Energy-saving measurement and verification method and platform for public institution-oriented integrated energy hosting project

Abstract

The application relates to the field of energy-saving management, and discloses a public institution-oriented comprehensive energy hosting project energy-saving measurement and verification method and platform, which has the technical scheme as follows: collecting hourly energy consumption data for at least 12 months, identifying and marking human flow related circuits; based on the energy consumption data of the human flow related circuits, calculating a human flow index capable of representing the relative change of personnel activity intensity, and configuring multi-level data missing fault tolerance processing rules; using historical operation data before energy-saving reconstruction implementation, constructing a multiple regression energy consumption benchmark model; substituting real-time environmental parameters and date attributes into the energy consumption benchmark model to calculate a theoretical benchmark power consumption; real-time monitoring of the proportion of the human flow disturbance component in the preliminary energy-saving amount, when the proportion exceeds a preset judgment threshold, automatically triggering a multi-period merging accounting mechanism; if the abnormality lasts for at least two consecutive settlement periods, starting on-site verification, abnormal control and settlement calibration of energy-saving amount accounting.

Description

Technical Field

[0001] This invention relates to the field of energy conservation management, and more specifically, to a method and platform for measuring and verifying energy savings in integrated energy management projects for public institutions. Background Technology

[0002] Public institutions, primarily including government office buildings, schools at all levels, hospitals, and cultural and sports venues, generally exhibit characteristics such as large energy consumption, complex functional zoning, high fluctuations in personnel movement, and energy consumption patterns significantly influenced by human activities. Currently, integrated energy cost management and energy-saving benefit sharing models have become the mainstream cooperation models for energy-saving renovations in public institutions. Accurate measurement and verification of energy savings are crucial for the implementation of energy management projects, cost settlement, benefit sharing, and contract fulfillment, and are also long-standing technical challenges and commercial disputes within the industry.

[0003] Currently, the industry-standard basis for energy saving calculation is the IPMVP international energy efficiency measurement and verification standard and national standards such as GB / T28750. Most mainstream calculation schemes use overall building energy consumption data, combined with ambient temperature, humidity, and weekday / holiday time dimensions to establish a benchmark model. Existing technologies have the following significant drawbacks: Energy consumption in public institutions is greatly affected by the flow of people and the intensity of their activities. Traditional models only consider meteorological and time factors and do not quantify the variables of human flow disturbance. They cannot distinguish between "energy consumption reduction caused by equipment energy-saving renovation" and "natural power saving caused by reduced human flow and activity frequency". This can easily lead to an overestimation or underestimation of energy savings, resulting in frequent disputes between the parties involved in the settlement. If hardware methods such as facial recognition, infrared passenger flow counters, and access control are used to count people, there are problems such as high deployment costs, difficulty in transformation, leakage of privacy in public areas, insufficient compliance, and high maintenance costs, making it extremely difficult for public institutions to implement. The building's refrigeration units, centralized power equipment, and computer room basic loads operate stably year-round. The overall total energy consumption can mask the fluctuation characteristics of the end-user traffic load. Using global electricity consumption statistics cannot effectively identify changes in personnel activity. Special public institutions such as hospitals and schools have characteristics such as large differences in the flow of people between different areas, obvious holiday rhythms, and sudden changes in the flow of people caused by public emergencies. As a result, the generalized energy consumption model has poor adaptability and low calculation accuracy. The lack of a sound data fault tolerance mechanism, anomaly detection rules, and multi-cycle calibration mechanism, coupled with the absence of standardized processing logic for scenarios such as meter disconnection, data loss, and short-term extreme fluctuations in population flow, results in insufficient stability and credibility of the accounting data.

[0004] Therefore, it is necessary to design a new solution to address the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method and platform for measuring and verifying energy savings in integrated energy management projects for public institutions. Based on the energy consumption characteristics of each circuit, the method identifies the load associated with pedestrian flow, constructs a regression benchmark model that integrates multiple influencing factors, quantitatively removes the interference of pedestrian flow fluctuations on energy consumption, and achieves objective, accurate, and traceable calculation of energy savings.

