Methods, systems, equipment and media for quantifying building energy consumption

Abstract

This application discloses a method, system, equipment, and medium for quantifying building energy consumption, relating to the technical field of energy consumption quantification. The building energy consumption quantification method includes: acquiring passenger flow data, meteorological data, and equipment data for a building area; quantifying the passenger flow data, meteorological data, and equipment data to obtain passenger flow coefficients, meteorological coefficients, and equipment efficiency coefficients; acquiring basic energy consumption data for the building area under standard operating conditions; and processing the basic energy consumption data based on the passenger flow coefficients, meteorological coefficients, and equipment efficiency coefficients to obtain quantified energy consumption data for the building area.

Description

Technical Field

[0001] This application relates to the technical field of energy consumption quantification, and in particular to a method, system, equipment and medium for quantifying building energy consumption. Background Technology

[0002] In the refined management of energy consumption in commercial buildings, the core challenge is how to accurately quantify the real-time energy consumption demand of a specific area (such as a shop or a restaurant) in the near future.

[0003] Related technologies estimate energy consumption based on fixed weights or simple linear regression, setting a base energy consumption value for a specific area of ​​a building and then linearly adjusting it based on one or two main factors (such as outdoor temperature). However, this method has a single data dimension, resulting in a significantly lower estimated energy consumption that fails to meet actual needs. It treats each influencing factor as an independent variable and simply adds them together, ignoring the coupling relationship between them. Moreover, the weighting coefficients are pre-set and fixed, failing to adapt to the dynamic changes in the building's characteristics at different operational stages and in different functional areas (such as restaurants versus bookstores), leading to a long-term decline in accuracy. Summary of the Invention

[0004] The embodiments of this application aim to at least partially solve one of the technical problems in the related art. Therefore, the purpose of the embodiments of this application is to provide a method, system, device, and medium for quantifying building energy consumption, thereby improving the accuracy of energy consumption quantification.

[0005] This application provides a method for quantifying building energy consumption, including: acquiring passenger flow data, meteorological data, and equipment data of a building area; quantifying the passenger flow data, meteorological data, and equipment data to obtain passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient; acquiring basic energy consumption data of the building area under standard operating conditions; and processing the basic energy consumption data based on the passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient to obtain quantified energy consumption data of the building area.

[0006] For example, the passenger flow data includes video stream data obtained by collecting passenger flow data in the building area based on the image acquisition device and probe data identifying connections to the wireless network in the building area; the passenger flow data is quantified to obtain a passenger flow coefficient, including: obtaining baseline number data, wherein the baseline number data is determined based on design specification data or historical passenger flow data; determining the number of people in the building area based on the video stream data and / or probe data; determining the ratio of the number data to the baseline number data, and using the ratio data as the passenger flow coefficient.

[0007] For example, the meteorological data includes temperature data, humidity data, and solar radiation intensity data; the meteorological data is quantified to obtain meteorological coefficients, including: mapping at least one of the temperature data, humidity data, and solar radiation intensity to a preset mapping table to obtain mapping rule data, wherein the preset mapping table represents the correspondence between at least one of the temperature data, humidity data, and solar radiation intensity and the mapping rule data; and the meteorological coefficients are obtained based on the mapping rule data. For example, the equipment data includes runtime data and refrigerant pressure data; the equipment data is quantified to obtain the equipment efficiency coefficient, including: determining equipment degradation data based on the refrigerant pressure data and runtime data; and quantifying the equipment data based on the equipment degradation data to obtain the equipment efficiency coefficient.

[0008] For example, the basic energy consumption data is processed based on passenger flow coefficient, meteorological coefficient and equipment efficiency coefficient to obtain the energy consumption quantitative data of the building area, including: performing continuous multiplication operation between the basic energy consumption data and passenger flow coefficient, meteorological coefficient and equipment efficiency coefficient to obtain the energy consumption quantitative data of the building area.

[0009] For example, the building energy consumption quantification method further includes: sending energy consumption quantification data to the device controller based on a standard industrial communication protocol, and the controller controlling the operation of the device based on the energy consumption quantification data.

[0010] For example, meteorological data is obtained by calling a meteorological interface, while device data is obtained based on the device's built-in sensors. Another embodiment of this application provides a building energy consumption quantification system, which includes: a first acquisition module for acquiring passenger flow data, meteorological data, and equipment data of a building area; a quantification module for quantifying the passenger flow data, meteorological data, and equipment data to obtain a passenger flow coefficient, a meteorological coefficient, and an equipment efficiency coefficient; a second acquisition module for acquiring basic energy consumption data of the building area under standard operating conditions; and a processing module for processing the basic energy consumption data based on the passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient to obtain quantified energy consumption data of the building area.

