CN-121981549-A - IBMS intelligent building energy consumption management system and method

CN121981549ACN 121981549 ACN121981549 ACN 121981549ACN-121981549-A

Abstract

The invention relates to the technical field of energy consumption management and discloses an IBMS intelligent building energy consumption management system and method, wherein the system comprises a data acquisition unit, a control unit and a control unit, wherein the data acquisition unit is used for collecting the internal and external environments and equipment operation information of a building; the first processing unit constructs an operation feature set of each subsystem of the building based on the historical information, and obtains an operation evolution factor set. The second processing unit is used for acquiring an operation characteristic set and an operation evolution factor set of the building sample to be detected, comparing the operation characteristic set and the operation evolution factor set with the first processing unit, calculating operation deviation and determining the deviation degree according to a preset grading standard; the system comprises a first processing unit, a third processing unit, a database management unit and a database analysis unit, wherein the first processing unit is used for analyzing the change direction of the operation characteristics of a building sample to be detected and the change trend of an operation evolution factor set when detecting the first deviation degree, and judging the accuracy of data. The invention can effectively solve the problems of data distortion and lack of dynamic verification.

Inventors

TIAN XIAOBING
Wang guanda
XU SHUSEN
HE YAXUAN
WANG QIAN

Assignees

泽滦科技河北雄安有限公司

Dates

Publication Date: 20260505
Application Date: 20260126

Claims (10)

