters of the building's main incoming lines and key branch lines, and performing clock synchronization, outlier removal, and baseline separation. The system can then label samples based on intraday time of day, weather, holidays, and building usage status for subsequent identification and profile matching.

[0115] The second step involves a non-intrusive load condition identification unit analyzing aggregated electrical parameters to estimate the current main load categories and operating status. For example, it identifies the current proportion of air conditioning load, lighting load, charging load, and unknown load, and provides the identification confidence level.

[0116] The third step involves matching the identification results with a flexible load profile database to determine currently adjustable resources. For air conditioning loads, the system estimates the capacity that can be temporarily reduced based on current power, outdoor temperature, historical response records, and comfort sensitivity; for lighting loads, the system estimates the adjustable lighting capacity based on time period, region, and authorization level; and for charging loads, the system estimates the transferable capacity based on historical downtime and delayability.

[0117] The fourth step involves the response capability assessment unit generating the current response capability package. This package includes capacity, duration, recovery time, risk level, and confidence level. For example, the system might output, "Within the next 30 minutes, a low-risk reduction of 80 kW is possible, a medium-risk reduction of 130 kW is possible, and a high-risk reduction of 180 kW is possible, but authorization is required."

[0118] The fifth step involves the carbon-electricity synergy signal analysis unit analyzing external signals. If the current period is characterized by high electricity prices, the system increases the weight of the economic response; if the grid carbon intensity is high, the system increases the weight of the low-carbon response; if the user prioritizes comfort, the system reduces the adjustment range of comfort-sensitive loads.

[0119] The sixth step involves the tiered response strategy generation unit generating candidate strategies based on the response capability package and carbon-electricity weights, and sorting them by risk. Loads with high confidence, low comfort impact, low rebound risk, and high carbon-electricity benefits are prioritized for response.

[0120] Step 7: The execution control and authorization unit executes control according to the policy level. For automatic authorization policies, the system directly issues control; for policies requiring confirmation, the system requests confirmation through the user terminal or operation and maintenance interface; for policies with excessively high risk, the system only provides suggestions and does not execute them automatically.

[0121] Step 8: After the response is completed, the system calculates the actual response amount, deviation, rebound, and comfort impact, and writes the results back to the flexible load profile database. If a certain type of load fails to respond multiple times, its subsequent confidence level is reduced; if a certain type of load responds stably for a long period, its available capacity weight is increased.

[0122] The key model expressions involved in the process can be, but are not limited to, represented as follows: Let the first Load type during time period The flexible image vector is:

[0123] in, For the current estimated power, For responsiveness ratio, For the duration, The rebound coefficient, For comfort sensitivity, Based on historical success rate, To identify confidence levels.

[0124] No. The responsive capacity of a load class can be expressed as:

[0125] If the comfort risk is high, a risk reduction item can be introduced:

[0126] The total responsive capacity of a building can be expressed as:

[0127] The carbon-electricity synergy benefit score can be expressed as:

[0128] in, This refers to the electricity price or demand pressure coefficient. For the carbon intensity or carbon emission reduction weight of the power grid, For the comprehensive risk of this type of load, These are the weighting coefficients.

[0129] The carbon-electricity synergy score can be implemented using weighted summation, hierarchical priority, rule base, optimization model, or model predictive control; the weights can be selected by the user based on economic priority, low-carbon priority, or comfort priority. The embodiments in this application are for illustrative purposes only and do not constitute specific limitations.

[0130] The profile can be updated recursively after a response:

[0131] in, Forgetting factor, This represents the image update amount observed after this response. This formula is used to ensure that the image retains long-term patterns while also adapting to changes in building equipment and user habits.

[0132] The building demand response method based on flexible load profiling proposed in this application collects aggregated electrical parameters at the building's main incoming line and key branch lines to identify various load categories and their operating behaviors. The identification results are then transformed into a "flexible load profile," recording not only power consumption but also response capability, response duration, rebound characteristics, comfort risks, and historical success rates. Upon receiving electricity price, carbon intensity, or demand response instructions, the method first assesses responsive resources based on the profile, then generates a tiered execution strategy, and finally writes the results back to the profile database after the response is completed. This process transforms non-intrusive detection results into a usable demand response capability profile, integrating economic signals, carbon signals, comfort risks, and response reliability into the decision-making process. The generated response strategy no longer relies on static capacity assumptions but more closely approximates the building's current actual adjustable capacity, thereby reducing risks such as insufficient response, comfort exceedances, and rebound peaks. Furthermore, this application can integrate electricity price and carbon intensity signals into the response score, simultaneously considering economic benefits and low-carbon operation goals. This addresses the problems in related technologies, such as the heavy reliance on equipment ledgers and manual configuration in building energy management solutions. Generally, load identification results are treated as energy statistics, adjustable loads are regarded as fixed capacity, and demand response strategies are treated as fixed rules or optimization plans. These solutions fail to dynamically estimate the actual response capacity of buildings, do not form a flexible profile to guide responses, lack an effect feedback mechanism, and are difficult to continuously improve response reliability and low-carbon economy.

