Public building energy consumption auditing statistical splitting method and system

Abstract

The invention relates to the technical field of energy management, in particular to a public building energy consumption auditing statistical splitting method and system, and the method comprises the following steps: obtaining the operation time, power data and load change rate of air conditioning, lighting, elevator and office equipment, calculating the power and load contribution rate of the equipment, and constructing an equipment operation characteristic matrix. According to the method, accurate energy consumption distribution and optimization are realized through subdivision analysis and power contribution rate adjustment based on equipment characteristics, the identification accuracy of abnormal energy consumption is improved through detailed analysis of equipment operation data, and energy consumption waste is effectively reduced through fine measures of load adjustment, so that the energy consumption is reduced, and the energy consumption is reduced. According to the optimization measure, the energy consumption distribution is more reasonable, the pertinence and the implementation effect of the energy-saving measure are enhanced, and the efficiency and the effect of energy management are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and particularly to a method and system for auditing, statistics and splitting of energy consumption in public buildings. Background Art

[0002] The technical field of energy management includes the monitoring, optimization and scheduling of energy to improve energy utilization efficiency, reduce energy consumption and waste. The core contents include energy consumption data collection, energy use analysis, energy efficiency evaluation and energy-saving strategy formulation. Energy management involves various energy forms such as electricity, heat, gas and water resources, covering multiple application scenarios such as buildings, industry and transportation. It includes energy consumption data acquisition, storage and processing, combined with mathematical models for energy consumption prediction, allocation and optimization, and achieving energy-saving goals through control strategies. In the building field, energy management mainly involves aspects such as building energy consumption monitoring, district energy supply optimization, load forecasting, energy-saving renovation evaluation and energy efficiency audit, and implements refined energy consumption management by means of sensor networks, intelligent metering, data analysis and automatic control.

[0003] Among them, the auditing, statistics and splitting of energy consumption in public buildings refers to the itemized statistics and auditing of energy consumption data of public buildings to achieve the quantitative splitting of different energy consumption categories. For the main energy consumption categories such as lighting, air conditioning, elevators, and office equipment inside the building, energy consumption data is obtained based on the building energy consumption monitoring system, and classified statistics are carried out according to different functional areas or equipment types. Through data classification, time series analysis and energy consumption model calculation, the energy consumption proportion of each energy consumption category is identified, and abnormal energy consumption data is screened and corrected. Using statistical calculation methods based on measurement data, the overall energy consumption is split into the energy consumption composition of specific equipment or systems, and then a refined energy consumption audit result is formed.

[0004] The prior art has problems of insufficient accuracy in the process of energy consumption statistics and optimization. The acquisition of energy consumption data is mainly based on the collection of energy consumption data of the whole building, lacking the refined analysis of the operating characteristics of different equipment, resulting in difficult accurate attribution during energy consumption splitting and errors in energy consumption anomaly identification. In terms of energy consumption optimization, fixed parameters or empirical values are mostly used for power distribution, ignoring the dynamic characteristics of equipment operation, making it difficult to adapt to load changes, resulting in redundant energy consumption in high-power areas and insufficient energy supply in low-power areas. Load adjustment often relies on a single index such as total power or operating time, lacking in-depth analysis of the equipment operation trend, making the load regulation accuracy limited. In the formulation of energy-saving schemes, the prior art usually based on the energy consumption analysis of a single device or local area, failing to effectively integrate the energy consumption correlation characteristics of multiple regions and multiple devices, resulting in a lack of systematicness in energy-saving schemes and affecting the overall effect of energy-saving optimization. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose a method and system for auditing, statistics and splitting of energy consumption in public buildings.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A method for auditing, statistics and splitting of energy consumption in public buildings, comprising the following steps:

[0007] S1: Obtain the running time, power data and load change rate of air conditioners, lighting, elevators and office equipment, calculate the equipment power and load contribution rate, and construct an equipment operation characteristic matrix;

[0008] S2: Based on the equipment operation characteristic matrix, extract the proportion of equipment power in different regions, calculate the regional power gain adjustment parameter according to the power contribution rate, set a power threshold, calculate the power gain coefficient of high-power regions and the load correction parameter of low-power regions, adjust the energy consumption weight of functional regions, and construct a regional energy consumption distribution coefficient;

[0009] S3: Based on the regional energy consumption distribution coefficient, extract the start time, running power, load change rate and shutdown time of high-energy-consuming equipment, calculate the energy consumption abnormal deviation amount, screen out the equipment with abnormal energy consumption, and obtain the abnormal deviation value of high-energy-consuming equipment;

[0010] S4: Extract the abnormal deviation value of the high-energy-consuming equipment, analyze the load conditions during working and non-working periods, calculate the load growth rate, and obtain the equipment running time sensitivity threshold;

[0011] S5: Based on the equipment running time sensitivity threshold, calculate the load growth rate, screen out the load adjustment equipment, calculate the equipment running time reduction amount or power adjustment range, and obtain the energy consumption adjustment plan for public buildings.

[0012] As a further solution of the present invention, the equipment operation characteristic matrix includes equipment power contribution rate, equipment load contribution rate, equipment power data, and equipment load change rate; the regional energy consumption distribution coefficient includes the power gain coefficient of high-power equipment regions, the regional power gain adjustment parameter, the load correction parameter of low-power equipment, and the energy consumption weight of functional regions; the abnormal deviation value of high-energy-consuming equipment includes equipment start time, equipment running power, equipment load change rate, equipment shutdown time, and energy consumption deviation value; the equipment running time sensitivity threshold includes the load level during working periods, the load level during non-working periods, the load growth rate, and the power change characteristics; the energy consumption adjustment plan for public buildings includes the equipment running time reduction amount, the power adjustment range, the load adjustment equipment, and the running trend deviation value.