1. An IBMS intelligent building energy consumption management system, comprising: The system comprises a data acquisition unit, a control unit and a control unit, wherein the data acquisition unit is configured to collect information of the internal and external environments of a building and the running state of equipment, and the information comprises temperature and humidity, illumination intensity, carbon dioxide concentration and behavior mode data of a user, and power consumption, load and running time of an air conditioner, illumination, heating and electric ladder system; the first processing unit is configured to construct an operation feature set of each subsystem in the building based on various information collected by history, and acquire an operation evolution factor set according to the operation trend of the various information; The system comprises a first processing unit, a second processing unit, a first processing unit, a second processing unit and a third processing unit, wherein the first processing unit is used for acquiring a first deviation degree and a second deviation degree of a building sample to be detected, comparing the first deviation degree with the first deviation degree and the second deviation degree in the first processing unit, calculating the operation deviation, and determining the deviation degree according to a preset grading standard; the third processing unit is configured to analyze the change direction of the operation characteristic of the building sample to be detected and the change trend of the operation evolution factor set when the second deviation degree is detected, and judge whether the data are accurate or not; and the database management unit is configured to store a database comprising calibration samples, an operation characteristic set, an operation evolution factor set and preset grading standards.
2. The IBMS intelligent building energy consumption management system of claim 1, further comprising: And the dynamic optimization module is configured to compare the building sample to be tested with the database according to the operation feature set and the response evolution factor before the third processing unit is started, acquire a neighboring building sample, and output the adjustable parameter range and the structural deviation trend risk of the building sample to be tested based on the historical response evolution factor change trend and the label level of the neighboring building sample.
3. The IBMS intelligent building energy consumption management system according to claim 2, wherein the various types of information include power consumption, load, run time, temperature and humidity, illumination intensity, carbon dioxide concentration and user behavior pattern data of each subsystem in the building; the environmental response data is a change curve of temperature and humidity change and illumination intensity change; the load response data is a load change curve of air conditioning, lighting and heating; The energy efficiency change data is a building energy efficiency change curve.
4. An IBMS intelligent building energy consumption management system according to claim 3, wherein the set of operational features constructed by the first processing unit includes: the relative deviation value between the load of each subsystem and the standard load curve, and the fitting residual value between the power consumption of the equipment and the target energy efficiency; coupling offset between illumination intensity and air conditioning system load; the operation evolution factor set constructed by the first processing unit comprises: a first-order slope value of an environmental data change curve and a device load change standard deviation; And response characteristics of the change trend of the energy efficiency data and the environment change.
5. The IBMS intelligent building energy consumption management system of claim 4, wherein the second processing unit specifically comprises: calculating the similarity based on the operation feature set and the operation evolution factor set respectively, and calculating the difference degree between the current building sample to be tested and the historical sample in the database through Euclidean distance, cosine included angle or Mahalanobis distance; generating a deviation vector according to the similarity, and obtaining response deviation degree by normalizing and weighting each index value in the deviation vector; Matching the response deviation degree with a set grading standard threshold value, and respectively endowing a first deviation degree or a second deviation degree; and when the response deviation degree exceeds the second deviation degree upper limit, classifying the response deviation degree as the second deviation degree.
6. The IBMS intelligent building energy consumption management system of claim 5, wherein the third processing unit performs the following operations upon receiving the second degree of deviation: Acquiring the numerical variation direction of the power consumption, load and running time of each subsystem in the running characteristic set in the current building sample to be tested; Meanwhile, the change directions of temperature and humidity change and illumination intensity change in the operation evolution factor set are extracted; if the change direction of each subsystem in the operation feature set is consistent with the change direction of any index in the operation evolution factor set, judging that the structure adjustment is consistent with the energy efficiency response, and finely adjusting the loads of the air conditioner and the illumination at the moment; If the change direction of any parameter in the operation feature set is opposite to the index directions of at least two parameters in the operation evolution factor set, judging that the energy efficiency disturbance is mismatched, and re-evaluating the operation state of the equipment; and if the maximum change rate and the duration of the load change rate are both more than twice of the respective historical samples, outputting an energy efficiency stability abnormality prompt.
7. The IBMS intelligent building energy consumption management system of claim 6, wherein the matching result based on the set of operational characteristics and the set of operational evolution factors performs the following operations: Selecting the first five groups of history samples with the smallest Euclidean distance with the running feature set of the current building sample to be detected as adjacent building samples; Extracting a corresponding operation evolution factor set in the adjacent building sample; Constructing an energy efficiency path track according to the numerical variation direction of the operation evolution factor set and the corresponding energy efficiency optimization record; And forming an energy efficiency optimization prediction sequence based on the change trend of the operation evolution factor set of the building sample to be tested under different equipment operation conditions.
8. The IBMS intelligent building energy consumption management system of claim 7, wherein the dynamic optimization module further comprises a risk assessment unit configured to: Calculating an adjustable parameter interval of the building sample to be tested in the current equipment operation state according to the combined change history of the operation feature set and the operation evolution factor set in the adjacent building sample; Calculating the risk level of the structure migration trend according to whether the fluctuation rate and the duration of the energy efficiency are higher than 95% confidence intervals corresponding to the samples in the database; When the energy efficiency fluctuation rate and the duration time are both in the abnormal interval, a high risk level is output, if only one index is in the abnormal interval, a medium risk level is output, and when both are in the normal interval, a low risk level is output.
9. The IBMS intelligent building energy consumption management system of claim 8, wherein the complementary mechanism is executed when the number of contiguous building samples in the database that satisfy the following conditions is less than three: The Euclidean distance between the operation feature set and the current building sample to be tested is smaller than or equal to a set threshold value; The change trend in the operation evolution factor set is in a set floating range; the supplementing mechanism comprises the steps of collecting three groups of building samples, and collecting corresponding environment response data, equipment load change data and energy efficiency data; and constructing an operation feature set and an operation evolution factor set by a feature construction module, and incorporating the operation feature set and the operation evolution factor set into a database management unit.
10. An IBMS intelligent building energy consumption management method applied to the IBMS intelligent building energy consumption management system of any one of claims 1-9, comprising: s1, collecting information of the internal and external environments of a building and the running state of equipment, wherein the information comprises temperature and humidity, illumination intensity, carbon dioxide concentration, behavior mode data of a user, and power consumption, load and running time of an air conditioner, illumination, heating and elevator subsystem; S2, constructing an operation feature set of each subsystem in the building based on various information collected by history, and acquiring an operation evolution factor set according to the various information; S3, acquiring an operation feature set and an operation evolution factor set of a current building sample to be detected, comparing the operation feature set and the operation evolution factor set with the operation feature set and the operation evolution factor set in a first processing unit, calculating operation deviation, and determining deviation degree according to a preset grading standard, wherein the deviation degree comprises a first deviation degree and a second deviation degree which are sequentially increased in size; s4, when the second deviation degree is detected, analyzing the change direction of the operation characteristic of the building sample to be detected and the change trend of the operation evolution factor set, and judging whether the data are accurate or not; s5, storing a database comprising calibration samples, an operation feature set, an operation evolution factor set and preset grading standards.

