A building energy-saving diagnosis data analysis system

Abstract

The application belongs to the technical field of building energy saving, and relates to a building energy saving diagnosis data analysis system, which screens standard evening power loss data of a target building in a reference period through environmental temperature similarity, and carries out statistical characteristic analysis to obtain an evening power loss baseline of the target building. According to evening power loss data of the target building in a diagnosis period, deviation characteristics of the target building relative to the evening power loss baseline are analyzed, and whether the target building has energy saving optimization space is judged. If the target building has energy saving optimization space, air conditioning equipment is dynamically adjusted according to personnel activity characteristics of each floor, and the number of elevator operation and standby floor positions in the evening are dynamically configured according to elevator equipment use demand characteristics. By comparing evening power loss conditions of the target building before and after optimization, energy saving effect is evaluated and feedback is executed, so that the precision and efficiency of building energy saving diagnosis are improved, and effective use of energy is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of building energy saving, and relates to a building energy saving diagnosis data analysis system. BACKGROUND

[0002] Building energy saving is the core link to promote the development of green buildings and sustainable use of energy. With the accelerated advancement of urbanization, global building energy consumption continues to rise. Office buildings have high personnel density and complex equipment systems, resulting in high operating costs and large power losses. Therefore, it is urgent to carry out energy saving and optimization management of office buildings to reduce energy waste and operating costs.

[0003] In the prior art, there are also some related solutions involving office building energy saving diagnosis. For example, the office building operation performance batch diagnosis method, energy saving method and system disclosed in Chinese Patent No. CN114819766A, which obtains energy consumption indicators and energy consumption states according to historical energy consumption information of office buildings, and determines energy consumption modification objects and modification directions of office buildings to improve energy utilization efficiency.

[0004] In addition, the power network optimization method and system for office buildings disclosed in Chinese Patent No. CN114266187B constructs corresponding digital twin systems through building information and historical power consumption data of each power consumption area, obtains regional control signals of each power consumption area according to initial optimization parameters input into each digital twin subsystem, and adjusts the operation parameters of each power consumption equipment to improve the operation efficiency of the power network of the office building.

[0005] The above-mentioned solutions propose some solutions for office building energy saving diagnosis, but the prior art still has the following limitations:

[0006] (1) The prior art relies on historical energy consumption average or static prediction model and does not consider the influence of environmental factors on power loss, which may lead to the disconnection between the formulated energy consumption optimization strategy and the actual energy consumption demand, and the inability to accurately adjust the actual energy consumption situation, thereby affecting the overall energy utilization efficiency of the building.

[0007] (2) The prior art analyzes and processes the whole day energy consumption as a homogeneous whole without considering the problem that the personnel activity in the evening time zone of the office building is greatly reduced compared with the daytime, but the high power loss equipment such as air conditioner and elevator is still running continuously, causing energy waste, and the subsequent formulated energy saving strategy cannot fully play the energy saving space of the time zone.

[0008] (3) The prior art only focuses on the comparison of overall energy consumption, ignores the internal correlation between the device running state and the personnel activity mode, and makes the formulated energy-saving optimization strategy too extensive, sacrifices the user experience and energy-saving potential, and leads to the lack of scene adaptability of the energy-saving strategy. SUMMARY

[0009] To solve the problems presented in the background art, an architectural energy-saving diagnosis data analysis system is proposed.

[0010] The technical solution adopted by the present application to solve its technical problems is: the present application provides an architectural energy-saving diagnosis data analysis system, comprising: a loss baseline construction module, a power loss analysis module, an air conditioner energy-saving optimization module, an elevator energy-saving optimization module and an energy-saving effect evaluation module.

[0011] The loss baseline construction module is connected with the power loss analysis module, the power loss analysis module is connected with the air conditioner energy-saving optimization module and the elevator energy-saving optimization module respectively, the air conditioner energy-saving optimization module is connected with the energy-saving effect evaluation module, and the elevator energy-saving optimization module is connected with the energy-saving effect evaluation module.

[0012] The loss baseline construction module divides the historical monitoring period into a reference period and a diagnosis period, selects standard evening power loss data in the reference period of the target building based on environmental temperature similarity, and obtains the evening power loss baseline of the target building through statistical feature analysis.