[0013] As a further solution of the present invention, the specific steps for obtaining the running time, power data and load change rate of air conditioners, lighting, elevators and office equipment, calculating the equipment power and load contribution rate, and constructing the equipment operation characteristic matrix are as follows:

[0014] S101: Obtain the operating time of air conditioners, lighting, elevators, and office equipment, extract power data and calculate the load change rate, summarize the equipment power data set, and obtain the average power load value;

[0015] S102: Call the average power load value, calculate the equipment power ratio, calculate the power contribution degree based on the total equipment power load, normalize and adjust the power contribution ratio of different equipment, screen the dominant power equipment and summarize the contribution trend, and obtain the power contribution rate of the equipment;

[0016] S103: Call the power contribution rate and the average power load value, calculate the equipment load ratio, summarize the load characteristics of each equipment, identify and verify the equipment load contribution ratio, and form an equipment operation characteristic matrix.

[0017] As a further solution of the present invention, based on the equipment operation characteristic matrix, extract the power ratio of equipment in different regions, calculate the regional power gain adjustment parameter according to the power contribution rate, set a power threshold, calculate the power gain coefficient of the high-power region and the load correction parameter of the low-power region, adjust the energy consumption weight of the functional region, and the specific steps for constructing the regional energy consumption distribution coefficient are:

[0018] S201: Based on the equipment operation characteristic matrix, extract the power ratio of air conditioners, lighting, elevators, and office equipment in different functional regions, and calculate the power gain coefficient of the high-power equipment region;

[0019] S202: Call the power gain coefficient, calculate the regional power gain adjustment parameter according to the equipment power contribution rate, set a power threshold in combination with the power ratio data, call the equipment operation mode change rate of the low-power equipment region to calculate the load correction parameter, summarize the equipment power change characteristics, screen the key load change trends, calculate the load correction ratio and summarize the regional load correction trend, and obtain the load correction parameter of the low-power equipment region;

[0020] S203: Call the power gain coefficient and the load correction parameter, adjust the energy consumption weight of the functional region, and calculate the energy consumption ratio value according to the adjusted energy consumption weight to form a regional energy consumption distribution coefficient.

[0021] As a further solution of the present invention, the specific formula for the energy consumption contribution rate is:

[0022]

[0023] where γ i represents the energy consumption contribution rate of equipment i in the functional region, ψ i represents the load impact factor of equipment i, θ i represents the operating duration of equipment i, ∑(ψj ·θ j ) represents the denominator of the normalized calculation of the total energy consumption of all devices in this area.

[0024] As a further solution of the present invention, based on the regional energy consumption distribution coefficient, extract the start time, operating power, load change rate, and shutdown time of high-energy-consuming devices, calculate the energy consumption abnormal offset, and screen out energy consumption abnormal devices. The specific steps to obtain the abnormal offset value of high-energy-consuming devices are as follows:

[0025] S301: Based on the regional energy consumption distribution coefficient, extract the start time, operating power, load change rate, and shutdown time of high-energy-consuming devices, calculate the device power fluctuation amplitude, and obtain the load change rate of high-energy-consuming devices;

[0026] S302: Call the load change rate of the high-energy-consuming devices, analyze the operating energy consumption of the devices and compare it with the expected energy consumption to obtain the deviation value, screen out the devices with deviations exceeding the set threshold, identify the offset amount outside the range, summarize the offset characteristics of the energy consumption abnormal devices, extract the key offset trends, calculate the offset difference between devices, and screen out the key energy consumption abnormal types to obtain the energy consumption abnormal offset;

[0027] S303: Call the energy consumption abnormal offset, extract the abnormal offset value of the high-energy-consuming devices, and classify the abnormal devices according to the operating status to form the abnormal offset value of the high-energy-consuming devices.

[0028] As a further solution of the present invention, the specific formula for the offset difference is as follows:

[0029]

[0030] Δ = |δ i - δ j |;

[0031] Wherein, δ represents the energy consumption deviation percentage, E actual represents the actually measured energy consumption value, E expected represents the expected energy consumption value calculated based on historical data, τ represents the threshold of the deviation, Δ represents the offset difference between different devices, δ i and δ j represent the energy consumption deviation rates of different devices.

[0032] As a further solution of the present invention, extract the abnormal offset value of the high-energy-consuming devices, analyze the load conditions during working and non-working periods, calculate the load growth rate, and the specific steps to obtain the device operation time sensitivity threshold are as follows:

[0033] S401: Based on the abnormal offset value of the high-energy-consuming devices, extract the device load conditions during the working period and the non-working period, calculate the load growth rate of each period, and obtain the device load result;

[0034] S402: Call the device load result, calculate the change in the device operation trend, obtain the device power change rate by comparing the power fluctuations, screen the fluctuation range of high-load devices, summarize the power change characteristics of the device operation mode, calculate the device load adjustment ratio, screen the key load change points, determine the key load fluctuation type, and obtain the device power change characteristics;

[0035] S403: Call the device power change characteristics, calculate the change range of the device operation time, and summarize the operation time change interval based on the load fluctuations to form the device operation time sensitivity threshold.

[0036] As a further solution of the present invention, based on the device operation time sensitivity threshold, calculate the load growth rate, screen the load adjustment devices, calculate the device operation time reduction amount or the power adjustment range, and the specific steps for obtaining the energy consumption adjustment plan for public buildings are as follows:

[0037] S501: Based on the device operation time sensitivity threshold, extract the operation time of air conditioners, lighting, elevators, and office equipment, calculate the load growth rate, and summarize the load change trend in multiple time periods to obtain the device load growth rate;

[0038] S502: Call the device load growth rate, screen the load adjustment devices by comparing the current operation trend offset value, calculate the device operation time reduction amount, and calculate the power adjustment range in combination with the power fluctuation amplitude, summarize the power change trend of the differential operation mode, screen the key power adjustment intervals, calculate the device load adjustment ratio, and obtain the device operation time reduction amount and the power adjustment range;

[0039] S503: Call the device operation time reduction amount and the power adjustment range, calculate the overall load of public buildings, and summarize the device adjustment parameters to form the energy consumption adjustment plan for public buildings.