Description

IBMS intelligent building energy consumption management system and method Technical Field The invention relates to the technical field of energy consumption management, in particular to an IBMS intelligent building energy consumption management system and method. Background Along with the acceleration of the global urbanization process, building energy efficiency management gradually becomes a core link in a modern building management system. An intelligent building energy consumption management system (IBMS) is taken as an integrated building management system, and aims to maximize energy efficiency, comfort and equipment life in a building through real-time monitoring, analysis and optimal control so as to realize energy conservation, emission reduction and cost control. The IBMS system dynamically adjusts the operation states of subsystems such as air conditioning, lighting, elevator, heating, etc. by collecting the internal and external environment data of the building and the equipment operation state data, so as to optimize the building energy efficiency and ensure the comfort level. However, the prior art has certain defects in the aspects of data acquisition precision, real-time feedback, optimal control and the like. In particular, IBMS systems typically rely on sensors and monitoring equipment for data collection, which are responsible for real-time collection of changes in the internal and external environments of a building, such as temperature, humidity, light intensity, carbon dioxide concentration, etc., and the operating status of various types of equipment, such as power consumption, load, operating duration, etc., of subsystems such as air conditioning, lighting, heating, elevators, etc. However, due to sensor errors, data transmission delays, environmental changes, etc., there is a lack of effective solutions to ensure data accuracy in the prior art. In IBMS systems, if there is an error in the data transmitted by the sensors, inaccuracy of these data will directly affect the decision of energy efficiency optimization. For example, temperature errors measured by temperature sensors may lead to inaccurate load adjustment of the air conditioning system, resulting in deviations in comfort and energy efficiency control within the building, and also data errors in the lighting system may lead to unnecessary energy wastage. In addition, when errors occur in the running state data of the equipment, the service life of the equipment can be predicted inaccurately, and the maintenance and repair strategies of the equipment are affected. Therefore, how to identify and correct the sensor data errors, and ensure the accuracy of data acquisition and the accuracy of real-time feedback becomes a key technical problem for improving the performance and reliability of the IBMS system. Disclosure of Invention The invention aims to provide an IBMS intelligent building energy consumption management system and method for solving the problems. In one aspect, the present invention provides an IBMS intelligent building energy consumption management system, comprising: The system comprises a data acquisition unit, a control unit and a control unit, wherein the data acquisition unit is configured to collect information of the internal and external environments of a building and the running state of equipment, and the information comprises temperature and humidity, illumination intensity, carbon dioxide concentration, behavior mode data of a user, and power consumption, load and running time of an air conditioner, illumination, heating and elevator subsystem; The first processing unit is configured to construct an operation feature set of each subsystem in the building based on various information collected by history, and acquire an operation evolution factor set according to the various information; The system comprises a first processing unit, a second processing unit, a first processing unit, a second processing unit and a third processing unit, wherein the first processing unit is used for acquiring a first deviation degree and a second deviation degree of a building sample to be detected, comparing the first deviation degree with the first deviation degree and the second deviation degree in the first processing unit, calculating the operation deviation, and determining the deviation degree according to a preset grading standard; the third processing unit is configured to analyze the change direction of the operation characteristic of the building sample to be detected and the change trend of the operation evolution factor set when the second deviation degree is detected, and judge whether the data are accurate or not; and the database management unit is configured to store a database comprising calibration samples, an operation characteristic set, an operation evolution factor set and preset grading standards. Further, the method further comprises the following steps: And the dynamic optimization module is conf