Energy efficiency management and control system and method based on operation and maintenance data of smart building

Abstract

The invention discloses an energy efficiency management and control system and method based on intelligent building operation and maintenance data, and belongs to the technical field of big data. Comprising a data acquisition unit, a cleaning processing unit, a network transmission unit, an anomaly detection unit, a calculation processing unit and a display reminding unit, and the data acquisition unit is used for acquiring various data required by the energy efficiency management and control system. The real-time data of the Internet of Things, sensors and the like of the current building system are collected to obtain the preprocessed data of the Internet of Things; and performing basic data quality verification and filtering on the preprocessed data to obtain energy efficiency management and control system basic data based on primary processing and data quality verification. And pushing the basic data and some equipment and product data to a self-research data processing platform for further data processing, data quality verification and data processing, wherein the data subjected to data processing and processing is the business data of the energy efficiency management and control system with data quality.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to an energy efficiency management and control system and method based on smart building operation and maintenance data. Background Art

[0002] In terms of smart building energy efficiency control systems, the quality of IoT building data has a great impact on the effect of energy efficiency control. There are already many solutions for the collection and extraction of relevant data in the pan-building field. For example, many developers in the construction field and the intelligent field have developed data collection models, drivers and data collection and processing platforms, such as Zigbee and Modbus. There are also some solutions that combine multiple technologies, such as the Xiaomi ecosystem, which utilizes and integrates data from multiple intelligent devices to form an ecosystem and data management, so as to carry out energy efficiency control. However, in the existing technical solutions, low data standardization, poor cross-system data sharing, lack of advanced analysis capabilities, and data processing capabilities restrict the full use of system performance. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide an energy efficiency management and control system and method based on smart building operation and maintenance data; it can solve the problems of low data standardization, poor cross-system data sharing, lack of advanced analysis capabilities, and data processing capabilities that restrict the full utilization of system performance.

[0004] Technical solution: To solve the above technical problems, according to one aspect of the present invention, more specifically, an energy efficiency management and control system based on smart building operation and maintenance data includes: a data acquisition unit, a cleaning and processing unit, a network transmission unit, an anomaly detection unit, a calculation and processing unit, and a display and reminder unit;

[0005] Data collection unit: used to collect various data required by the energy efficiency management and control system;

[0006] Cleaning and processing unit: used to clean and process various data required by the energy efficiency management and control system collected by the data collection unit so that the data meets the requirements for use of the energy efficiency management and control system;

[0007] Network transmission unit: used to transmit the data collected by the data collection unit and cleaned and processed by the cleaning and processing unit to the abnormality detection unit and the calculation processing unit, and transmit the detection and judgment results of the abnormality detection unit and the calculation results of the calculation processing unit to the display reminder unit;

[0008] Abnormality detection unit: used to set the threshold of the data to be detected, and to acquire the data collected by the data acquisition unit and cleaned and processed by the cleaning and processing unit and needing abnormality detection, and to perform detection and judgment according to the set threshold of the data to be detected;

[0009] Calculation and processing unit: used for receiving the data collected by the data collection unit and cleaned and processed by the cleaning and processing unit and calculating the corresponding results, and regularly monitoring the calculation results to generate an efficiency report on the building;

[0010] Display reminder unit: used to receive and display the detection and judgment results of the abnormality detection unit, the calculation results of the calculation processing unit, and the generated performance report on the building, and when the detection result judged by the abnormality detection unit is abnormal, it will issue a warning reminder to promptly notify the maintenance personnel to deal with the problem.

[0011] Furthermore, the data collection unit includes: an energy consumption collection module, an area collection module, a heating and cooling quantity collection module and an environmental parameter collection module;

[0012] Energy consumption collection module: used to collect energy consumption data required by the energy efficiency management and control system;

[0013] Area collection module: used to collect area data required by the energy efficiency management and control system;

[0014] Heating and cooling capacity collection module: used to collect data on heating and cooling capacity required by the energy efficiency management and control system;

[0015] Environmental parameter collection module: used to collect indoor and outdoor environmental parameters of buildings required by the energy efficiency management and control system.