[0006] The above-mentioned technical objective of this invention is achieved through the following technical solution: a method for measuring and verifying energy savings in integrated energy management projects for public institutions, comprising the following steps: S1. Collect hourly energy consumption data for each power circuit in public institutions for at least 12 months, combine the ratio of electricity consumption between non-working days and working days, the ratio of low-load periods at night to total daily electricity consumption, and the inherent attributes of electrical equipment to identify and mark circuits related to pedestrian flow that are strongly correlated with changes in pedestrian flow. S2. Based on the energy consumption data of the marked pedestrian flow-related loops, combined with the historical benchmark energy consumption for the same period, calculate the pedestrian flow index that can characterize the relative change in the intensity of human activity, and configure multi-level data missing fault tolerance rules. S3. Using historical operating data before the implementation of energy-saving renovation, construct a multivariate regression energy consumption benchmark model with ambient temperature, ambient humidity, workday attributes and pedestrian flow index as independent variables. S4. During the operation cycle after the energy-saving renovation, substitute the real-time environmental parameters and date attributes into the energy consumption benchmark model to calculate the theoretical benchmark electricity consumption; combine the actual electricity consumption of the building to calculate the preliminary energy saving, quantitatively eliminate the energy consumption disturbance component caused by the fluctuation of people flow, and obtain the real energy saving. S5. Real-time monitoring of the proportion of pedestrian disturbance in the initial energy saving. When the proportion exceeds the preset judgment threshold, the multi-cycle merging accounting mechanism is automatically triggered. If there are continuous abnormalities for at least two consecutive settlement cycles, on-site verification is initiated to carry out abnormal control and settlement calibration of energy saving accounting.

[0007] As a preferred technical solution of the present invention, in S1, by calculating the ratio of electricity consumption during non-working days and working days, and the ratio of electricity consumption during low-load periods at night and throughout the day, and combining the dual ratio joint judgment rule, the traffic flow related loops are initially screened. Fixed public terminal circuits will be directly designated as circuits related to human traffic; special power circuits with significant seasonal fluctuations will be marked for manual confirmation, and the system will record the verification results and the identity of the verifier.

[0008] As a preferred technical solution of the present invention, in S2, the median historical energy consumption of the same type of time period in the past 12 months is used as the benchmark energy consumption, and combined with the actual power consumption of the pedestrian flow related circuits on the day, the pedestrian flow index, which represents the change in the intensity of human activity, is calculated. Data missing error handling rules include: Short-term missing data for a single hour are filled by interpolation using the mean of adjacent time periods; A single-circuit connection that has been interrupted for an extended period will be suspended from operation for the day. If the percentage of valid data collected is insufficient, the pedestrian flow index is marked as invalid.

[0009] As a preferred technical solution of the present invention, in S3, the multivariate regression energy consumption benchmark model uses ambient temperature, ambient humidity, workday identification, and pedestrian flow index as independent variables, and building electricity consumption as dependent variable. The model updates historical sample data periodically and dynamically adjusts the regression coefficients of each influencing factor.

[0010] As a preferred technical solution of the present invention, in S4, the deviation component between the current flow index and the historical normal flow index is quantitatively calculated based on the energy consumption benchmark model coefficient.

[0011] As a preferred technical solution of the present invention, in S5, the preset threshold is a configurable proportional parameter; In the event of an anomaly in a single cycle, the energy saving is calculated by merging the average values ​​of two cycles to avoid settlement deviations caused by drastic fluctuations in short-term passenger flow.

[0012] As a preferred technical solution of the present invention, when applied in a medical institution setting: Divide the power circuits into independent power circuits according to functional zones, and calculate the pedestrian flow index for each area. For areas with high volatility, a weighting correction coefficient is added.

[0013] As a preferred technical solution of the present invention, when applied to a public building scenario such as an educational institution: Specific modified restrictions have been set for periods of low foot traffic during winter and summer vacations; Energy consumption sub-models were established for different functional areas such as teaching buildings, dormitories, and canteens to match the different activity patterns of personnel.

[0014] A platform for measuring and verifying energy savings in integrated energy management projects for public institutions includes a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor implements the above-mentioned method when executing the computer program.