[0013] The power loss analysis module extracts the evening power loss data of the target building in the diagnosis period, analyzes the deviation characteristics thereof relative to the evening power loss baseline, judges whether the target building has energy-saving optimization space, and if so, performs energy-saving optimization on the air conditioner equipment and the elevator equipment respectively.

[0014] The air conditioner energy-saving optimization module extracts the evening operation data of the lighting equipment of each floor of the target building in the diagnosis period, calculates the personnel activity characteristics of each floor in each evening period, and adjusts the air conditioner equipment parameters at the low personnel activity time point, and stops the air conditioner equipment at the time point close to the personnel leaving time.

[0015] The elevator energy-saving optimization module extracts the evening calling data of the elevator equipment of the target building in the diagnosis period, analyzes the use demand characteristics of each floor, and dynamically configures the number of evening elevators and the standby floor position.

[0016] The energy-saving effect evaluation module compares the evening power loss status of the target building before and after optimization, evaluates the energy-saving effect and performs feedback.

[0017] Compared with the prior art, the present application has the following beneficial effects:

[0018] The application constructs an environmental temperature feature vector and a similarity matrix by comprehensively analyzing the environmental temperature difference between different diagnosis days and reference days, screens out reference days most similar to the environmental temperature of the diagnosis period, and obtains standard evening power loss data, thereby ensuring the accuracy of the power loss data and making the energy-saving optimization strategy fit the actual energy consumption demand.

[0019] The application focuses on the analysis of power loss in the evening time zone, extracts the evening power loss data of the target building in the diagnosis period through the power loss analysis module, analyzes the deviation characteristics of the target building relative to the evening power loss baseline, locates the optimization potential of the evening time zone, and fully utilizes the energy-saving space of the time zone, thereby overcoming the limitation of the prior art that the whole day energy consumption is regarded as a homogeneous whole for analysis and processing.

[0020] The application adjusts the air conditioning equipment parameters during low personnel activity, and stops using the air conditioning equipment when the personnel are about to leave, and dynamically configures the number of elevators running and the standby floor position in the evening by analyzing the elevator usage demand of each floor, thereby avoiding the strategy of the prior art that only focuses on the comparison of overall energy consumption, and improving the scene adaptability of the energy-saving strategy.

[0021] The application compares the evening power loss status of the target building before and after optimization, evaluates the energy-saving effect, and performs feedback, so that it can accurately judge whether the building energy-saving achieves the expected effect, and then continuously optimize and improve the building energy-saving strategy. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0023] Figure 1 It is a schematic diagram of the module connection of the application.

[0024] Figure 2 It is a logic flow chart of the application for obtaining the evening power loss baseline of the target building in the loss baseline construction module.

[0025] Figure 3 It is a logic flow chart of the application for judging whether the target building has energy-saving optimization space in the power loss analysis module. DETAILED DESCRIPTION

[0026] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0027] Please refer to Figure 1 As shown in the drawings, the building energy-saving diagnosis data analysis system provided by the present application comprises a loss baseline construction module, a power loss analysis module, an air conditioner energy-saving optimization module, an elevator energy-saving optimization module and an energy-saving effect evaluation module.

[0028] The loss baseline construction module is connected with the power loss analysis module, the power loss analysis module is connected with the air conditioner energy-saving optimization module and the elevator energy-saving optimization module respectively, the air conditioner energy-saving optimization module is connected with the energy-saving effect evaluation module, and the elevator energy-saving optimization module is connected with the energy-saving effect evaluation module.

[0029] The loss baseline construction module divides the historical monitoring period into a reference period and a diagnosis period, selects standard evening power loss data in the reference period of the target building based on the similarity of the ambient temperature, and obtains the evening power loss baseline of the target building through statistical feature analysis.

[0030] As a preferred, the process of selecting standard evening power loss data in the reference period of the target building based on the similarity of the ambient temperature comprises: calling the ambient temperature data of the evening time zone of each diagnosis working day in the diagnosis period and each reference working day in the reference period, constructing the ambient temperature feature vector of each diagnosis working day and each reference working day according to the ambient temperature difference and the average ambient temperature of the evening time zone.

[0031] Based on the cosine similarity calculation formula, the ambient temperature similarity of each reference working day and each diagnosis working day is calculated respectively, and a similarity matrix is constructed based on this, wherein the rows and columns of the matrix correspond to the diagnosis working days and the reference working days respectively, and the element values of the matrix correspond to the ambient temperature similarity of the diagnosis working days and the reference working days.