[0016] Furthermore, the abnormality detection unit includes: a threshold setting module, a data acquisition module and a detection judgment module;

[0017] Threshold setting module: used to set the threshold of the data to be detected;

[0018] Data acquisition module: used to acquire data collected by the data acquisition unit and cleaned and processed by the cleaning and processing unit and requiring abnormality detection;

[0019] Detection and judgment module: used to detect the data acquired by the data acquisition module and judge whether the detected data is abnormal data according to the threshold value set by the threshold setting module.

[0020] Furthermore, the calculation processing unit includes: a data receiving module, a data calculation module and a report generation module;

[0021] Data receiving module: used for receiving the data collected by the data collection unit and cleaned and processed by the cleaning and processing unit that needs to be calculated and processed;

[0022] Data calculation module: used to calculate the data received by the data receiving module to obtain corresponding results;

[0023] Report generation module: used to regularly monitor the results calculated by the data calculation module and generate performance reports on the building.

[0024] Furthermore, the display reminder unit includes: a data receiving module, a display module and a warning reminder module;

[0025] Data receiving module: used to receive the detection and judgment results of the abnormality detection unit and the calculation results and generated performance reports on the building by the calculation processing unit;

[0026] Display module: used to display the detection and judgment results of the abnormality detection unit received by the data receiving module, the calculation results of the calculation processing unit, and the generated performance report on the building;

[0027] Warning reminder module: used to issue a warning reminder when the detection result judged by the receiving abnormal detection unit is abnormal, so as to promptly notify the maintenance personnel to deal with the problem.

[0028] Furthermore, the cleaning and processing unit performs the following method on cleaning and processing data: perform basic data quality verification and filtering on the pre-processed data, obtain basic data of the energy efficiency management and control system based on preliminary processing and data quality verification, and then push the basic data and some equipment and product data to the self-developed data processing platform for further data processing, data quality verification, and data processing. The data processed and processed is the energy efficiency management and control system business data with data quality.

[0029] Furthermore, the method for implementing the functions of the detection and judgment module is to use the Pandas library to clean and prepare data, fill in missing values, process outliers, perform feature scaling and selection, and then select a suitable machine learning model (such as random forest or XGBoost), divide the data into training sets and test sets, train the model and tune the parameters, evaluate the model performance through the Scikit-learn library to ensure its prediction accuracy, and then deploy the trained model to the actual environment, develop an API interface to receive real-time data and return prediction results, and integrate it into the energy efficiency management and control system to provide decision support. At the same time, use the isolation forest algorithm to build an anomaly detection model, prepare a data set containing normal and abnormal data points, use the IsolationForest class in Scikit-learn to train the model, detect abnormal conditions of new data points, and judge the anomalies according to the set threshold, and promptly notify maintenance personnel to deal with the problem.

[0030] Furthermore, the data calculation module can obtain the energy consumption of each part of the building and the area of ​​each part of the building during the calculation process, and obtain the annual energy consumption per unit area of ​​the building based on the comprehensive analysis of the above data:

[0031]

[0032] Therefore, the energy consumption of a building can be judged based on the annual energy consumption per unit area. The larger the annual energy consumption per unit area, the higher the annual energy consumption of the building. Conversely, the lower the annual energy consumption of the building. i is the annual energy consumption of each energy-consuming device, n is the total number of energy-consuming devices in the building, S i is the area of ​​each part in the building, and m is the total number of parts in the building.

[0033] Furthermore, when performing calculations, the calculation module can obtain the energy efficiency ratio of the building by comprehensively analyzing the heat or cold provided by the equipment during the heating or cooling process and the electrical energy consumed:

[0034]

[0035] Therefore, the energy consumption of a building can be judged based on the energy efficiency ratio. The larger the energy efficiency ratio, the smaller the energy consumption of the building. On the contrary, the smaller the energy efficiency ratio, the larger the energy consumption of the building. i The heating capacity provided by each heating device during the heating process, HW i The electric energy consumed by each heating device during the heating process, x the number of heating devices in the building, CQ i CW is the cooling capacity provided by each refrigeration equipment during the refrigeration process. i is the electrical energy consumed by each refrigeration equipment during the refrigeration process, and y is the number of refrigeration equipment in the building.