[0015] In summary, the present invention has the following beneficial effects: This invention differs from traditional overall energy consumption statistics methods by employing a refined breakdown of individual power consumption circuits. Through dual determination of power consumption ratios in two time periods and inherent equipment attributes, it accurately filters out end circuits strongly correlated with human traffic, shielding the interference of stable basic loads such as refrigeration units and computer room power, and ensuring that human activity characteristics can be effectively extracted.

[0016] It can calculate the pedestrian flow index by relying on the hourly energy consumption data collected by existing smart meters. There is no need to install additional hardware devices such as facial recognition and infrared passenger flow statistics. It does not collect personal privacy data and is fully compatible with public institutions with strict privacy control, such as governments, hospitals, and schools. It is simple to deploy and has low operation and maintenance costs.

[0017] By constructing a four-dimensional multiple regression benchmark model that incorporates temperature, humidity, weekday attributes, and a self-built pedestrian flow index, the model dynamically quantifies the combined impact of meteorological, temporal, and human activity factors on energy consumption. This breaks through the limitations of traditional models with a single variable, resulting in a higher degree of fit to the benchmark electricity consumption.

[0018] By quantifying the energy consumption disturbance caused by the deviation of people flow through model coefficients, the energy consumption changes caused by non-energy-saving factors can be accurately eliminated from the initial energy savings, thus preventing accounting distortions caused by fewer people saving electricity and more people consuming electricity, and eliminating settlement disputes between the parties in the energy management project from a technical perspective.

[0019] A tiered fault-tolerance rule is set up to address common field issues such as meter disconnection, data loss, and circuit failure. At the same time, a mechanism is configured to determine the proportion of human disturbance exceeding the limit, perform multi-cycle combined accounting, and conduct continuous anomaly on-site verification to ensure data continuity and the stability of accounting results.

[0020] It sets up exclusive optimization logic for typical scenarios of public institutions such as fluctuations in patient flow in hospital zones and energy consumption characteristics during school holidays. Through zone modeling, weight correction, and holiday restrictions, it meets the personalized accounting needs of different types of public buildings and has a wide range of applications. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] It is readily understood that, based on the technical solution of this invention, various embodiments of the invention can be conceived by those skilled in the art without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention. Rather, these embodiments are provided to enable those skilled in the art to gain a more thorough understanding of the invention. Preferred embodiments of the invention are described below in conjunction with the accompanying drawings, which form part of this application and, together with the embodiments of the invention, serve to illustrate the innovative concept of the invention.

[0023] like Figure 1 As shown, this invention provides a method and a compatible hardware and software platform to comprehensively improve the objectivity, stability, and traceability of energy saving accounting for public institution energy management projects.

[0024] The method of the present invention includes the following steps: S1. The system first centrally collects the electricity consumption data of the entire power distribution circuit of the target public institution, and uniformly collects the hourly energy consumption raw data for at least twelve consecutive months. The data time span fully covers the four seasons, daily workdays, weekends, statutory holidays, long holidays, winter and summer vacations, seasonal maintenance, and other full operating cycles to ensure comprehensive statistical samples and avoid judgment errors caused by short-term data bias.

[0025] After data processing, daytime operating hours and nighttime low-load hours were uniformly defined, and two types of core comparative indicators were calculated separately.

[0026] The first type of indicator is the ratio of daytime electricity consumption on non-working days to daytime electricity consumption on working days, which is used to reflect whether the load of the circuit changes synchronously with the flow of people during work and rest.

[0027] The second type of indicator is the ratio of electricity consumption during the low-load period at night to the total electricity consumption of the day. It is used to determine the basic standby load ratio of the circuit and to screen out power loads that operate stably throughout the day and are unrelated to human traffic.

[0028] Combining the above-mentioned dual-ratio joint judgment rules, and taking into account the actual function, usage scenario, and operating attributes of each electrical device, a multi-level screening is carried out to accurately identify electrical circuits whose operating power, operating duration, load changes, and personnel activity intensity are highly correlated. Independent label classification, circuit number binding, and attribute remarks are completed to form a list of circuits related to human flow.