[0032] The similarity matrix is summed by column to obtain the cumulative similarity of each reference working day relative to the diagnosis period.

[0033] According to the cumulative similarity, each reference working day is sorted in descending order, and the reference working days in the front of the sequence are selected as standard reference working days, and the power loss data of the evening time zone of each standard reference working day is integrated to obtain standard evening power loss data.

[0034] It should be noted that the cosine similarity calculation formula is a formula for calculating the environment temperature similarity of each reference working day and each diagnosis working day. wherein , correspond to the environment temperature difference of the first reference working day, the first diagnosis working day, respectively, , correspond to the average environment temperature of the first reference working day, the first diagnosis working day, respectively, wherein is the number of each reference working day, , is the number of each diagnosis working day, .

[0035] It should be noted that the preset number should be more than twice the number of diagnosis working days.

[0036] The embodiment of the application constructs an environment temperature feature vector and a similarity matrix by comprehensively analyzing the environment temperature difference between different diagnosis working days and reference working days, selects each reference working day most similar to the environment temperature of the diagnosis period, and obtains the standard night power consumption data, thereby ensuring the accuracy of the power consumption data and making the energy saving optimization strategy fit the actual energy consumption demand.

[0037] Referring to FIG. 6, as a kind of preferred, the process of obtaining the night power consumption baseline of target building by statistical feature analysis includes: according to the preset time interval, the night time zone is divided, and each night time period is generated. Figure 2

[0038] The power consumption data of each standard reference working day in the same night time period is arranged in ascending order to construct the power consumption ascending data set of each night time period.

[0039] Based on the preset percentile value, quantile calculation is performed on each power consumption ascending data set, and the corresponding quantile value is extracted as the power consumption reference value, thereby obtaining the power consumption reference value of each night time period, wherein the preset percentile value is a statistical parameter for specifying the relative position in the ascending data arrangement, and the quantile value is the specific value of the preset percentile value in the power consumption ascending data set.

[0040] The power consumption reference values of the night time periods are combined in time sequence to construct the night power consumption baseline of the target building.

[0041] ​It should be noted that the above preset percentile value is preset according to the statistical characteristics of the historical energy consumption data of the target building in the reference period, and the setting principle is: selecting a lower percentile value, which can be exemplified as a 25th percentile value, to reflect the actual lower energy consumption level that can be achieved under normal operating conditions after excluding abnormally high energy consumption days.

[0042] The above quantile calculation can be realized by linear interpolation or nearest neighbor method.

[0043] Here, the ascending data set of power consumption in a certain evening period is According to the 25th percentile value, the quantile value can be calculated by linear interpolation method as 93.5, which is taken as the power consumption reference value of the evening period. Here, the unit is kWh by default.

[0044] The power consumption analysis module extracts the evening power consumption data of the target building in the diagnosis period, analyzes its deviation characteristics relative to the evening power consumption baseline, judges whether the target building has energy saving optimization space, and if so, performs energy saving optimization on the air conditioning equipment and elevator equipment.

[0045] Referring to Figure 3 As a preferred, the specific acquisition process of judging whether the target building has energy saving optimization space is as follows: removing the maximum value and minimum value in the power consumption data of the same evening period of each diagnosis working day, calculating the arithmetic mean of the remaining data to obtain the average power consumption data of each evening period, drawing the average power consumption line of the evening time zone of the diagnosis period, and comparing it with the evening power consumption baseline segment by segment, and counting the positive deviation value and positive deviation time length of the power consumption of each evening period.

[0046] Taking the positive deviation time length of the power consumption of each evening period as a weight factor, the positive deviation value of the power consumption of each evening period is linearly weighted and fused to obtain the diagnosis period deviation parameter.

[0047] The length of each evening period and its corresponding power consumption reference value are linearly weighted and calculated to obtain the power consumption reference parameter, and the ratio of the diagnosis period deviation parameter to the diagnosis period deviation parameter is calculated as the power consumption deviation evaluation index.

[0048] If the power consumption deviation evaluation index is greater than the preset optimization judgment threshold, it is determined that the target building has energy saving optimization space.