[0011] Another embodiment of this application provides an electronic device having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0012] Another embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0013] In the above embodiments, the building energy consumption quantification method includes: acquiring passenger flow data, meteorological data, and equipment data of the building area; quantifying the passenger flow data, meteorological data, and equipment data to obtain passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient; acquiring basic energy consumption data of the building area under standard operating conditions; and processing the basic energy consumption data based on the passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient to obtain quantified energy consumption data of the building area. By simultaneously collecting passenger flow data, meteorological data, and equipment operation data of the building area, and uniformly quantifying the above multi-source data to construct passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient respectively, and combining them with the basic energy consumption data of the building area under standard operating conditions for coupling correction, the comprehensive impact of different passenger flow loads, meteorological environments, and equipment operating states on building energy consumption can be accurately quantified. This achieves refined and quantitative characterization of building area energy consumption, effectively improves the accuracy and objectivity of energy consumption assessment, and provides reliable data support and quantitative basis for building energy conservation control, energy consumption optimization, and intelligent operation and maintenance. Attached Figure Description

[0014] Figure 1 A flowchart of a building energy consumption quantification method provided for embodiments of this application; Figure 2 A flowchart of another building energy consumption quantification method provided for the embodiments of this application; Figure 3 A block diagram of a building energy consumption quantification system provided for another embodiment of this application; Figure 4 A block diagram of an electronic device provided for another embodiment of this application. Detailed Implementation

[0015] The embodiments of this application are described in detail below. Examples of the 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.

[0016] In the refined management of energy consumption in commercial buildings, the core challenge is how to accurately quantify the real-time energy consumption demand of a specific area (such as a shop or a restaurant) in the near future.

[0017] Related technologies estimate energy consumption based on fixed weights or simple linear regression, setting a basic energy consumption value for a certain area of ​​the building, and then making linear adjustments based on 1-2 main factors (such as outdoor temperature). For example, energy consumption = basic energy consumption + temperature coefficient × (outdoor temperature - reference temperature). However, this method has the following disadvantages: (1) Single data dimension, resulting in large prediction deviation: only temperature is considered, while key factors such as passenger flow density and equipment aging are ignored. This leads to a serious underestimation of energy consumption during holidays (high passenger flow but suitable temperature), which cannot meet actual needs. (2) Isolated calculation between factors leads to failure to reflect coupling effect: each influencing factor is treated as an independent variable and simply superimposed, ignoring the coupling relationship between them. For example, when high passenger flow (increased internal heat source) and high summer temperature (increased external heat source) occur at the same time, the resulting increase in cooling demand is non-linearly superimposed, which cannot be accurately described by a simple linear model. (3) Fixed and rigid weights lead to poor adaptability: The weight coefficients in the energy consumption estimation model are pre-set and fixed, which cannot adapt to the dynamic changes in the building's characteristics at different operating stages and in different functional areas (such as restaurants vs. bookstores), resulting in a long-term decline in accuracy.

[0018] The aforementioned technologies cannot effectively integrate the three dynamically changing heterogeneous dimensions of data: passenger flow, seasonal climate, and equipment performance. This leads to a disconnect between energy supply and actual demand, resulting in either waste due to "oversupply" or a decline in the user experience due to "insufficient energy."

[0019] The objective of this application is to overcome the aforementioned shortcomings and provide a precise, dynamic, and multi-factor fusion method for quantifying energy consumption demand. Through an innovative calculation model, it dynamically fuses multi-source data to calculate energy consumption values ​​that are closer to actual needs, thus providing a data foundation for precise regulation.

[0020] In view of this, the embodiments of this application provide a method for quantifying building energy consumption, based on a multi-source dynamic coefficient fusion calculation model, for accurately quantifying the real-time energy consumption demand of building functional areas. This method can: (1) fuse multi-source heterogeneous data: incorporate dynamic data of three dimensions, namely passenger flow, seasonal climate, and equipment status, into a unified model. (2) quantify the coupling relationship between factors: describe the nonlinear growth effect under the combined action of multiple factors more accurately through a model of coefficient multiplication rather than addition. (3) output accurate demand quantification values: calculate a dynamic and reasonable "energy consumption demand instruction" for each spatial unit, thereby realizing on-demand energy supply and achieving a closed loop from data perception to demand calculation.

[0021] Figure 1 A flowchart illustrating the building energy consumption quantification method provided for embodiments of this application.

[0022] like Figure 1As shown, the building energy consumption quantification method 100 provided in this application includes, for example, steps S110-S140.