[0029] Simultaneously, a hierarchical classification and manual review mechanism is established: For common public terminal circuits such as elevator equipment, public area corridor lighting, lobby lighting, public water heaters, public restroom exhaust, and fresh air terminals, there is no need to participate in the ratio calculation. They can be directly fixed and identified as human flow-related circuits, reducing the amount of algorithm calculation and ensuring the accuracy of basic circuit identification. For special power circuits that are greatly affected by seasons, holidays, and summer and winter vacations, such as canteen power, logistics auxiliary rooms, and park supporting rooms, the system automatically marks them separately as items to be manually confirmed. The operation and maintenance management personnel will review and confirm each item in combination with the actual use of the building, holiday closed management requirements, and seasonal operation arrangements. All manual review operations are recorded simultaneously with the review time, operator, and review conclusion. The entire process is documented and archived to ensure that the circuit classification results are traceable and verifiable, meeting the traceability requirements for settlement of managed projects.

[0030] By using a refined screening process for each circuit, basic loads that operate constantly year-round, such as central air conditioning units, refrigeration units, fire backup power supplies, UPS systems in computer rooms, and strong and weak current equipment rooms, are effectively eliminated. This prevents large-capacity fixed loads from masking fluctuations in electricity consumption at the end of the flow of people, thus providing clean and effective data samples for subsequent calculations of the flow index.

[0031] S2. After completing the circuit classification, the system only retrieves all marked pedestrian-related circuits for statistical calculation, completely isolating non-pedestrian-related power loads and process loads to ensure that the calculation variables focus solely on the characteristics of personnel activities.

[0032] In this step, historical operating data from the past twelve months are selected as statistical samples. Historical data with the same time period, the same weekday attribute, and the same operating mode are strictly matched, and the median energy consumption of the same period in history is used as the benchmark energy consumption.

[0033] Compared to the mean, the median can effectively avoid interference from abnormal and discrete data such as extreme weather, temporary large-scale events, short-term maintenance, and occasional equipment start-up and shutdown, making the benchmark value more stable and reliable.

[0034] The real-time pedestrian flow index is obtained by combining the actual total power consumption of all pedestrian-related circuits on the day and calculating according to the logic of relative load change.

[0035] Real-time pedestrian flow index can quantitatively reflect the increase or decrease in the overall scale and intensity of personnel activities on a given day relative to historical norms. It does not require the deployment of privacy-related data collection equipment such as facial recognition, infrared passenger flow, access control statistics, and video analysis. It is entirely based on existing electricity metering data for calculation, with no risk of privacy leakage, no hardware modification costs, and is highly compatible with public institution management standards.

[0036] To address frequent real-world issues such as offline smart meters, signal fluctuations, gateway disconnections, scheduled equipment maintenance, and temporary power outages for debugging, multi-level and hierarchical data loss tolerance rules are implemented: In the first stage, for short-term, sporadic data gaps in a single hour, the electricity consumption data from the two valid collection periods before and after the circuit are used to perform mean interpolation to complete the data, ensuring the continuity of time-series data and avoiding the impact of single-point gaps on the overall daily statistical results. The second level is that when a single loop experiences a prolonged and continuous disconnection and data cannot be collected at all, the data of that loop will be automatically blocked on the same day and its participation in the daily flow index calculation will be suspended to avoid the abnormal data from lowering the overall calculation accuracy. Level 3: When multiple circuits malfunction simultaneously within the same day, and the overall proportion of valid and normal pedestrian flow circuits is less than the preset qualified proportion, the overall data for the day is directly determined to be invalid, and the pedestrian flow index is marked as invalid.

[0037] During the period when the pedestrian flow index is invalid, the pedestrian flow disturbance correction calculation will not be performed for the current period. The original calculation result will prevail. The rules are clear and the boundaries are well-defined to prevent erroneous corrections under abnormal data conditions.

[0038] S3. Relying on long-term historical operating data before the official commencement of energy-saving renovation projects and the commissioning of energy-saving equipment as model training samples, ensure that the benchmark data fully corresponds to the original energy consumption state before the renovation, and eliminate the interference of equipment characteristic changes after the renovation on the benchmark.