[0049] It should be noted that the evening power loss positive deviation value is the deviation amplitude of the average power loss line of the evening time zone of the diagnosis cycle in the corresponding line segment of each evening period, which is higher than the evening power loss baseline, and the positive deviation duration is the duration of the deviation of the average power loss line of the evening time zone of the diagnosis cycle in the corresponding line segment of each evening period, which is higher than the evening power loss baseline.

[0050] The embodiment of the present application focuses on the analysis of the power loss of the evening time zone. The power loss analysis module extracts the evening power loss data of the target building in the diagnosis cycle, analyzes the deviation characteristics thereof relative to the evening power loss baseline, locates the optimization potential of the evening time zone, and fully utilizes the energy saving space of the time zone, thereby overcoming the limitation of the prior art that the whole day energy consumption is regarded as a homogeneous whole for analysis and processing.

[0051] The air conditioning energy saving optimization module extracts the evening operation data of the lighting devices of each floor of the target building in the diagnosis cycle, calculates the personnel activity characteristics of each evening period of each floor, and adjusts the air conditioning device parameters at the low personnel activity time point and stops the air conditioning device at the time point close to the personnel leaving time point.

[0052] As a preferred, the process of calculating the personnel activity characteristics of each evening period of each floor includes: calling the on-off loop state data of the lighting devices of an office area of a floor in an evening time zone of a diagnosis working day, when the on-off loop state of the lighting devices of the office area is in an open state at a time point, and the open duration is greater than a preset duration, the time point is recorded as a reference personnel leaving time point of the floor.

[0053] The power loss data of the lighting devices of the floor is monitored according to a preset sampling period, and the power loss change rate of each sampling time point and the previous sampling node is calculated, when the absolute power loss change rate of a sampling time point is greater than a preset change threshold, and the power loss data is less than a preset power loss threshold, the sampling time point is recorded as a reference low personnel activity time point.

[0054] The reference personnel leaving time point data set and the reference low personnel activity time point data set of the floor of each diagnosis working day are obtained.

[0055] Each time point in the reference personnel leaving time point data set is sequentially taken as a reference time point, the absolute cumulative deviation duration of each time point and the reference time point is obtained, the reference time point corresponding to the minimum absolute cumulative deviation duration is screened as a personnel leaving time point, and the low personnel activity time point is obtained from the reference low personnel activity time point data set, and the personnel leaving time point and the low personnel activity time point of each floor of the target building are obtained.

[0056] It should be noted that the lighting device switches in the switch circuit of the office lighting device are connected in parallel, and when any lighting device switch is not disconnected, the switch circuit remains in a connected state, and it can be inferred that there is personnel activity in the office, and when all lighting device switches are disconnected, the switch circuit is in a disconnected state, and it can be preliminarily inferred that there is no personnel activity in the office on the floor, and by analyzing whether the disconnection duration is greater than the preset duration, the personnel off-site time point can be more accurately determined.

[0057] It should be noted that when personnel activity is frequent, the absolute power consumption change rate will be smaller, and when personnel activity decreases, the absolute power consumption change rate will be relatively larger, so when the absolute power consumption change rate is greater than the preset change threshold, it can be preliminarily inferred that the personnel activity in the period is at a low level, and the power consumption data directly reflects the energy consumption of the building at a specific time point, and when the power consumption data is less than the preset power consumption threshold, it can be ensured that the determined time point is the reference low personnel activity time point.

[0058] As a preferred, the air conditioning device parameters are adjusted at the low personnel activity time point, and the air conditioning device is stopped near the personnel off-site time point, and the specific process includes: adjusting the temperature of the air conditioning device on each floor at the low personnel activity time point according to the season of the diagnosis period.

[0059] The time point of a preset duration before the personnel off-site time point on each floor is defined as the personnel off-site time point, and the air conditioning device is stopped at the personnel off-site time point on each floor.

[0060] It should be noted that the process of adjusting the temperature of the air conditioning device on each floor at the low personnel activity time point includes: adjusting the temperature of the air conditioning device according to the preset temperature adjustment parameter, and if the target building diagnosis period is in a high-temperature season, the positive adjustment is performed, and if it is in a low-temperature season, the reverse adjustment is performed.