[0023] Step S110: Obtain passenger flow data, meteorological data, and equipment data for the building area. For example, passenger flow data (used to reflect the impact of the intensity of human activity within the building area on energy consumption) may include video stream data obtained by an image acquisition device (such as a camera) collecting passenger flow data in the building area, or probe data (such as connection time, connection location, connection duration, etc.) identifying wireless networks connected to the building area (such as mobile phones, tablets, etc.), meteorological data (such as temperature data, humidity data, and solar radiation intensity data, etc.) used to reflect the interference of external environmental conditions on building energy consumption, and equipment data (such as runtime data, refrigerant pressure data, etc.) used to reflect the effect of the operating status of energy-consuming equipment inside the building on energy consumption. Step S120: Quantify the passenger flow data, meteorological data, and equipment data to obtain the passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient. For example, the passenger flow data can be quantified by determining the number of people in the building area in real time or within a time period based on the collected video stream data and / or probe data, and calculating the ratio of the number of people to the baseline number of people (determined based on the design specification data or historical passenger flow data of the building area). This ratio can be directly used as the passenger flow coefficient K_passenger flow (K_people flow). This passenger flow coefficient can intuitively and quantitatively represent the difference between the actual passenger flow load and the baseline passenger flow load of the building area. At least one of the collected temperature, humidity, and solar radiation intensity data is matched and mapped with a preset mapping table (used to clarify the correspondence between at least one of the temperature, humidity, and solar radiation intensity data and the mapping rule data) to obtain the corresponding mapping rule data (preset based on the degree of influence of meteorological parameters on building energy consumption). The meteorological coefficient K_meteorology (K_season) can be calculated based on the mapping rule data. Based on the collected refrigerant pressure and runtime data, and based on a preset equipment performance degradation model (e.g., equipment operating efficiency decreases by 0.8% for every 1000 hours of operation, or based on the degree of deviation of refrigerant pressure data from the rated refrigerant pressure data), the efficiency degradation corresponding to runtime data and the efficiency degradation corresponding to refrigerant pressure data are calculated respectively. The weights of the two are then combined to obtain equipment degradation data. The equipment degradation data is then quantified to obtain the equipment efficiency coefficient K_equipment. The equipment efficiency coefficient is used to quantitatively characterize the difference between the actual operating efficiency of the equipment and the standard operating efficiency. The smaller the degree of equipment performance degradation, the closer the equipment efficiency coefficient is to 1, and vice versa. This achieves accurate quantification of the impact of equipment operating status on building energy consumption.

[0024] Step S130: Obtain basic energy consumption data of the building area under standard operating conditions. For example, a standard operating condition refers to a preset ideal operating condition that reflects the normal operating state of a building and excludes various interference factors. The basic energy consumption data is the energy consumption benchmark of the building area under this standard operating condition, providing a reference benchmark for subsequent energy consumption correction in conjunction with various quantitative coefficients, and ensuring the comparability and rationality of energy consumption quantification results. The standard operating condition can be preset and determined according to parameters such as the functional type of the building area (e.g., residential building, commercial building, office building, etc.), building scale, and energy-consuming equipment configuration. Specifically, it may include preset standard environmental meteorological conditions (e.g., standard temperature, standard humidity, etc.), standard passenger flow load (e.g., rated passenger flow, standard stay time, etc.), and standard equipment operating status (e.g., rated operating power, standard operating time, fault-free operation, etc.). Basic energy consumption data can be obtained in the following ways: First, by collecting and statistically analyzing energy consumption data over a period of time through actual operational testing of the building area under standard operating conditions, this data can be used as basic energy consumption data. Second, based on the building's design parameters, the rated energy consumption parameters of energy-consuming equipment, and the parameters of standard operating conditions, basic energy consumption data can be obtained through theoretical calculations. Third, by retrieving energy consumption records from the building area's historical operation that conform to standard operating conditions, and then filtering and averaging them, basic energy consumption data can be obtained. Specifically, basic energy consumption data may include the total energy consumption per unit time of the building area and the individual energy consumption of various types of energy-consuming equipment.

[0025] Step S140: Process the basic energy consumption data based on passenger flow coefficient, meteorological coefficient and equipment efficiency coefficient to obtain quantitative energy consumption data of the building area. For example, the basic energy consumption data can be processed using a coefficient weighted correction method. Specifically, according to the influence weight of various quantitative coefficients on building energy consumption, corresponding weight coefficients can be set. The passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient are multiplied by the corresponding weight coefficients and then summed to obtain a comprehensive correction coefficient. The basic energy consumption data is then multiplied by this comprehensive correction coefficient to obtain the energy consumption quantitative data of the building area.

[0026] In the above embodiments, by simultaneously collecting passenger flow data, meteorological data, and equipment operation data of the building area, and performing unified quantitative processing on the above multi-source data, passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient are constructed respectively. Combined with the basic energy consumption data under the standard operating conditions of the building area for coupling correction, the comprehensive impact of different passenger flow loads, meteorological environment, and equipment operation status on building energy consumption can be accurately quantified. This achieves a refined and quantitative characterization of building area energy consumption, effectively improves the accuracy and objectivity of energy consumption assessment, and provides reliable data support and quantitative basis for building energy conservation control, energy consumption optimization, and intelligent operation and maintenance.

[0027] In one example, passenger flow data includes video stream data collected from passenger flow in the building area using an image acquisition device and probe data identifying connections to the building area's wireless network. The passenger flow data is then quantified to obtain a passenger flow coefficient, including: acquiring baseline passenger flow data, which is determined based on design specifications or historical passenger flow data; determining the number of people in the building area based on the video stream data and / or probe data; and determining the ratio of the passenger flow data to the baseline passenger flow data, using this ratio as the passenger flow coefficient.

[0028] For example, image acquisition devices (such as cameras) deployed at key locations such as building entrances and public areas can be used to collect real-time passenger flow data in the building area, resulting in video stream data containing information such as passenger flow quantity, passenger flow trajectory, and passenger dwell time. Alternatively, wireless probe devices deployed in the building area can be used to collect and identify probe data from terminal devices (such as mobile phones, tablets, etc.) connected to the building area's wireless network. Passenger flow related information in the building area can then be calculated based on the probe data. The probe data may include the terminal device's connection time, connection location, connection duration, etc., which can indirectly reflect the distribution and activity of passenger flow.