[0039] Construct a multivariate regression energy consumption benchmark model, with rigorously defined model variables corresponding to actual influencing factors: Using ambient temperature, ambient humidity, weekday attribute identifiers, and the aforementioned calculated pedestrian flow index as four core independent variables, and the building's overall actual electricity consumption as the dependent variable, the study comprehensively quantifies meteorological environmental changes, diurnal humidity differences, work and rest schedules, and pedestrian flow interference.

[0040] By fitting and solving the independent regression coefficients of each variable using regression algorithms, the influence weights of different factors on building energy consumption are clarified, and a specific benchmark calculation model adapted to the energy consumption characteristics of this building is formed.

[0041] After the model is built, it adopts a periodic rolling update mechanism, automatically iterating the historical sample library on a monthly or quarterly basis, continuously absorbing the latest normal operation data, and dynamically correcting key parameters such as temperature coefficient, humidity coefficient, working day coefficient, and pedestrian flow index coefficient.

[0042] Through dynamic optimization, the model can adapt to long-term changes such as the aging of building equipment, renovation of local functional areas, internal office adjustments, changes in energy consumption habits, and seasonal operation and maintenance strategy adjustments. This ensures that the model maintains a good fit throughout the entire energy management cooperation cycle and avoids benchmark distortion caused by model aging.

[0043] S4. After all energy-saving renovations are completed, energy-saving devices are put into stable operation and enter a normalized management and operation cycle, standardized energy-saving calculations will be carried out periodically, using natural months or the settlement cycle agreed in the contract as the unit.

[0044] The system collects hourly ambient temperature and humidity data in real time during the settlement period, simultaneously identifies date attributes, automatically distinguishes between weekdays, rest days, and statutory holidays, and uniformly substitutes all parameters into a pre-trained multivariate regression energy consumption benchmark model to accurately calculate: The theoretical baseline electricity consumption is based on the current climate conditions, work and rest schedules, and personnel activity levels, without implementing any energy-saving renovation measures and maintaining the original equipment status and energy consumption patterns.

[0045] The theoretical baseline electricity consumption output by the model is compared with the total electricity consumption actually measured by the smart meters of the building during the same period to calculate the preliminary energy savings for the current period.

[0046] Preliminary energy savings include energy-saving benefits from equipment and natural energy consumption changes caused by fluctuations in pedestrian traffic, and cannot be directly used as a basis for settlement.

[0047] This invention relies on the regression coefficients of various influencing factors fixed in the benchmark model to accurately extract the corresponding coefficients of the flow index, quantitatively calculate the deviation component between the current actual flow index and the historical normal flow index, and separately quantify and decompose the increase or decrease in energy consumption caused purely by the increase or decrease in personnel and the intensity of activities.

[0048] By uniformly and quantitatively removing the portion of human-induced disturbance from the initial energy savings and eliminating non-energy-saving interference factors, the true energy savings after removing the influence of human fluctuations are obtained. This ensures that the accounting results only objectively reflect the actual energy-saving benefits brought about by energy-saving renovations and energy management optimization, making the settlement basis more equitable.

[0049] S5. The system automatically monitors the absolute value of the pedestrian flow disturbance component to the proportion of the initial energy saving in each settlement cycle. The threshold for this proportion is a configurable parameter in the system backend and can be flexibly customized and adjusted according to different building types, management contract requirements, and project scale to adapt to differentiated management and control needs.

[0050] When the proportion of pedestrian flow disturbance in a single month or period exceeds a preset reasonable threshold, it is determined that the current period's pedestrian flow fluctuation is abnormal and the short-term disturbance is too large, making it unsuitable for separate settlement. The system will then automatically trigger a multi-period combined accounting mechanism.

[0051] The method of merging the average of the current month and the next settlement cycle is preferred to uniformly calculate energy savings. By extending the statistical period, the calculation deviation caused by extreme fluctuations such as short-term surges and drops in traffic, large-scale events, and temporary closures is smoothed out, and extreme data in a single month is prevented from causing settlement unfairness.