[0133] Next, referring to the accompanying drawings, a building demand response device based on a flexible load profile proposed according to an embodiment of this application is described.

[0134] Figure 8 This is a schematic diagram of the building demand response device based on flexible load profile according to an embodiment of this application.

[0135] like Figure 8 As shown, the building demand response device 10 based on flexible load profile includes: module 100, module 200 and module 300.

[0136] The identification module 100 is used to acquire the aggregated electrical parameters of the main incoming line and the branch lines that meet the preset important conditions of the target building, so as to identify the current load condition of the target building based on the aggregated electrical parameters; the matching module 200 is used to obtain the responsiveness of the target building based on the matching results of the current load condition and the pre-built flexible load profile library; the control module 300 is used to generate response priorities according to the current needs of the target building, so as to generate a hierarchical response strategy based on the responsiveness and response priorities, and control the target building to execute the hierarchical response strategy.

[0137] Optionally, in one embodiment of this application, the identification module 100 includes: a data acquisition unit, used to acquire active power, reactive power, voltage, current, power factor, and electrical energy at the main incoming line and branches that meet preset important conditions; and an identification unit, used to identify the current air conditioning load ratio, lighting load ratio, charging load ratio, and unknown load ratio in the target building based on the active power, reactive power, voltage, current, power factor, and electrical energy, so as to obtain the current load condition by combining the identification confidence level.

[0138] Optionally, in one embodiment of this application, the matching module 200 includes: a first calculation unit, used to calculate the short-term reduction capacity of the air conditioning load of the target building based on the current power of the air conditioning in the target building, the outdoor temperature, historical response records, and comfort sensitivity; a second calculation unit, used to calculate the dimmable capacity of the lighting load of the target building based on the lighting time period, lighting area, and preset authorization level of the target building; and a third calculation unit, used to calculate the transferable capacity of the charging load of the target building based on the historical parking time and delay level of vehicles in the target building.

[0139] Optionally, in one embodiment of this application, the control module 300 includes: a first determining unit, configured to determine the carbon-electricity weight of the target building based on at least one of the target building's time-of-use electricity price, demand warning, demand response event, grid carbon emission factor, green electricity consumption instruction and building operation target; and a second determining unit, configured to determine the response priority according to the carbon-electricity weight.

[0140] Optionally, in one embodiment of this application, it further includes: a first update module, configured to update the power reduction capacity, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics corresponding to each type of load in the pre-built flexible load profile library based on the execution result of the hierarchical response strategy after controlling the target building to execute the hierarchical response strategy; a determination module, configured to determine a new responsive profile corresponding to each type of load based on the updated power reduction capacity, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics corresponding to each type of load; and a second update module, configured to update the pre-built flexible load profile library based on the responsive profile to obtain an updated flexible load profile library.

[0141] It should be noted that the foregoing explanation of the building demand response method embodiment based on flexible load profile also applies to the building demand response device based on flexible load profile in this embodiment, and will not be repeated here.

[0142] The building demand response device based on flexible load profiling proposed in this application can collect aggregated electrical parameters at the building's main incoming line and key branch lines to identify various load categories and their operating behaviors. The identification results are then transformed into a "flexible load profile," recording not only power consumption but also response capability, response time, rebound characteristics, comfort risks, and historical success rates. Upon receiving electricity price, carbon intensity, or demand response commands, the device first assesses responsive resources based on the profile, then generates a tiered execution strategy, and writes the results back to the profile database after the response is completed. This process transforms non-intrusive detection results into a usable demand response capability profile, integrating economic signals, carbon signals, comfort risks, and response reliability into the decision-making process. The generated response strategy no longer relies on static capacity assumptions but is closer to the building's current actual adjustable capacity, thereby reducing risks such as insufficient response, comfort exceeding limits, and rebound peaks. Furthermore, this application can integrate electricity price and carbon intensity signals into the response score, simultaneously considering economic benefits and low-carbon operation goals. This addresses the problems in related technologies, such as the heavy reliance on equipment ledgers and manual configuration in building energy management solutions. Generally, load identification results are treated as energy statistics, adjustable loads are regarded as fixed capacity, and demand response strategies are treated as fixed rules or optimization plans. These solutions fail to dynamically estimate the actual response capacity of buildings, do not form a flexible profile to guide responses, lack an effect feedback mechanism, and are difficult to continuously improve response reliability and low-carbon economy.