[0029] Specifically, the baseline passenger flow data can be determined based on the design specifications of the building area, i.e., according to the rated passenger flow standard preset during the building design phase; or it can be determined based on historical passenger flow data of the building area, i.e., by retrieving passenger flow statistics from the building area during its past normal operation, and then processing them through screening and averaging to determine the baseline passenger flow data. Based on the collected video stream data and / or probe data, the real-time or time-period passenger flow data of the building area is determined. Specifically, passenger flow data can be obtained by extracting passenger flow information from video stream data using image recognition algorithms, while passenger flow data can be obtained by converting the number of terminal devices based on probe data. The ratio of the passenger flow data to the baseline passenger flow data is calculated, and this ratio is directly used as the passenger flow coefficient. This passenger flow coefficient can intuitively quantify the difference between the actual passenger flow load and the baseline passenger flow load of the building area. When the actual passenger flow data is greater than the baseline passenger flow data, the passenger flow coefficient is greater than 1, indicating that the actual passenger flow load is higher than the baseline, and the impact on building energy consumption is greater; when the actual passenger flow data is less than the baseline passenger flow data, the passenger flow coefficient is less than 1, indicating that the actual passenger flow load is lower than the baseline, and the impact on building energy consumption is smaller.

[0030] For example, by analyzing the video stream from cameras or Wi-Fi probe data at the entrance of a building area, the number of people in the area can be counted in real time (personnel data). The calculation is performed according to formula (1). The baseline number of people is derived from the statistical value of the area's design specifications or historical operation data (historical passenger flow data) (e.g., the baseline number of people in the catering area is 100).

[0031] K_Passenger Flow = Number of People / Baseline Number of People in the Area (1) In one example, meteorological data includes temperature data, humidity data, and solar radiation intensity data; the meteorological data is quantified to obtain meteorological coefficients, including: mapping at least one of the temperature data, humidity data, and solar radiation intensity to a preset mapping table to obtain mapping rule data, wherein the preset mapping table represents the correspondence between at least one of the temperature data, humidity data, and solar radiation intensity and the mapping rule data; and the meteorological coefficients are obtained based on the mapping rule data. Specifically, a preset mapping table is used to clarify the correspondence between at least one of the temperature data, humidity data, and solar radiation intensity data and the mapping rule data. The mapping rule data is preset and determined based on the degree of influence of meteorological parameters on building energy consumption (for example, when the temperature data T is 30°C, the mapping rule data is 0.05*(T-26)). At least one of the collected temperature data, humidity data, and solar radiation intensity data is matched and mapped with the preset mapping table to obtain the corresponding mapping rule data. Finally, based on the mapping rule data, a meteorological coefficient is calculated. The meteorological coefficient is used to quantitatively characterize the comprehensive influence of meteorological parameters such as temperature, humidity, and solar radiation intensity on building energy consumption, thereby achieving accurate quantification of the impact of meteorological factors on building energy consumption.

[0032] For example, by calling the meteorological bureau's API (Application Programming Interface), real-time outdoor temperature and humidity (temperature data, humidity data), and solar radiation intensity data can be obtained. Based on a preset mapping table, this data is converted into a meteorological coefficient K_meteorology. For example, the mapping rule data could be: when the temperature data T>26°C, K_meteorology = 1 + (T-26)*0.05. Substituting the temperature data into the mapping rule data will yield the meteorological coefficient K_meteorology.

[0033] In one example, the equipment data includes runtime data and refrigerant pressure data; the equipment data is quantified to obtain the equipment efficiency coefficient, including: determining equipment degradation data based on refrigerant pressure data and runtime data; and quantifying the equipment data based on the equipment degradation data to obtain the equipment efficiency coefficient.

[0034] Specifically, by reading sensor data, the cumulative runtime (runtime data) and refrigerant pressure data of equipment (such as air conditioning units) can be obtained. The cumulative runtime data is calculated by the sensors in real time, accumulating the runtime after each start-up and summing it to obtain the total cumulative runtime (unit: hours). The refrigerant pressure data is obtained by the sensors in real time, collecting the actual pressure value (unit: MPa) within the equipment's refrigeration circuit, and simultaneously recording the equipment's operating conditions (such as cooling / heating mode) at the time of collection to ensure the relevance of the pressure data. Secondly, based on a preset equipment performance degradation model, the efficiency degradation corresponding to the cumulative runtime and the efficiency degradation corresponding to the refrigerant pressure are calculated separately. The weights of these two factors are then combined to obtain the equipment degradation data. Finally, the equipment degradation data is quantified to obtain the equipment efficiency coefficient K_equipment.

[0035] For example, the efficiency decay corresponding to the cumulative running time is calculated as follows: the preset decay rule is "the equipment operating efficiency decays by 0.8% for every 1000 hours of operation of the air conditioning unit". The calculation formula is shown in formula (2). If the cumulative running time is 10000 hours, the time decay = (10000÷1000)×0.8%=8%.