[0052] If the abnormal judgment conditions are triggered continuously for two or more consecutive settlement cycles, it indicates that there is a long-term data anomaly or a change in energy consumption conditions on site. The system will automatically generate an anomaly ledger and early warning record, and proactively prompt the operation and maintenance party to initiate the on-site verification process.

[0053] The on-site inspection focused on practical issues such as smart meter metering errors, circuit wiring modifications, unauthorized addition of high-power electrical equipment, unauthorized adjustment of terminal equipment operating modes, aging and malfunctioning metering devices, and circuit splitting and alteration.

[0054] Through closed-loop management including abnormal early warning, consolidated accounting, and on-site investigation, the entire process of energy saving accounting is subject to abnormal control and settlement rule calibration, thereby reducing business disputes in energy management projects from a technical perspective.

[0055] In the context of implementation in medical institutions: Medical institutions are characterized by uninterrupted operation throughout the year, significant regional differences, strong fluctuations in sudden outpatient visits, and inconsistent energy consumption between day and night. Therefore, this solution adds a customized adaptation logic.

[0056] Based on the different functions of outpatient buildings, inpatient wards, emergency areas, fever clinics, infusion wards, and medical technology departments, independent power circuits are separated, and each area completes load ratio determination, pedestrian flow circuit marking, and zonal pedestrian flow index calculation separately to avoid global average calculation masking drastic fluctuations in pedestrian flow in local areas.

[0057] For highly sensitive and volatile areas such as emergency rooms, fever clinics, and nighttime emergency services, a separate weighting correction coefficient is added to amplify the correction weight of sudden changes in population flow on energy consumption in these areas. This aligns with actual energy consumption changes during peak infectious disease seasons, flu cycles, and public health emergencies, significantly improving the accuracy of accounting for medical-related managed projects.

[0058] In the context of implementation in educational institutions: Various types of schools and colleges are characterized by fixed work and rest schedules, long periods of vacancy during winter and summer vacations, and complete separation of energy use between teaching and living areas.

[0059] This invention sets specific correction restrictions for long-term low-traffic vacancy periods such as winter and summer vacations, spring break, and National Day holidays, reasonably constraining the correction range of traffic flow during vacancy periods, and avoiding excessive correction and distortion of energy saving calculations when buildings are almost deserted.

[0060] At the same time, independent energy consumption sub-models are established according to different usage scenarios of teaching buildings, student dormitories, canteens, sports venues, and logistics support buildings. These models match the different activity patterns of personnel during class time, accommodation time, and centralized meal time, adapting to different school operation models such as boarding school and day school, and comprehensively covering the usage scenarios of educational public institutions.

[0061] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this invention should be included within the protection scope of this invention.

[0062] It should be understood that, in order to simplify the present invention and help those skilled in the art understand its various aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes described in a single embodiment or with reference to a single figure. However, the present invention should not be construed as implying that all features included in the exemplary embodiments are essential technical features of the claims of the present invention.

[0063] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0064] It should be understood that the modules, units, components, etc., included in the device of one embodiment of the present invention can be adaptively changed to be placed in a device different from that embodiment. Different modules, units, or components included in the device of the embodiment can be combined into a single module, unit, or component, or they can be divided into multiple sub-modules, sub-units, or sub-components.

[0065] The modules, units, or components in the embodiments of the present invention can be implemented in hardware, in software running on one or more processors, or in a combination thereof. Those skilled in the art should understand that... In practice, microprocessors or digital signal processors (DSPs) can be used to implement embodiments of the invention. The invention can also be implemented on computer program products or computer-readable media for performing some or all of the methods described herein.