[0040] A public building energy consumption audit and statistics splitting system, comprising:

[0041] The device operation characteristic module obtains the operation time of air conditioners, lighting, elevators, and office equipment in public buildings, calculates the device power contribution rate and the device load contribution rate, and constructs a device operation characteristic matrix;

[0042] The energy consumption allocation and adjustment module, based on the device operation characteristic matrix, extracts the power ratios of air conditioners, lighting, elevators, and office equipment in different functional areas, calculates the power gain coefficient of high-power device areas, calculates the regional power gain adjustment parameters according to the device power contribution rate, sets the power threshold, and adjusts the energy consumption weights of the functional areas according to the high-power area power gain coefficient and the low-power area load correction parameters to construct a regional energy consumption allocation coefficient;

[0043] The abnormal energy consumption monitoring module extracts the startup time, operating power, load change rate, and shutdown time of high-energy-consuming devices based on the regional energy consumption distribution coefficient, calculates the energy consumption abnormal offset, and obtains the abnormal offset value of high-energy-consuming devices according to the deviation value between the device energy consumption and the expected energy consumption of the device.

[0044] The load change analysis module extracts the device load conditions during working hours and non-working hours based on the abnormal offset value of high-energy-consuming devices, calculates the load growth rate, and obtains the device operation time sensitivity threshold.

[0045] The energy consumption optimization control module extracts the operation time of air conditioners, lighting, and elevators based on the device operation time sensitivity threshold, calculates the load growth rate, calculates the device operation time reduction amount or power adjustment range, and obtains the public building energy consumption adjustment plan.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0047] In the present invention, through the sub-analysis based on device characteristics and the adjustment of power contribution rate, accurate energy consumption distribution and optimization are achieved. The detailed analysis of device operation data improves the identification accuracy of abnormal energy consumption, and through the refined measures of load adjustment, energy consumption waste is effectively reduced. Such optimization measures not only make the energy consumption distribution more reasonable, but also enhance the pertinence and implementation effect of energy-saving measures, and significantly improve the efficiency and effect of energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0049] Figure 1 It is a schematic diagram of the step flow of the present invention;

[0050] Figure 2 It is a flowchart of step S1 of the present invention;

[0051] Figure 3 It is a flowchart of step S2 of the present invention;

[0052] Figure 4 It is a flowchart of step S3 of the present invention;

[0053] Figure 5 It is a flowchart of step S4 of the present invention;

[0054] Figure 6 It is a flowchart of step S5 of the present invention;

[0055] Figure 7 This is the system module diagram of the present invention. Detailed implementation manners

[0056] The technical solutions in the present invention will be described below with reference to the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0059] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0060] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0061] Please refer to Figure 1 , a method for auditing, statistics and splitting of energy consumption in public buildings, including the following steps:

[0062] S1: Obtain the operating time of air conditioners, lighting, elevators, and office equipment in public buildings, extract power data and load change rates, calculate the equipment power contribution rate and the equipment load contribution rate, and construct an equipment operation characteristic matrix;

[0063] S2: Based on the equipment operation characteristic matrix, extract the power ratios of air conditioners, lighting, elevators, and office equipment in different functional areas, calculate the power gain coefficient of high-power equipment areas, calculate the regional power gain adjustment parameter according to the equipment power contribution rate, set a power threshold, call the equipment operation mode change rate of the low-power equipment concentration area to calculate the load correction parameter, and adjust the energy consumption weight of the functional area according to the high-power area power gain coefficient and the low-power area load correction parameter to construct a regional energy consumption distribution coefficient;

[0064] S3: Based on the regional energy consumption distribution coefficient, extract the startup time, operating power, load change rate, and shutdown time of high-energy-consuming devices, calculate the energy consumption abnormal offset, screen for energy consumption abnormal devices according to the deviation value between the device energy consumption and the expected energy consumption of the device, and obtain the abnormal offset value of high-energy-consuming devices;

[0065] S4: Based on the abnormal offset value of high-energy-consuming devices, extract the device load conditions during working hours and non-working hours, calculate the load growth rate, call the operation trend comparison result to calculate the device power change characteristics, and obtain the device operation time sensitivity threshold;

[0066] S5: Based on the device operation time sensitivity threshold, extract the operation time of air conditioners, lighting, and elevators to calculate the load growth rate, call the current operation trend offset value to screen for load adjustment devices, calculate the device operation time reduction amount or power adjustment range, and obtain the public building energy consumption adjustment plan.

[0067] The device operation characteristic matrix includes the device power contribution rate, device load contribution rate, device power data, and device load change rate. The regional energy consumption distribution coefficient includes the high-power device regional power gain coefficient, regional power gain adjustment parameter, low-power device load correction parameter, and functional area energy consumption weight. The abnormal offset value of high-energy-consuming devices includes the device startup time, device operating power, device load change rate, device shutdown time, and energy consumption deviation value. The device operation time sensitivity threshold includes the working-hour load level, non-working-hour load level, load growth rate, and power change characteristics. The public building energy consumption adjustment plan includes the device operation time reduction amount, power adjustment range, load adjustment devices, and operation trend offset value.

[0068] Please refer to Figure 2 , the specific steps of S1 are as follows:

[0069] S101: Obtain the operation time of air conditioners, lighting, elevators, and office equipment, extract the power data and calculate the load change rate, and summarize the device power data set to obtain the average power load value;

[0070] Obtain the operating time of air conditioners, lighting, elevators, and office equipment. By retrieving the real-time operation logs of the equipment, extract the power-on and power-off times of each equipment in sequence, and calculate the total operating duration for each day, week, and month. Filter out the outliers in the equipment operating time, set reasonable time thresholds to eliminate incorrect data. For example, set that the single operation time of the air conditioner shall not exceed 16 hours, and the operating time of the lighting equipment shall not be less than 10 minutes. Eliminate the data that exceeds or is lower than the threshold. When extracting power data, call the equipment power acquisition module to obtain the power consumption situation, and store it based on the sampling data per minute, per hour, and per day. Complement the missing points in the power data using the linear interpolation method. When calculating the load change rate, call the power data of adjacent time points for comparison, and calculate the change rate per unit time based on the difference between the power at the current moment and the power at the previous moment. The calculation formula is where ΔP is the load change rate, P t is the power at the current moment, P t-1 is the power at the previous moment, and Δt is the time interval. When summarizing the equipment power data set, classify and store the data of the same type of equipment, and calculate its statistical characteristics, including parameters such as the maximum power, minimum power, average power, and standard deviation. At the same time, screen out the equipment with large fluctuations, set the power change threshold. For example, when the standard deviation is greater than 10% of the rated power of the equipment, mark this equipment as a high-fluctuation equipment. Finally, calculate the average power load value of each equipment, that is, perform an arithmetic average on the power values at all moments to obtain where is the average power load value, P i is the power value at the i-th time point, and N is the total number of time points. Finally, output the average power load value of each equipment.