[0036] Duration attenuation (%) = (Cumulative runtime ÷ 1000) × 0.8% (2) Calculation of efficiency reduction corresponding to refrigerant pressure: The rated refrigerant pressure range of the air conditioning unit is preset (e.g., 0.4-0.6MPa). If the actual collected refrigerant pressure is within the rated range, the pressure reduction is 0%. If the actual pressure deviates from the rated range, the efficiency will decrease by 1% for every 0.1MPa deviation. The calculation formula is shown in formula (3). The average rated refrigerant pressure is 0.5MPa. The actual collected pressure is 0.3MPa. If it deviates by 0.2MPa, the pressure reduction is 0.2÷0.1×1%=2%.

[0037] Pressure decay (%) = |Actual refrigerant pressure - Average rated refrigerant pressure| ÷ 0.1MPa × 1% (3) Equipment attenuation data calculation: Set the cumulative running time attenuation weight to 0.7 and the refrigerant pressure attenuation weight to 0.3 (the weights can be adjusted according to the actual equipment characteristics), equipment attenuation data (%) = time attenuation × 0.7 + pressure attenuation × 0.3; combined with the above time attenuation of 8% and pressure attenuation of 2%, equipment attenuation data = 8% × 0.7 + 2% × 0.3 = 5.6% + 0.6% = 6.2%. Finally, based on the equipment attenuation data and combined with the equipment rated operating efficiency benchmark (rated efficiency is recorded as 1), the current equipment efficiency coefficient K_equipment is calculated, and the calculation formula is shown in formula (4): K_device = 1 - equipment attenuation data (4) Based on the calculated equipment degradation data of 6.2%, K_equipment = 1 - 0.062 = 0.938. K_equipment is used to quantitatively characterize the difference between the actual operating efficiency and the standard operating efficiency of the air conditioning unit. The smaller the degree of equipment performance degradation, the closer K_equipment is to 1, and vice versa, the greater the deviation from 1, thus achieving accurate quantification of the impact of equipment operating status on building energy consumption.

[0038] For example, by reading data from the built-in sensors of the air conditioning unit, parameters such as its cumulative operating time (operating time data) and refrigerant pressure data can be obtained. Based on the equipment performance degradation model (e.g., efficiency decreases by 0.8% for every 1000 hours of operation), the current K_equipment (e.g., 0.92) can be calculated.

[0039] In the above embodiments, three completely different types of physical quantities (passenger flow data, equipment data, and meteorological data) are uniformly transformed into dimensionless scaling factors (passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient) to provide standardized input for subsequent fusion calculations.

[0040] In one example, basic energy consumption data is processed based on passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient to obtain quantitative energy consumption data for the building area. This includes performing continuous multiplication operations on the basic energy consumption data with passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient to obtain quantitative energy consumption data for the building area.

[0041] Specifically, the basic energy consumption data can be processed using a coefficient weighted correction method (multiplication model). Specifically, according to the influence weight of various quantitative coefficients on building energy consumption, corresponding weight coefficients can be set. The passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient are multiplied by the corresponding weight coefficients and then summed to obtain a comprehensive correction coefficient. The basic energy consumption data is then multiplied by this comprehensive correction coefficient to obtain the energy consumption quantitative data of the building area.

[0042] The computing engine reads the basic energy consumption data (E_base) of the spatial unit under standard operating conditions from the database (e.g., E_base = 10kW for a standard shop). Then, the computing engine performs the following arithmetic logic operations, as shown in formula (5): E_real = E_base × K_passenger flow × K_weather × K_equipment (5) For example, during the peak lunch hour on a weekend in a certain catering area, the calculated K_customer flow = 1.8 (customer flow exceeds the benchmark by 80%); the day is extremely hot, K_weather = 1.3 (outdoor temperature 33 degrees Celsius); the equipment is in good condition, K_equipment = 0.98. Then, its real-time demand (energy consumption quantification data E_real) is: E_real = 10kW × 1.8 × 1.3 × 0.98 ≈ 22.93kW.

[0043] In the above embodiments, the nonlinear demand growth under the combined effect of multiple factors is accurately quantified by the multiplication model. The three types of quantification coefficients obtained are coupled with the basic energy consumption data to realize the correction of the basic energy consumption data, thereby obtaining energy consumption quantification data that can truly reflect the actual operating status of the building area, eliminating the interference of factors such as passenger flow, weather, and equipment operating status on energy consumption, and improving the accuracy of energy consumption quantification.

[0044] In another example, a weighted additive model can be used instead, for example: E_real = E_base + W1 *(K_occupancy-1) + W2 * (K_season-1). Where W1 and W2 are weights, K_occupancy represents the passenger flow coefficient (K_passenger flow), and K_season represents the weather coefficient (K_weather).

[0045] However, this scheme is partially feasible and can reflect the influence of multiple factors. But the additive model is difficult to accurately describe the coupling amplification effect (nonlinearity) between factors, and its calculation accuracy is not as good as the multiplicative model under extreme conditions.

[0046] In one example, the building energy consumption quantification method also includes: sending energy consumption quantification data to the device's controller based on a standard industrial communication protocol, and the controller controlling the device's operation based on the energy consumption quantification data.