[0061] It should be noted that a reasonable buffer period is set before personnel off-site, which can fully utilize the indoor residual temperature to maintain the relative stability of the indoor temperature, avoid energy waste caused by sudden stop of the device, and the preset duration can be exemplarily set to 30 minutes, which can guarantee the utilization efficiency of the residual temperature before personnel off-site, and effectively balance the energy saving goal and the demand for indoor comfort.

[0062] The elevator energy-saving optimization module extracts the evening calling data of the target building elevator device in the diagnosis period, analyzes the use demand characteristics of each floor, and dynamically configures the number of evening elevators and standby floor positions.

[0063] As a kind of preferred, the process of analyzing each floor use demand characteristics includes: extracting the evening call data of elevator equipment in each diagnostic workday, screening the operation combination with the same calling floor and target floor in each evening period and counting its operation times, to build the elevator equipment transfer matrix of each evening period, wherein the row and column of the matrix correspond to the calling floor and target floor respectively, and the element value of the matrix corresponds to the statistical operation times.

[0064] The elevator equipment transfer matrix is normalized to obtain the elevator equipment transfer probability matrix of each evening period of each diagnostic workday.

[0065] The arithmetic mean of the corresponding elements of the elevator equipment transfer probability matrix of each diagnostic workday is calculated by grouping according to the evening period to obtain the reference elevator equipment transfer probability matrix of each evening period.

[0066] The collection of elevator equipment call time points of each diagnostic workday is retrieved to obtain the number of times of calling elevator equipment in each evening period of each diagnostic workday.

[0067] The number of times of calling elevator equipment in the same evening period of different diagnostic workdays is averaged to obtain the reference number of times of calling elevator equipment in each evening period, and the elevator equipment call frequency in each evening period is calculated.

[0068] As a kind of preferred, the process of dynamically configuring the number of evening elevator operation is as follows: calling the preset evening elevator operation number configuration table stored in the cloud database, determining the elevator equipment demand level according to the elevator equipment call frequency in each evening period, and matching the corresponding elevator operation number, to dynamically configure the number of elevator operation in each evening period.

[0069] It should be noted that the preset evening elevator operation number configuration table includes the elevator equipment call frequency in each evening period corresponding to different elevator equipment demand levels and the adapted elevator operation number.

[0070] As a kind of preferred, the process of dynamically configuring the evening elevator standby floor position is as follows: according to the reference elevator equipment transfer probability matrix of each evening period, screening the calling floor with the highest probability of calling elevator equipment in each evening period, and taking it as the elevator equipment standby floor in each evening period.

[0071] The embodiment of the application adjusts the air conditioning equipment parameters during low personnel activity, and stops the air conditioning equipment when approaching personnel departure by calculating the personnel activity characteristics of each floor in each evening period, and dynamically configures the number of evening elevator operation and standby floor position by analyzing the elevator use demand of each floor, avoiding the strategy of only focusing on the overall energy consumption comparison in the prior art, and improving the scene adaptability of energy saving strategy.

[0072] As a kind of preferred, the energy-saving effect is evaluated, and the specific acquisition process is as follows: with the diagnostic cycle termination time as starting point, extend the same number of days as diagnostic cycle to define optimization period, and execute the energy-saving optimization measures of air conditioning equipment and elevator equipment in optimization period.

[0073] The nighttime power consumption data of the target building in the optimization period is retrieved, the average power consumption line of the nighttime time zone of the optimization period is drawn, and the integral operation is carried out on the average power consumption line of the nighttime time zone of the optimization period and the average power consumption line of the nighttime time zone of the diagnostic cycle respectively, to obtain the optimization period power consumption coefficient and the diagnostic cycle power consumption coefficient.

[0074] The relative change rate of the optimization period power consumption coefficient relative to the diagnostic cycle power consumption coefficient is calculated, and the negative value thereof is taken as the energy-saving effect evaluation index.

[0075] The embodiment of the present application compares the nighttime power consumption conditions of the target building before and after optimization, evaluates the energy-saving effect and performs feedback, so that it can be accurately judged whether the building energy saving achieves the expected effect, and the building energy saving strategy is continuously optimized and improved.

[0076] The cloud database is used in the system during execution, for storing the preset nighttime elevator running quantity configuration table, and all parameters in the cloud database are derived from external provision.

[0077] The above formulas are all dimensionless numerical calculations, the formula is obtained by collecting a large amount of data to simulate the recent real situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0078] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized in the form of computer program product wholly or partially.