[0071] S102: Call the average power load value, calculate the power proportion of the equipment, calculate the power contribution degree based on the total power load of the equipment, normalize and adjust the power contribution ratio of different equipment, screen out the dominant power equipment and summarize the contribution trend to obtain the power contribution rate of the equipment;

[0072] Call the average power load value, calculate the power proportion for each equipment, that is, the ratio of the average power load value of this equipment to the total power load of all equipment. The calculation formula is where ρ i is the power proportion of equipment i, is the average power load value of equipment i, is the total power load of all equipment. When calculating the power contribution degree, consider the operation duration weight of each equipment, and set the weight factor where T i is the operation time of equipment i, ∑T j is the total operation time of all equipment. The final power contribution degree is calculated as η i= ρ i · ω i When normalizing and adjusting the power contribution ratio of the differential device, considering the power levels of different devices, set the power level coefficient k i Through Calculate the power contribution correction factor of the device. The finally corrected power contribution ratio is When screening the dominant power devices, set the power contribution rate threshold. For example, select the devices with the top 30% contribution degree as the dominant devices, and summarize the change trend of their power contributions. By statistically analyzing the daily, weekly, and monthly power contribution degree changes of the devices, analyze the long-term trend of the device power load, and finally obtain the power contribution rate of each device.

[0073] S103: Call the power contribution rate and the average power load value, calculate the device load ratio, summarize the load characteristics of each device, identify and verify the device load contribution ratio, and form a device operation characteristic matrix;

[0074] Call the power contribution rate and the average power load value, calculate the device load ratio, that is, the ratio of the power contribution rate of the device to its operation time weight. The calculation formula is Where λ i Is the load ratio of device i. When summarizing the load characteristics of each device, according to the change trend of the load ratio, divide the devices into high-load, medium-load, and low-load devices, and set the judgment threshold. For example, λ > 0.5 is a high-load device, 0.2 ≤ λ ≤ 0.5 is a medium-load device, and λ < 0.2 is a low-load device. When identifying and verifying the device load contribution ratio, cross-verify the data in different time periods to ensure the stability of the load ratio calculation, and calculate the mean square deviation of the device load change to ensure the rationality of the load characteristic division. Finally, form a device operation characteristic matrix. This matrix takes the device type as the row and the power characteristic parameters as the column, and displays the load characteristics of each device, including information such as the operation duration, average power load value, power contribution rate, and load ratio.

[0075] Please refer to Figure 3 The specific steps of S2 are as follows:

[0076] S201: Based on the device operation characteristic matrix, extract the power ratios of air conditioners, lighting, elevators, and office equipment in different functional areas, and calculate the power gain coefficient of the high-power device area;

[0077] Based on the device operation characteristic matrix, extract the power ratios of air conditioners, lighting, elevators, and office equipment in different functional areas, call the average power load values of each device in the matrix, and classify and summarize them according to the regional division dimension. Calculate the total power of each device type in each functional area, accumulate the powers of the same devices in the same area, and obtain the device power ratio in this area. The calculation process is Where ψq The device power ratio of area q, ∑P q,k is the total power of device k in area q, ∑P t is the total power sum of devices in all areas. When calculating the power gain coefficient of high-power device areas, first screen the areas with the top 30% power ratios, and calculate the power gain coefficient θ of each area q , and the value-taking method is where ∑P q，h is the total power sum of the devices with the top 30% power in area q, ∑P q,l is the total power sum of the remaining devices in area q. If θ q > 1.5, it is determined as a high-power device area, and finally the power gain coefficient of the high-power device area is obtained.

[0078] S202: Invoke the power gain coefficient, calculate the area power gain adjustment parameter according to the device power contribution rate, set the power threshold in combination with the power ratio data, invoke the device operation mode change rate of the low-power device area to calculate the load correction parameter, summarize the device power change characteristics, screen the key load change trends, calculate the load correction ratio and summarize the area load correction trend to obtain the load correction parameter of the low-power device area;

[0079] Invoke the power gain coefficient, calculate the area power gain adjustment parameter according to the device power contribution rate. First, extract the device power contribution rate η in each area k , and calculate the average value of the power contribution rate within the area where N q is the total number of devices in area q, and the power gain adjustment parameter β q The value-taking method is During the calculation process, ensure that the power gain adjustment parameters of all areas are normalized to the 0-1 interval. When setting the power threshold in combination with the power ratio data, set the device power change critical point τ k , and the value-taking method is the rated power of the device multiplied by the device operation duration weight, that is, τ k = P 额定,k ·ω k , if the operating power of a certain device is lower than 0.8 times of τ k , it is marked as a low-power device area. When invoking the device operation mode change rate of the low-power device area to calculate the load correction parameter, summarize the number of times of change δ of the device operation state k , and calculate the average change rate The load correction parameter γ q The value-taking method is When summarizing the device power change characteristics, record the device operation mode change trends of each area, screen the key load change trends, and calculate the load correction ratio Summarize the regional load correction trend, and finally obtain the regional load correction parameters for low-power devices.