[0047] Specifically, the calculated energy consumption quantification data E_real value (for example, 22.93kW) is sent to the controller of the air conditioning unit in the building area through standard industrial communication protocols (such as Modbus TCP protocol, BACnet protocol and other commonly used industrial communication protocols). After receiving the E_real value, the controller generates control commands based on the equipment operating characteristics and sends them to the actuators. The actuators adjust the equipment operating status according to the commands (for example, setting its operating power to 23kW), together to achieve precise operation control of the air conditioning unit and ensure that the energy consumption optimization target is achieved.

[0048] In the above embodiments, the algorithm calculation results are transformed into specific, executable physical control commands, completing the closed loop from "data" to "action" and realizing precise energy supply.

[0049] In one example, meteorological data is obtained by calling a meteorological interface, while device data is obtained based on the device's built-in sensors.

[0050] Specifically, meteorological data is obtained by calling meteorological interfaces. These interfaces are official or standardized interfaces with meteorological data release qualifications (such as meteorological bureau API interfaces, Application Programming Interfaces). Through these interfaces, accurate and real-time meteorological data of the building area's surroundings can be directly obtained without deploying additional complex meteorological monitoring equipment, effectively reducing data collection costs. At the same time, the authority and timeliness of the interface ensure the accuracy and reliability of the meteorological data. Equipment data is obtained based on the built-in sensors of the equipment. Specifically, energy-consuming equipment inside the building (especially air conditioning units) is equipped with built-in sensors. These built-in sensors are directly integrated into the core operating components of the equipment and can capture various key parameters during equipment operation in real time, eliminating the need for additional external sensors, reducing the workload of equipment modification, and ensuring the timeliness and accuracy of data collection. By reading the acquisition signals of these built-in sensors, core equipment operating parameters such as the air conditioning unit's operating time and refrigerant pressure data can be obtained, providing reliable data support for subsequent quantitative processing of equipment data and calculation of equipment efficiency coefficients.

[0051] In another example, the building energy consumption quantification method can be extended to other resource demand quantification scenarios, such as building water consumption prediction (basic water volume × passenger flow coefficient × weather coefficient) and electricity load prediction. Only the corresponding dynamic coefficients need to be replaced, which reflects the versatility and scalability of the method.

[0052] Figure 2 Another flowchart of a building energy consumption quantification method provided for the implementation of this application is shown below. Figure 2 As shown, the methods for quantifying building energy consumption include S201-S203.

[0053] S201, the data perception and quantification layer collects and quantifies data to obtain passenger flow coefficient, seasonal coefficient and equipment efficiency coefficient.

[0054] For example, the data perception and coefficient quantification layer collects passenger flow data (including video stream data and / or probe data) from passenger flow sensors, quantifies the passenger flow data, determines the number of people in the building area in real time or within a time period, calculates the ratio of the number of people to the baseline number of people (determined according to the design specification data or historical passenger flow data of the building area), and directly uses this ratio as the passenger flow coefficient K_passenger flow (K_people flow). This passenger flow coefficient can intuitively quantify the difference between the actual passenger flow load and the baseline passenger flow load in the building area. The data perception and coefficient quantification layer acquires meteorological data (at least one of temperature, humidity, and solar intensity data) through a meteorological API and performs matching and mapping processing with a preset mapping table (used to clarify the correspondence between at least one of temperature, humidity, and solar intensity data and mapping rule data) to obtain the corresponding mapping rule data (preset based on the degree of influence of meteorological parameters on building energy consumption). The meteorological coefficient (seasonal coefficient) K_meteorology (K_seasonal) is calculated based on the mapping rule data. The data perception and coefficient quantification layer collects device data (refrigerant pressure data and operating time data) from IoT (Internet of Things Sensor) sensors. Based on a preset device performance degradation model (e.g., 0.8% efficiency degradation per 1000 hours of operation, or the degree to which refrigerant pressure data deviates from the rated refrigerant pressure data), it calculates the efficiency degradation corresponding to the operating time data and the efficiency degradation corresponding to the refrigerant pressure data, respectively. Combining the weights of both, it obtains the device degradation data, which is then quantified to obtain the device efficiency coefficient K_device.

[0055] S202, the core computing engine calls the basic energy consumption value, and performs a fusion calculation on the basic energy consumption value based on the passenger flow coefficient, seasonal coefficient and equipment efficiency coefficient to obtain energy consumption quantification data.

[0056] For example, the core computing engine acquires basic energy consumption data (basic energy consumption value) of the building area under standard operating conditions. Standard operating conditions refer to preset ideal operating conditions that reflect the normal operation of the building and exclude various interference factors. Basic energy consumption data serves as the energy consumption benchmark for the building area under these standard operating conditions. The basic energy consumption data is processed based on passenger flow coefficients, meteorological coefficients, and equipment efficiency coefficients to obtain quantitative energy consumption data for the building area. The processing of basic energy consumption data can employ a coefficient-weighted correction method. Specifically, based on the influence weight of various quantitative coefficients on building energy consumption, corresponding weight coefficients are set. The passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient are multiplied by their corresponding weight coefficients and summed to obtain a comprehensive correction coefficient. The basic energy consumption data is then multiplied by this comprehensive correction coefficient to obtain the quantitative energy consumption data for the building area.