[0143] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

[0144] When the processor 902 executes the program, it implements the building demand response method based on flexible load profile provided in the above embodiments.

[0145] Furthermore, electronic devices also include: Communication interface 903 is used for communication between memory 901 and processor 902.

[0146] The memory 901 is used to store computer programs that can run on the processor 902.

[0147] The memory 901 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0148] If the memory 901, processor 902, and communication interface 903 are implemented independently, then the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0149] Optionally, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated on a single chip, then the memory 901, processor 902, and communication interface 903 can communicate with each other through an internal interface.

[0150] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0151] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described building demand response method based on flexible load profiles.

[0152] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the building demand response method based on flexible load profiles provided in this application.

[0153] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0154] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0155] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0156] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0157] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0158] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0159] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0160] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A building demand response method based on flexible load profiles, characterized in that, Includes the following steps: The aggregated electrical parameters at the main incoming line and branch lines that meet preset important conditions of the target building are obtained, so as to identify the current load condition in the target building based on the aggregated electrical parameters; Based on the matching results between the current load conditions and the pre-built flexible load profile database, the responsiveness of the target building is obtained; A response priority is generated based on the current needs of the target building, and a hierarchical response strategy is generated based on the responsiveness and the response priority, and the target building is controlled to execute the hierarchical response strategy.

2. The method according to claim 1, characterized in that, The step of acquiring the aggregated electrical parameters at the main incoming line and branch lines meeting preset important conditions of the target building, and identifying the current load condition in the target building based on the aggregated electrical parameters, includes: Collect the active power, reactive power, voltage, current, power factor, and electrical energy at the main incoming line and the branches that meet the preset important conditions; Based on the active power, reactive power, voltage, current, power factor, and electrical energy, the current air conditioning load ratio, lighting load ratio, charging load ratio, and unknown load ratio in the target building are identified, and the current load condition is obtained by combining the identification confidence level.

3. The method according to claim 1, characterized in that, The matching results based on the current load conditions and the pre-built flexible load profile database are used to obtain the responsiveness of the target building, including: Based on the current air conditioning power, outdoor temperature, historical response records, and comfort sensitivity of the target building, calculate the short-term reduction capacity of the air conditioning load of the target building; Based on the lighting time period, lighting area, and preset authorization level of the target building, calculate the dimmable capacity of the lighting load of the target building; Based on the historical parking duration and delayability of vehicles in the target building, the transferable capacity of the charging load of the target building is calculated.

4. The method according to claim 1, characterized in that, The step of generating a response priority based on the current needs of the target building includes: The carbon-electricity weight of the target building is determined based on at least one of the following: time-of-use electricity price, demand warning, demand response events, grid carbon emission factor, green electricity consumption instructions, and building operation targets. The response priority is determined based on the carbon-electric weight.

5. The method according to claim 1, characterized in that, After controlling the target building to execute the tiered response strategy, the method further includes: Based on the execution results of the graded response strategy, update the power reduction, transferable power, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics of each type of load in the pre-built flexible load profile library. Based on the updated power reduction capacity, power transfer capacity, sustainable response duration, minimum recovery time, rebound power coefficient, comfort sensitivity, user authorization level, historical response success rate, and post-response energy consumption compensation characteristics of each type of load, a new responsive profile is determined for each type of load. The pre-built flexible load profile library is updated based on the new responsive profile to obtain the updated flexible load profile library.

6. A building demand response device based on flexible load profiles, characterized in that, include: The identification module is used to obtain the aggregated electrical parameters at the main incoming line and the branch lines that meet the preset important conditions of the target building, so as to identify the current load condition in the target building based on the aggregated electrical parameters; The matching module is used to obtain the responsiveness of the target building based on the matching results between the current load conditions and the pre-built flexible load profile library; The control module is used to generate response priorities based on the current needs of the target building, generate a hierarchical response strategy based on the responsiveness and the response priorities, and control the target building to execute the hierarchical response strategy.

7. The apparatus according to claim 6, characterized in that, The identification module includes: The data acquisition unit is used to acquire the active power, reactive power, voltage, current, power factor, and electrical energy at the main incoming line and the branch lines that meet the preset important conditions; The identification unit is used to identify the current air conditioning load ratio, lighting load ratio, charging load ratio, and unknown load ratio in the target building based on the active power, reactive power, voltage, current, power factor, and electrical energy, so as to obtain the current load condition by combining the identification confidence level.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the building demand response method based on flexible load profiles as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the building demand response method based on flexible load profiles as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the building demand response method based on flexible load profiles as described in any one of claims 1-5.

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