[0079] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0080] In addition, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0081] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0082] Finally, the above merely provides the preferred embodiments of the present application, but is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A building energy saving diagnosis data analysis system, characterized by, include: The historical monitoring period is divided into a reference period and a diagnostic period. Standard nighttime power loss data of the target building within the reference period are screened based on the similarity of ambient temperature, and the nighttime power loss baseline of the target building is obtained through statistical feature analysis. Extract the nighttime power consumption data of the target building during the diagnostic period, analyze its deviation characteristics relative to the nighttime power consumption baseline, and determine whether there is room for energy-saving optimization in the target building. If there is, perform energy-saving optimization on the air conditioning equipment and elevator equipment respectively; otherwise, operate normally. Extract the nighttime operation data of the lighting equipment on each floor of the target building during the diagnostic period, estimate the human activity characteristics of each floor at each nighttime, adjust the air conditioning equipment parameters at low human activity times, and turn off the air conditioning equipment when it is close to the time when people leave the site. Extract nighttime call data of elevator equipment in the target building during the diagnostic cycle, analyze the usage demand characteristics of each floor, and dynamically configure the number of elevators in operation at night and the location of standby floors. Compare the nighttime power consumption of the target building before and after optimization, evaluate the energy-saving effect and provide feedback; The process of filtering standard nighttime power loss data for target buildings within a reference period based on ambient temperature similarity includes: retrieving ambient temperature data for each diagnostic workday of the diagnostic period and each reference workday within the reference period in the evening time zone; constructing ambient temperature feature vectors for each diagnostic workday and each reference workday based on the ambient temperature difference and average ambient temperature in the evening time zone; calculating the ambient temperature similarity between each reference workday and each diagnostic workday based on the cosine similarity calculation formula, and constructing a similarity matrix, where the rows and columns of the matrix correspond to the diagnostic workday and the reference workday, respectively, and the element values ​​of the matrix correspond to the ambient temperature similarity between the diagnostic workday and the reference workday; summing the similarity matrix column by column to obtain the cumulative similarity of each reference workday relative to the diagnostic period; sorting each reference workday in descending order based on the cumulative similarity, selecting a preset number of reference workdays at the top of the sequence as standard reference workdays, and integrating the power loss data of each standard reference workday in the evening time zone to obtain standard nighttime power loss data; The process of obtaining the evening power loss baseline of the target building through statistical feature analysis includes: dividing the evening time zone according to a preset time interval to generate each evening time period; sorting the power loss data of the same evening time period on each standard reference weekday in ascending order to construct the power loss ascending order dataset for each evening time period; performing quantile calculation on each power loss ascending order dataset based on a preset percentile value, extracting the corresponding quantile value as the power loss benchmark value, thereby obtaining the power loss benchmark value for each evening time period; and combining the power loss benchmark values ​​of each evening time period in chronological order to construct the evening power loss baseline of the target building.

2. The building energy-saving diagnosis data analysis system according to claim 1, characterized in that, The specific process for determining whether the target building has room for energy-saving optimization is as follows: The average power consumption data of each evening period is obtained by removing the maximum value and the minimum value from the power consumption data of the same evening period of each diagnosis working day and calculating the arithmetic mean of the remaining data, and the average power consumption line of the evening time zone of the diagnosis period is drawn and compared with the evening power consumption baseline in each period, and the positive deviation value and the positive deviation length of each evening period are counted; The power consumption positive deviation value of each evening period is linearly weighted and fused by taking the power consumption positive deviation length of each evening period as a weight factor, and the diagnosis period deviation parameter is obtained; The power consumption reference parameter is obtained by linearly weighting and calculating the length of each evening period and its corresponding power consumption reference value, and the ratio of the diagnosis period deviation parameter to the diagnosis period deviation parameter is calculated as the power consumption deviation evaluation index; If the power consumption deviation evaluation index is greater than the preset optimization judgment threshold, it is determined that the target building has energy saving optimization space.