[0080] S203: Call the power gain coefficient and load correction parameters, adjust the energy consumption weight of the functional area, and calculate the energy consumption contribution rate based on the adjusted energy consumption weight to form the regional energy consumption distribution coefficient;

[0081] The specific formula for calculating the energy consumption contribution rate is:

[0082]

[0083] Among them, γ i represents the energy consumption contribution rate of device i in the functional area, ψ i represents the load impact factor of device i, θ i represents the running duration of device i, and ∑(ψ j ·θ j ) represents the denominator of the normalized calculation of the total energy consumption of all devices in this area.

[0084] In this formula, calculate the energy consumption contribution rate γ i of device i in the functional area. This is achieved by considering the load impact factor ψ i of the device and the running duration θ i of the device. The overall energy consumption contribution rate is the ratio of the product of the load impact factor of this device and the running duration to the sum of the corresponding products of all devices in the area.

[0085] The load impact factor ψ i of device i depends on its relative proportion of energy consumption during a specific time period, which can be directly measured by an energy monitoring system. Suppose that during a specific evaluation period, the energy consumption of device i is 12,000 kilowatt-hours (kWh), and the total energy consumption of all devices in the entire area is 300,000 kWh. Therefore, the calculation of ψ i is as follows:

[0086]

[0087] The running duration θ i of the device is recorded by a real-time monitoring system. In this example, assume that the device has run for 450 hours within the month under investigation. In addition, to calculate the denominator of the formula, we need to consider the sum of the products of the running durations and load impact factors of all devices. Suppose that the similar data of other devices have been calculated and the sum is 15,000.

[0088] Substitute the specific values, and the calculation of the formula is:

[0089]

[0090] The result shows that the energy consumption contribution rate of device i is 0.12%, which means that within the considered functional area, the contribution of device i to the total energy consumption is relatively small. This calculation helps to identify devices or groups of devices that may require more attention in the energy consumption management strategy. In this way, the operating efficiency and energy consumption allocation of the devices can be optimized to achieve a reduction in energy consumption and an improvement in cost-effectiveness.

[0091] Please refer to Figure 4 , and the specific steps of S3 are as follows:

[0092] S301: Based on the regional energy consumption allocation coefficient, extract the start time, operating power, load change rate, and shutdown time of high-energy-consuming devices, calculate the amplitude of device power fluctuation, and obtain the load change rate of high-energy-consuming devices;

[0093] Based on the regional energy consumption allocation coefficient, extract the start time, operating power, load change rate, and shutdown time of high-energy-consuming devices, screen the devices with the top 20% in terms of device power contribution rate from the energy consumption dataset, and extract their operating time series. By traversing the start and stop time points of the devices, obtain the start and stop moments of the devices every day, and calculate the average start time σ s and shutdown time σ e , and the calculation method is and where T s and T e respectively represent the start and shutdown time sets of the device, n s and n e are the number of starts and shutdowns respectively. When calculating the operating power, extract the power sampling data of the device and take the average value to obtain the average operating power of the device where P d represents the total power of the device during operation, t d is the total operating duration of the device. When calculating the load change rate, traverse the power data during the operation of the device and calculate the power change value at adjacent time points where P d,t is the power of the device at time t. Finally, calculate the amplitude of device power fluctuation, screen the devices with power changes exceeding the mean ±20%, and obtain their load change rate.

[0094] S302: Call the load change rate of high-energy-consuming devices, analyze the operating energy consumption of the devices and compare it with the expected energy consumption to obtain the deviation value, screen the devices with deviations exceeding the set threshold, identify the offset amount outside the range, summarize the offset characteristics of energy consumption abnormal devices, extract the key offset trends, calculate the offset difference between devices, screen the key energy consumption abnormal types, and obtain the energy consumption abnormal offset amount;

[0095] The specific formula for the offset difference is:

[0096]

[0097] Δ = |δ i - δ j |;

[0098] where δ represents the percentage of energy consumption deviation, E actual represents the actually measured energy consumption value, E expected represents the expected energy consumption value calculated based on historical data, τ represents the threshold of deviation, Δ represents the offset difference between different devices, δ i and δ j represent the energy consumption deviation rates of different devices.

[0099] The percentage of energy consumption deviation δ of the device represents the relative deviation degree between the actual energy consumption E actual of the device and the expected energy consumption E expected . The energy consumption data of the device is collected by the intelligent monitoring system, and the data sources include power metering instruments, equipment management systems and energy consumption model prediction systems. E actual is calculated by summarizing monthly, quarterly or annual data and combining the power consumption data of the device. E expected is calculated based on the historical average energy consumption, current workload and operating efficiency of the device. The relationship between the device load and energy consumption is predicted by statistical modeling, and the parameters of the prediction model are calculated from the energy consumption trend in the past 12 months and weighted and corrected to ensure the prediction accuracy.

[0100] The actual monthly energy consumption of the central air-conditioning system in an office building is 13000 kWh. Through historical data and operating parameters, the monthly expected energy consumption of the device is predicted to be 12500 kWh. Substitute it into the formula to calculate the percentage of energy consumption deviation of the device:

[0101]

[0102] δ = 4%;

[0103] The calculation result shows that the energy consumption of the device is 4% higher than the expected value, indicating that the operating efficiency of the device has decreased or there is an overloaded operation situation. When screening devices with deviations exceeding the set threshold, the energy consumption deviation threshold τ = 5% is set. Since δ < τ, this device is not marked as an abnormal device.

[0104] The energy consumption offset between devices is calculated using the Δ formula. Set the energy consumption deviation values of two devices to be δ 1 = 4% and δ 2 = 7.5% respectively, and calculate their energy consumption offset difference:

[0105] Δ = |4% - 7.5%| = 3.5%;

[0106] The results show that the energy consumption offset difference between these two devices is 3.5%. When the Δ value is higher than the set energy consumption balance standard value, it is necessary to further analyze the operating status of the device with high offset, and investigate the types of abnormal energy consumption, including factors such as overtime operation, equipment aging, or abnormal system parameters, etc., and finally obtain the abnormal energy consumption offset amount.