[0057] S203, the output and application layer receive and output the real-time control energy consumption value, and send the real-time control energy consumption value to the air conditioning unit actuator and the fresh air unit actuator for control.

[0058] For example, the output and application layer receives and outputs real-time energy consumption values ​​(energy consumption quantification data). The calculated energy consumption quantification data E_real value (for example, 22.93kW) is sent to the controller of the air conditioning unit in the building area through standard industrial communication protocols (such as Modbus TCP protocol, BACnet protocol, and other commonly used industrial communication protocols). After receiving the E_real value, the controller generates control commands based on the equipment operating characteristics and sends them to the actuators. The actuators adjust the equipment operating status according to the commands (for example, setting its operating power to 23kW), jointly realizing the precise operation control of the air conditioning unit and ensuring that the energy consumption optimization target is achieved.

[0059] In the above embodiments, data perception and coefficient transformation (input layer) acquires raw data in real time from three different types of sensor or data source interfaces (passenger flow sensor, meteorological API, and device IoT sensor), and transforms it into standardized dynamic coefficients (K_passenger flow, K_season, K_device) through specific calculation rules, unifying heterogeneous data into calculable dimensions. The core computing engine (fusion computing) calls the basic energy consumption value (E_base) of the spatial unit and multiplies it with the three dynamic coefficients to obtain the final real-time control energy consumption value (E_real). The multiplication relationship is used to express the coupling amplification effect between multiple factors. Output and application (execution layer): The calculated E_real value is used as a control command, output and sent to the corresponding energy-consuming device actuators (such as air conditioners and fresh air units), thereby realizing precise on-demand control of the equipment.

[0060] The building energy consumption quantification method proposed in this application achieves the following: (1) Coefficient fusion of multi-source heterogeneous data: Passenger flow, seasonal climate, and equipment status, three types of dynamic data with different sources and different properties, are incorporated into a unified and calculable energy demand model through coefficient processing, solving the problem of single data dimension. (2) Nonlinear demand coupling based on multiplication relationship: The innovative use of multiplication (×) rather than superposition (+) model to describe the combined effect of multiple factors, more scientifically quantifies the coupling effect between factors, and significantly improves the accuracy of demand calculation under complex working conditions (such as high passenger flow + high temperature). (3) Demand quantification for precise control: The output of the model is directly an "energy consumption command" that can be executed, realizing a direct and precise mapping from "sensory data" to "control command", providing core algorithm support for on-demand energy supply.

[0061] The building energy consumption quantification system and method proposed in this application is a specific mathematical model for calculating building energy consumption demand. This model solves the quantification problem of multi-source dynamic data fusion and nonlinear coupling relationship through multi-coefficient multiplication, which is a key algorithmic breakthrough for realizing intelligent control of building energy conservation.

[0062] Figure 3 A block diagram of a building energy consumption quantification system provided for another embodiment of this application.

[0063] This specification provides a building energy consumption quantification system 300. Please refer to [link / reference]. Figure 3 The building energy consumption quantification system 300 includes: a first acquisition module 310, a quantification module 320, a second acquisition module 330, and a processing module 340.

[0064] The first acquisition module 310 is used to acquire passenger flow data, meteorological data and equipment data of the building area. The quantization module 320 is used to quantify passenger flow data, meteorological data, and equipment data to obtain passenger flow coefficient, meteorological coefficient, and equipment efficiency coefficient. The second acquisition module 330 is used to acquire basic energy consumption data of the building area under standard operating conditions.

[0065] The processing module 340 is used to process basic energy consumption data based on passenger flow coefficient, meteorological coefficient and equipment efficiency coefficient to obtain quantitative energy consumption data of the building area.

[0066] For example, the passenger flow data includes video stream data obtained by collecting passenger flow data in the building area based on the image acquisition device and probe data that identifies the connection to the wireless network in the building area; the quantization module 320 is also used to obtain baseline passenger flow data, wherein the baseline passenger flow data is determined based on design specification data or historical passenger flow data; determine the passenger flow data in the building area based on the video stream data and / or probe data; determine the ratio data of the passenger flow data to the baseline passenger flow data, and use the ratio data as the passenger flow coefficient. For example, the meteorological data includes temperature data, humidity data, and solar radiation intensity data; the quantization module 320 is further used to map at least one of the temperature data, humidity data, and solar radiation intensity to a preset mapping table to obtain mapping rule data, wherein the preset mapping table represents the correspondence between at least one of the temperature data, humidity data, and solar radiation intensity and the mapping rule data; and based on the mapping rule data, a meteorological coefficient is obtained. For example, the equipment data includes runtime data and refrigerant pressure data; the quantization module 320 is also used to determine equipment degradation data based on the refrigerant pressure data and runtime data; and to quantize the equipment data based on the equipment degradation data to obtain the equipment efficiency coefficient. For example, the processing module 340 is also used to perform continuous multiplication of basic energy consumption data with passenger flow coefficient, meteorological coefficient and equipment efficiency coefficient to obtain energy consumption quantitative data of the building area.