3. The building energy-saving diagnosis data analysis system according to claim 1, wherein The process of calculating the personnel activity characteristics of each evening period of each floor includes: The switch loop state data of the lighting equipment in the office area of a floor in the evening time zone of a diagnosis working day is called, and when the switch loop state of the lighting equipment in the office area is in an open state at a certain time point and the open duration is greater than the preset duration, the time point is recorded as the reference personnel leaving time point of the floor; The power consumption data of the lighting equipment of the floor is monitored according to a preset sampling period, and the power consumption change rate of each sampling time point and its previous sampling node is calculated. When the absolute power consumption change rate of a certain sampling time point is greater than a preset change threshold, and the power consumption data is less than a preset power consumption threshold, the sampling time point is recorded as a reference low personnel activity time point; The reference personnel leaving time point data set and the reference low personnel activity time point data set of the floor of each diagnosis working day are obtained; Each time point in the reference personnel leaving time point data set is sequentially taken as a reference time point, the absolute cumulative deviation length of each time point and the reference time point is obtained, and the reference time point corresponding to the minimum absolute cumulative deviation length is selected as the personnel leaving time point. In the same way, the low personnel activity time point is obtained from the reference low personnel activity time point data set, and the personnel leaving time point and the low personnel activity time point of each floor of the target building are obtained.

4. The building energy-saving diagnosis data analysis system according to claim 1, characterized in that, The specific process of adjusting the air conditioning equipment parameters at the low personnel activity time point and stopping the air conditioning equipment near the personnel leaving time point includes: According to the season in which the diagnosis period is located, the temperature of the air conditioning equipment of each floor is adjusted at the low personnel activity time point; The time point of a preset duration before the personnel leaving time point of each floor is defined as the time point near the personnel leaving time point, and the air conditioning equipment of each floor is stopped at the time point near the personnel leaving time point.

5. The building energy-saving diagnosis data analysis system according to claim 1, characterized in that, The process of analyzing the use demand characteristics of each floor includes: The elevator equipment call data of each diagnosis working day is extracted, the operation combination with the same calling floor and target floor in each evening period is screened, and the operation times are counted, so as to construct the elevator equipment transfer matrix of each evening period, wherein the rows and columns of the matrix correspond to the calling floor and the target floor respectively, and the element value of the matrix corresponds to the counted operation times; The elevator equipment transfer matrix is normalized to obtain the elevator equipment transfer probability matrix of each evening period of each diagnostic working day; The arithmetic mean of the corresponding elements of the elevator equipment transfer probability matrix of each diagnostic working day is calculated according to the evening period grouping to obtain the reference elevator equipment transfer probability matrix of each evening period; The diagnostic working day elevator equipment calling time point set is called to obtain the number of times of calling elevator equipment in each evening period of each diagnostic working day; The number of times of calling elevator equipment in the same evening period of different diagnostic working days is averaged to obtain the reference number of times of calling elevator equipment in each evening period, and the elevator equipment calling frequency in each evening period is calculated based thereon.

6. The building energy-saving diagnosis data analysis system according to claim 5, wherein, The process of dynamically configuring the number of evening elevator runs is as follows: The preset evening elevator run number configuration table stored in the cloud database is called, the elevator equipment demand level is determined according to the elevator equipment calling frequency in each evening period, and the corresponding elevator run number is matched to dynamically configure the number of elevator runs in each evening period.

7. The building energy-saving diagnosis data analysis system according to claim 5, characterized in that, The process of dynamically configuring the evening elevator standby floor position is as follows: According to the reference elevator equipment transfer probability matrix of each evening period, the calling floor with the highest probability of calling elevator equipment in each evening period is screened and used as the elevator equipment standby floor in each evening period.

8. The building energy-saving diagnosis data analysis system according to claim 2, characterized in that, The specific process of evaluating the energy saving effect is as follows: Taking the end time of the diagnostic period as the starting point, the same number of days as the diagnostic period is extended backward to define an optimization period, and the air conditioning equipment and elevator equipment energy saving optimization measures are executed in the optimization period; The evening power consumption data of the target building in the optimization period is called, the average power consumption line of the evening time zone of the optimization period is drawn in the same way as the method of obtaining the average power consumption line of the evening time zone of the diagnostic period, and the integral operation is performed on the average power consumption line of the evening time zone of the optimization period and the average power consumption line of the evening time zone of the diagnostic period, respectively, to obtain the optimization period power consumption coefficient and the diagnostic period power consumption coefficient; The relative change rate of the optimization period power consumption coefficient relative to the diagnostic period power consumption coefficient is calculated, and the negative value thereof is taken as the energy saving effect evaluation index.

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