[0107] S303: Call the abnormal energy consumption offset amount, extract the abnormal offset value of the high-energy-consuming device, and classify the abnormal devices according to the operating status to form the abnormal offset value of the high-energy-consuming device;

[0108] Call the abnormal energy consumption offset amount, extract the abnormal offset value of the high-energy-consuming device, classify the abnormal devices according to the operating status, extract the historical operating data of the abnormal devices, and count the time period when the abnormality occurs, and calculate the offset amplitude of the abnormal devices where δ c is the energy consumption deviation value of each abnormal device, and N o is the total number of abnormal devices. When judging the type of abnormality, according to the power fluctuation situation of the device, the device is classified as a sudden abnormality, a periodic abnormality, or a device with long-term abnormal energy consumption, and finally the abnormal offset value of the high-energy-consuming device is formed.

[0109] Please refer to Figure 5 , the specific steps of S4 are as follows:

[0110] S401: Based on the abnormal offset value of the high-energy-consuming device, extract the device load conditions during the working period and the non-working period, calculate the load growth rate for each period, and obtain the device load result;

[0111] Based on the abnormal offset value of the high-energy-consuming device, extract the device load conditions during the working period and the non-working period, traverse the operation logs of each device, divide the daytime working period and the nighttime non-working period, and record the operating power of the device for each period. When calculating the load growth rate for each period, first calculate the total load κ s and κ n , where κ s =∑P s,t represents the total load during the working period, and κ n =∑P n,t represents the total load during the non-working period. Subsequently, calculate the load growth rate where φ t represents the load growth rate. When φ t >0.3, the device load growth rate is marked as a significant increase, and finally the device load growth rate is obtained.

[0112] S402: Call the device load result, calculate the change in the device operation trend, obtain the device power change rate by comparing the power fluctuations, screen the fluctuation range of high-load devices, summarize the power change characteristics of the device operation mode, calculate the device load adjustment ratio, screen the key load change points, determine the key load fluctuation type, and obtain the device power change characteristics;

[0113] Call the device load growth rate, calculate the change in the device operation trend, extract the device load data for consecutive days, and calculate the average daily load change where κ d is the load value on the d-th day, and calculate the device operation trend change rate And obtain the device power change rate by comparing the power fluctuations, and calculate the power change in adjacent time periods where υ w is the power change rate. When screening the fluctuation range of high-load devices, set the fluctuation threshold ζ f = 0.2. When υ w > ζ f this device is marked as a high-load fluctuation device. When summarizing the power change characteristics of the device operation mode, calculate the device operation mode change rate where μ p represents the device mode change rate, and T p is the device observation period, and calculate the device load adjustment ratio When screening the key load change points, set the change amplitude critical value ξ h , when β y > ξ h this device is identified as a key load change device, determine the key load fluctuation type, and finally obtain the device power change characteristics.

[0114] S403: Call the device power change characteristics, calculate the change range of the device operation time, and summarize the operation time change interval based on the load fluctuations to form the device operation time sensitivity threshold;

[0115] Call the device power change characteristics, calculate the change range of the device operation time, traverse the operation data of the device, extract the operation time γ u of each time period, and calculate the operation time change rate And summarize the operation time change interval based on the load fluctuations, set the operation time fluctuation limit α v . If λ w > α v , it is marked as a high-fluctuation device, and finally form the device operation time sensitivity threshold.

[0116] Please refer to Figure 6 , the specific steps of S5 are as follows:

[0117] S501: Based on the device operation time sensitivity threshold, extract the operation times of air conditioners, lighting, and elevators, calculate the load growth rate, and summarize the load change trends in multiple time periods to obtain the device load growth rate.

[0118] Based on the device operation time sensitivity threshold, extract the operation times of air conditioners, lighting, and elevators, call the device operation log data, record the startup and shutdown times of each device in different time periods, and calculate the average daily operation duration of each device. Where T o represents the single operation time of the device, and n u is the number of device operations. When calculating the load growth rate, extract the operation power data of the device and calculate the power change rate in adjacent time periods. Where P o is the device power in the current time period, and T p is the time interval. When summarizing the load change trends in multiple time periods, statistically calculate the average load change of the device and calculate the standard deviation of the change trend. Where is the average load change rate, and n g is the number of observation time periods, and finally obtain the device load growth rate.

[0119] S502: Call the device load growth rate, screen the load adjustment devices by comparing the current operation trend offset value, calculate the device operation time reduction amount, and combine the power fluctuation amplitude to calculate the power adjustment amplitude. Summarize the power change trends of different operation modes, screen the key power adjustment intervals, and calculate the device load adjustment ratio to obtain the device operation time reduction amount and the power adjustment amplitude.

[0120] Call the device load growth rate, screen the load adjustment devices by comparing the current operation trend offset value, and calculate the change in the operation time of each device. Where is the average value of the device historical operation duration. When γ t > δ u (δ u is the set time change threshold), this device is identified as a load adjustment device. When calculating the device operation time reduction amount, set the target operation time θ r = θ u - γ t . When combining the power fluctuation amplitude to calculate the power adjustment amplitude, extract the historical power data of the device and calculate the fluctuation amplitude in adjacent time periods. Where n p is the number of time periods. When summarizing the power change trends of different operation modes, calculate the operation mode stability of the device. Where is the average power value. When screening the key power adjustment intervals, set the power adjustment critical value ωt , when ξ p > ω t , the device enters the power adjustment device list and calculates the device load adjustment ratio Finally, the device operation time reduction amount and the power adjustment range are obtained.

[0121] S503: Invoke the device operation time reduction amount and the power adjustment range, calculate the overall load of the public building, and summarize the device adjustment parameters to form an energy consumption adjustment plan for the public building;

[0122] Invoke the device operation time reduction amount and the power adjustment range, calculate the overall load of the public building, traverse the reduction time and power adjustment data of all devices, and calculate the overall load adjustment amount ψ b = ∑(θ r ·P r ), where P r is the operating power of the device after adjustment. When summarizing the device adjustment parameters, calculate the adjusted energy consumption ratio Form an energy consumption adjustment plan for the public building. This plan is optimized and adjusted based on the device load change situation, and the overall energy consumption trend is summarized to ensure the rationality of the load adjustment.