Claims

1. A public institution-oriented integrated energy hosting project energy saving measurement and verification method, characterized by: Includes the following steps: S1. Collect hourly energy consumption data for each power circuit in public institutions for at least 12 months, combine the ratio of electricity consumption between non-working days and working days, the ratio of low-load periods at night to total daily electricity consumption, and the inherent attributes of electrical equipment to identify and mark circuits related to pedestrian flow that are strongly correlated with changes in pedestrian flow. S2. Based on the energy consumption data of the marked pedestrian flow-related loops, combined with the historical benchmark energy consumption for the same period, calculate the pedestrian flow index that can characterize the relative change in the intensity of human activity, and configure multi-level data missing fault tolerance rules. S3. Using historical operating data before the implementation of energy-saving renovation, construct a multivariate regression energy consumption benchmark model with ambient temperature, ambient humidity, workday attributes and pedestrian flow index as independent variables. S4. During the operation cycle after the energy-saving renovation, substitute the real-time environmental parameters and date attributes into the energy consumption benchmark model to calculate the theoretical benchmark electricity consumption. Preliminary energy savings are calculated based on the actual electricity consumption of the building, and the energy consumption disturbance component caused by fluctuations in pedestrian flow is quantitatively eliminated to obtain the true energy savings. S5. Real-time monitoring of the proportion of pedestrian disturbance in the initial energy saving. When the proportion exceeds the preset judgment threshold, the multi-cycle merging accounting mechanism is automatically triggered. If there are continuous abnormalities for at least two consecutive settlement cycles, on-site verification is initiated to carry out abnormal control and settlement calibration of energy saving accounting.

2. The energy saving measurement and verification method for a public institution-oriented integrated energy hosting project according to claim 1, characterized in that: In S1, by calculating the ratio of electricity consumption during non-working days to that during working days, and the ratio of electricity consumption during low-load periods at night to that throughout the day, and combining the dual ratio judgment rules, the relevant loops of people flow are initially screened. Fixed public terminal circuits will be directly designated as circuits related to human traffic; special power circuits with significant seasonal fluctuations will be marked for manual confirmation, and the system will record the verification results and the identity of the verifier.

3. The method for measuring and verifying energy savings in integrated energy management projects for public institutions, as described in claim 2. Its characteristic is: In S2, the median of historical energy consumption of the same period type in the past 12 months is used as the benchmark energy consumption, and combined with the actual power consumption of the relevant loops of the pedestrian flow on the day, the pedestrian flow index, which represents the change of the intensity of human activity, is calculated. Data missing fault tolerance rules include: Short-term missing data for a single hour are filled by interpolation using the mean of adjacent time periods; A single-circuit connection that has been interrupted for an extended period will be suspended from operation for the day. If the percentage of valid data collected is insufficient, the pedestrian flow index is marked as invalid.

4. The method for measuring and verifying energy savings in integrated energy management projects for public institutions, as described in claim 3. Its characteristic is that: in S3, the multivariate regression energy consumption benchmark model uses ambient temperature, ambient humidity, workday identification, and pedestrian flow index as independent variables, and building electricity consumption as the dependent variable; The model updates historical sample data periodically and dynamically adjusts the regression coefficients of each influencing factor.

5. The method for measuring and verifying energy savings in a comprehensive energy management project for public institutions as described in claim 4, characterized in that: in S4, the deviation component between the current passenger flow index and the historical normal passenger flow index is quantitatively calculated based on the energy consumption benchmark model coefficient.

6. The method for measuring and verifying energy savings in a comprehensive energy management project for public institutions according to claim 5, characterized in that: S5 In this context, the preset threshold is a configurable proportional parameter; In the event of an anomaly in a single cycle, the energy saving is calculated by merging the average values ​​of two cycles to avoid settlement deviations caused by drastic fluctuations in short-term passenger flow.

7. The method for measuring and verifying energy savings in integrated energy management projects for public institutions according to claim 6, characterized in that: When applied in medical institutions: Divide the power circuits into independent power circuits according to functional zones, and calculate the pedestrian flow index for each area. For areas with high volatility, a weighting correction coefficient is added.

8. The method for measuring and verifying energy savings in integrated energy management projects for public institutions according to claim 7, characterized in that: When applied to public buildings such as educational institutions: Specific modified restrictions have been set for periods of low foot traffic during winter and summer vacations; Energy consumption sub-models were established for different functional areas such as teaching buildings, dormitories, and canteens to match the different activity patterns of personnel.

9. A platform for measuring and verifying energy savings in integrated energy management projects for public institutions, characterized by: include: A processor and a memory, the memory storing a computer program executable by the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-8.

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