[0067] For example, the building energy consumption quantification system 300 further includes a control module for sending energy consumption quantification data to the device's controller based on a standard industrial communication protocol, so that the controller can control the operation of the device based on the energy consumption quantification data.

[0068] For example, meteorological data is obtained by calling a meteorological interface, while device data is obtained based on the device's built-in sensors. Figure 4 A block diagram of an electronic device provided for another embodiment of this application.

[0069] Another embodiment of this application provides an electronic device having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0070] like Figure 4 As shown, for ease of understanding, embodiments of this application illustrate a specific electronic device 400.

[0071] Electronic device 400 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 400 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0072] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0073] Multiple components in electronic device 400 are connected to input / output (I / O) interface 405. These components include: input unit 406, such as a keyboard or mouse; output unit 407, such as various types of displays or speakers; storage unit 408, such as a hard disk or optical disk; and communication unit 409, such as a network interface card (NIC), modem, or wireless transceiver. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0074] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods described above. For example, in some embodiments, any one or more of the various methods described above can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of any one or more of the various methods described above can be performed. Alternatively, in other embodiments, the computing unit 401 can be configured to perform any one or more of the various methods described above by any other suitable means (e.g., by means of firmware).

[0075] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0076] It should be noted that 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 specifically implemented 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 application, "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: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), 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). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0077] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination 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.

[0078] In the description of this application, 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 application, 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.

[0079] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0080] Furthermore, the terms "first," "second," etc., used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of technical features indicated in this embodiment. Therefore, features defined with terms such as "first" and "second" in the embodiments of this application can explicitly or implicitly indicate that the embodiment includes at least one of those features. In the description of this application, the word "multiple" means at least two or more, such as two, three, four, etc., unless otherwise explicitly and specifically defined in the embodiments.

[0081] In this application, unless otherwise explicitly specified or limited in the embodiments, the terms "installation," "connection," "joining," and "fixing" appearing in the embodiments should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral part; it can also be a mechanical connection, an electrical connection, etc. Of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication between two components, or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific implementation.

[0082] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0083] 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 energy consumption quantification method, characterized in that, The method includes: Acquire passenger flow data, meteorological data, and equipment data for the building area; The passenger flow data, the meteorological data, and the equipment data are quantified to obtain the passenger flow coefficient, the meteorological coefficient, and the equipment efficiency coefficient. Obtain basic energy consumption data for the building area under standard operating conditions; The basic energy consumption data is processed based on the passenger flow coefficient, the meteorological coefficient, and the equipment efficiency coefficient to obtain the quantitative energy consumption data of the building area.

2. The method of claim 1, wherein, The passenger flow data includes video stream data obtained by collecting passenger flow data in the building area based on the image acquisition device, and probe data identifying connections to the wireless network in the building area; The process of quantifying the passenger flow data to obtain the passenger flow coefficient includes: Obtain baseline passenger flow data, wherein the baseline passenger flow data is determined based on design specification data or historical passenger flow data; Based on the video stream data and / or the probe data, determine the number of people in the building area; The ratio of the number of people to the baseline number of people is determined, and the ratio is used as the passenger flow coefficient.

3. The method of claim 1, wherein, The meteorological data includes temperature data, humidity data, and solar radiation intensity data; the quantification of the meteorological data to obtain meteorological coefficients includes: At least one of the temperature data, humidity data, and solar radiation intensity is mapped to a preset mapping table to obtain mapping rule data, wherein the preset mapping table represents the correspondence between at least one of the temperature data, humidity data, and solar radiation intensity and the mapping rule data; Based on the mapping rule data, meteorological coefficients are obtained.

4. The method of claim 1, wherein, The equipment data includes runtime data and refrigerant pressure data; the quantification of the equipment data to obtain the equipment efficiency coefficient includes: Based on the cooling pressure data and the running time data, determine the equipment degradation data; The equipment data is quantified based on the equipment attenuation data to obtain the equipment efficiency coefficient.

5. The method of claim 1, wherein, The process of processing the basic energy consumption data based on the passenger flow coefficient, the meteorological coefficient, and the equipment efficiency coefficient to obtain the quantitative energy consumption data of the building area includes: The energy consumption data of the building area is obtained by performing continuous multiplication operations with the passenger flow coefficient, the meteorological coefficient, and the equipment efficiency coefficient.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: The energy consumption quantification data is sent to the device's controller based on a standard industrial communication protocol, and the controller controls the device's operation based on the energy consumption quantification data.

7. The method according to any one of claims 1-5, characterized in that, The meteorological data is obtained by calling the meteorological interface, and the device data is obtained based on the built-in sensors of the device.

8. A building energy consumption quantification system, characterized in that, The system includes: The first acquisition module is used to acquire passenger flow data, meteorological data, and equipment data in the building area; The quantization module is used to quantify the passenger flow data, the meteorological data, and the equipment data to obtain the passenger flow coefficient, the meteorological coefficient, and the equipment efficiency coefficient. The second acquisition module is used to acquire the basic energy consumption data of the building area under standard operating conditions; The processing module is used to process the basic energy consumption data based on the passenger flow coefficient, the meteorological coefficient, and the equipment efficiency coefficient to obtain the energy consumption quantification data of the building area.

9. An electronic device having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-7.

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