[0123] Please refer to Figure 7 , a public building energy consumption audit and statistics splitting system, including:

[0124] The device operation characteristics module obtains the operation time of air conditioners, lighting, elevators, and office equipment in the public building, calculates the device power contribution rate and the device load contribution rate, and constructs a device operation characteristics matrix;

[0125] The energy consumption allocation and adjustment module, based on the device operation characteristics matrix, extracts the power ratios of air conditioners, lighting, elevators, and office equipment in different functional areas, calculates the power gain coefficient of high-power equipment areas, calculates the regional power gain adjustment parameters according to the device power contribution rate, sets the power threshold, and adjusts the energy consumption weights of the functional areas according to the high-power area power gain coefficient and the low-power area load correction parameters to construct a regional energy consumption allocation coefficient;

[0126] The abnormal energy consumption monitoring module, based on the regional energy consumption allocation coefficient, extracts the start time, operating power, load change rate, and shutdown time of high-energy-consuming equipment, calculates the energy consumption abnormal offset, and obtains the high-energy-consuming equipment abnormal offset value according to the deviation value between the device energy consumption and the device expected energy consumption;

[0127] The load change analysis module, based on the high-energy-consuming equipment abnormal offset value, extracts the device load conditions during working hours and non-working hours, calculates the load growth rate, and obtains the device operation time sensitivity threshold;

[0128] The energy consumption optimization control module extracts the running time of air conditioners, lighting, and elevators based on the time-sensitive threshold of equipment operation, calculates the load growth rate, calculates the reduction amount of equipment running time or the power adjustment range, and obtains the energy consumption adjustment plan for public buildings.

[0129] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the said claims.

Claims

1. A method for statistical splitting of public building energy consumption audit, characterized in that: The following steps are involved: S1: Obtain the operating time, power data and load change rate of air conditioners, lighting, elevators and office equipment, calculate the equipment power and load contribution rate, and build the equipment operation characteristic matrix; S2: Based on the equipment operation characteristic matrix, extract the power proportion of differentiated regional equipment, calculate the regional power gain adjustment parameter according to the power contribution rate, set the power threshold, calculate the power gain coefficient of the high-power area and the load correction parameter of the low-power area, adjust the energy consumption weight of the functional area, and construct the regional energy consumption allocation coefficient; S3: Based on the regional energy consumption distribution coefficient, extract the startup time, operating power, load change rate, and downtime of the high-energy consumption equipment, calculate the energy consumption abnormality offset, and screen out the energy consumption abnormality equipment to obtain the high-energy consumption equipment abnormality offset value; S4: extracting the abnormal offset value of the high-energy-consuming device, analyzing the load conditions during working and non-working periods, calculating the load growth rate, and obtaining the device operation time sensitive threshold; S5: Based on the equipment operation time sensitive threshold, calculate the load growth rate, screen the load adjustment equipment, calculate the equipment operation time reduction or power adjustment range, and obtain the public building energy consumption adjustment plan.

2. The public building energy consumption audit statistical splitting method according to claim 1 is characterized in that: The equipment operation characteristic matrix includes equipment power contribution rate, equipment load contribution rate, and equipment power data; the regional energy consumption allocation coefficient includes high-power equipment regional power gain coefficient, regional power gain adjustment parameter, low-power equipment load correction parameter, and functional area energy consumption weight; the high-energy consumption equipment abnormal offset value includes equipment startup time, equipment operating power, equipment load change rate, equipment downtime, and energy consumption deviation value; the equipment operation time sensitive threshold includes working period load level, non-working period load level, load growth rate, and power change characteristics; the public building energy consumption adjustment plan includes equipment operation time reduction, power adjustment range, load adjustment equipment, and operation trend offset value.

3. The public building energy consumption audit statistical splitting method according to claim 1 is characterized in that: The specific steps to obtain the operating time, power data and load change rate of air conditioners, lighting, elevators and office equipment, calculate the equipment power and load contribution rate, and build the equipment operation characteristic matrix are as follows: S101: Obtain the operating time of air conditioners, lighting, elevators, and office equipment, extract power data, calculate the load change rate, summarize the equipment power data set, and obtain the average power load value; S102: calling the average power load value, calculating the power proportion of the equipment, calculating the power contribution based on the total power load of the equipment, normalizing and adjusting the power contribution ratio of the differentiated equipment, screening the dominant power equipment and summarizing the contribution trend, and obtaining the power contribution rate of the equipment; S103: calling the power contribution rate and the average power load value, calculating the equipment load proportion, summarizing the load characteristics of each device, identifying and verifying the equipment load contribution proportion, and forming an equipment operation characteristic matrix.

4. The public building energy consumption audit statistical splitting method according to claim 1 is characterized in that: Based on the equipment operation characteristic matrix, the power proportion of differentiated regional equipment is extracted, the regional power gain adjustment parameter is calculated according to the power contribution rate, and the power threshold is set. The power gain coefficient of the high-power area and the load correction parameter of the low-power area are calculated, and the energy consumption weight of the functional area is adjusted. The specific steps of constructing the regional energy consumption allocation coefficient are as follows: S201: Based on the equipment operation characteristic matrix, extract the power proportions of air conditioners, lighting, elevators, and office equipment in differentiated functional areas, and calculate the power gain coefficient of the high-power equipment area; S202: calling the power gain coefficient, calculating the regional power gain adjustment parameter according to the equipment power contribution rate, setting the power threshold in combination with the power proportion data, calling the equipment operation mode change rate of the low-power equipment area to calculate the load correction parameter, summarizing the equipment power change characteristics, screening the key load change trend, calculating the load correction ratio and summarizing the regional load correction trend, and obtaining the low-power equipment area load correction parameter; S203: calling the power gain coefficient and the load correction parameter, adjusting the energy consumption weight of the functional area, and calculating the energy consumption ratio value according to the adjusted energy consumption weight to form the regional energy consumption allocation coefficient.

5. The public building energy consumption audit statistical splitting method according to claim 4 is characterized in that: The energy consumption contribution rate calculation formula is specifically: Among them, γ i represents the energy consumption contribution rate of equipment i in the functional area, ψ i represents the load impact factor of device i, θ i represents the running time of device i, ∑(ψ j ·θ j ) represents the denominator of the normalized calculation of the total energy consumption of all devices in the area.

6. The public building energy consumption audit statistical splitting method according to claim 1 is characterized in that: Based on the regional energy consumption distribution coefficient, the startup time, operating power, load change rate, and downtime of high-energy-consuming equipment are extracted, the energy consumption abnormality offset is calculated, and the energy consumption abnormality equipment is screened out. The specific steps for obtaining the abnormal offset value of high-energy-consuming equipment are as follows: S301: Based on the regional energy consumption allocation coefficient, extract the startup time, operating power, load change rate, and downtime of the high-energy consumption equipment, calculate the power fluctuation amplitude of the equipment, and obtain the load change rate of the high-energy consumption equipment; S302: calling the load change rate of the high-energy-consuming device, analyzing the operating energy consumption of the device and comparing it with the expected energy consumption to obtain a deviation value, screening devices whose deviations exceed the set threshold, identifying the offsets that exceed the range, summarizing the offset characteristics of the energy-consuming abnormal devices, extracting the key offset trends, calculating the offset differences between devices, screening the key energy-consuming abnormal types, and obtaining the energy-consuming abnormal offsets; S303: calling the energy consumption abnormal offset, extracting the abnormal offset value of the high energy consumption device, and classifying the abnormal devices according to the operating status to form the abnormal offset value of the high energy consumption device.

7. The public building energy consumption audit statistical splitting method according to claim 6 is characterized in that: The offset difference calculation formula is specifically: Δ=|δ i -d j |; Among them, δ represents the energy consumption deviation percentage, E actual Represents the actual measured energy consumption value, E expected represents the expected energy consumption value calculated based on historical data, Δ represents the offset difference between different devices, and δ i and δ j Represents the energy consumption deviation rate of different devices.

8. The public building energy consumption audit statistical splitting method according to claim 1 is characterized in that: The specific steps of extracting the abnormal offset value of the high-energy-consuming equipment, analyzing the load conditions during working and non-working periods, calculating the load growth rate, and obtaining the equipment operation time sensitive threshold are as follows: S401: Based on the abnormal offset value of the high-energy-consuming device, extract the device load conditions during the working period and the non-working period, calculate the load growth rate of each period, and obtain the device load result; S402: calling the equipment load result, calculating the equipment operation trend change, and comparing the power fluctuation to obtain the equipment power change rate, screening the fluctuation range of the high-load equipment, summarizing the power change characteristics of the equipment operation mode, calculating the equipment load adjustment ratio, screening the key load change points, determining the key load fluctuation type, and obtaining the equipment power change characteristics; S403: calling the power variation characteristics of the device, calculating the variation range of the device operation time, and summarizing the operation time variation interval according to the load fluctuation to form a device operation time sensitive threshold.

9. The public building energy consumption audit statistical splitting method according to claim 1 is characterized in that: Based on the equipment operation time sensitive threshold, the specific steps of calculating the load growth rate, screening the load adjustment equipment, calculating the equipment operation time reduction or power adjustment range, and obtaining the public building energy consumption adjustment plan are as follows: S501: Based on the equipment operation time sensitive threshold, extract the operation time of air conditioning, lighting, and elevator, calculate the load growth rate, and summarize the load change trend in multiple time periods to obtain the equipment load growth rate; S502: calling the device load growth rate, comparing the current operation trend offset value to select the load adjustment device, calculating the device operation time reduction, and calculating the power adjustment range in combination with the power fluctuation range, summarizing the power change trend of the differentiated operation mode, selecting the key power adjustment interval, calculating the device load adjustment ratio, and obtaining the device operation time reduction and the power adjustment range; S503: The reduction amount of the equipment operation time and the power adjustment range are called to calculate the overall load of the public building, and the equipment adjustment parameters are summarized to form an energy consumption adjustment plan for the public building.

10. A public building energy consumption audit statistics splitting system, characterized in that: According to a method for statistical splitting of energy consumption audit of public buildings according to any one of claims 1 to 9, the system comprises: The equipment operation characteristic module obtains the operating time of air conditioners, lighting, elevators, and office equipment in public buildings, calculates the equipment power contribution rate and equipment load contribution rate, and constructs the equipment operation characteristic matrix; The energy consumption allocation adjustment module extracts the power proportion of air conditioners, lighting, elevators, and office equipment in differentiated functional areas based on the equipment operation characteristic matrix, calculates the power gain coefficient of the high-power equipment area, calculates the regional power gain adjustment parameter based on the equipment power contribution rate, and sets the power threshold. The energy consumption weight of the functional area is adjusted according to the power gain coefficient of the high-power area and the load correction parameter of the low-power area, and the regional energy consumption allocation coefficient is constructed; The abnormal energy consumption monitoring module extracts the startup time, operating power, load change rate, and downtime of high-energy-consuming equipment based on the regional energy consumption allocation coefficient, calculates the energy consumption abnormality offset, and obtains the abnormal offset value of high-energy-consuming equipment based on the deviation value between the equipment energy consumption and the expected energy consumption of the equipment; The load change analysis module extracts the equipment load conditions during working hours and non-working hours based on the abnormal offset value of the high-energy-consuming equipment, calculates the load growth rate, and obtains the equipment operation time sensitive threshold; The energy consumption optimization control module extracts the operating time of air conditioners, lighting, and elevators based on the equipment operating time sensitive threshold to calculate the load growth rate, calculates the equipment operating time reduction or power adjustment range, and obtains the public building energy consumption adjustment plan.

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