[0036] According to another aspect of the present invention, a method for energy efficiency control based on smart building operation and maintenance data is provided. The method is implemented based on the above-mentioned energy efficiency control system based on smart building operation and maintenance data, and specifically includes the following steps:

[0037] S1. Data collection required by the system;

[0038] S2, data cleaning and processing;

[0039] S3, threshold setting and anomaly detection;

[0040] S4, parameter data calculation;

[0041] S5. Data, result display and abnormal warning reminders.

[0042] Beneficial effects: Provide an energy efficiency management and control system and method based on smart building operation and maintenance data, collect real-time data of the Internet of Things, sensors, etc. of the current building system to obtain Internet of Things pre-processing data; perform basic data quality verification and filtering on the pre-processed data to obtain basic data of the energy efficiency management and control system based on preliminary processing and data quality verification. Push the basic data and some equipment and product data to the self-developed data processing platform for further data processing, data quality verification, and data processing. The data processed and processed is the business data of the energy efficiency management and control system with data quality. Through complex business logic processing and processing, the business model and business chart to be displayed are obtained for business display. On the other hand, by establishing a corresponding energy consumption prediction model, using anomaly detection algorithms, isolation forest algorithms, etc. to detect data anomalies, define energy efficiency indicators such as EUI and COP, and quantitatively evaluate building energy efficiency. The designed energy efficiency management and control system automatically adjusts the operating parameters of building equipment according to the analysis results to achieve the best energy efficiency ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the system principle. DETAILED DESCRIPTION

[0044] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] Example 1

[0046] First, the energy consumption data, area data, heating and cooling data, and indoor and outdoor environmental parameters of the building required by the energy efficiency management and control system are collected through the energy consumption collection module, area collection module, heating and cooling capacity collection module, and environmental parameter collection module of the data collection unit. Then, the basic data quality is verified and filtered on the pre-processed data through the cleaning and processing unit to obtain the basic data of the energy efficiency management and control system based on preliminary processing and data quality verification. The basic data and some equipment and product data are pushed to the self-developed data processing platform for further data processing, data quality verification, and data processing. The data that has been processed and processed is the business data of the energy efficiency management and control system with data quality.

[0047] Then, the threshold value of the data to be detected is set through the threshold setting module of the anomaly detection unit, and the data to be detected is obtained through the data acquisition module. Then, the data obtained by the data acquisition module is detected through the detection judgment module and the data to be detected is judged whether it is abnormal data according to the threshold set by the threshold setting module. The function of the detection judgment module is realized by using the Pandas library to clean and prepare data, fill missing values, process outliers, perform feature scaling and selection, and then select a suitable machine learning model (such as random forest or XGBoost), divide the data into training sets and test sets, train the model and tune the parameters, evaluate the model performance through the Scikit-learn library to ensure its prediction accuracy, and then deploy the trained model to the actual environment, develop an API interface to receive real-time data and return prediction results, and integrate it into the energy efficiency management system to provide decision support. At the same time, the isolation forest algorithm is used to build an anomaly detection model, prepare a data set containing normal and abnormal data points, use the IsolationForest class in Scikit-learn to train the model, detect the abnormality of new data points, and judge the abnormality according to the set threshold, and notify the maintenance personnel to deal with the problem in time.

[0048] Afterwards, the data receiving module of the calculation processing unit receives the data collected by the data collection unit and cleaned by the cleaning processing unit, and the data calculation module calculates the data received by the data receiving module to obtain corresponding results.

[0049] During the calculation process, the data calculation module can obtain the energy consumption of each part of the building and the area of ​​each part of the building, and obtain the annual energy consumption per unit area of ​​the building based on the comprehensive analysis of the above data:

[0050]

[0051] Therefore, the energy consumption of a building can be judged based on the annual energy consumption per unit area. The larger the annual energy consumption per unit area, the higher the annual energy consumption of the building. Conversely, the lower the annual energy consumption of the building. i is the annual energy consumption of each energy-consuming device, n is the total number of energy-consuming devices in the building, S i is the area of ​​each part in the building, and m is the total number of parts in the building.

[0052] At the same time, when calculating, the calculation module can obtain the energy efficiency ratio of the building by comprehensively analyzing the heat or cold provided by the equipment during the heating or cooling process and the electrical energy consumed:

[0053]

[0054] Therefore, the energy consumption of a building can be judged based on the energy efficiency ratio. The larger the energy efficiency ratio, the smaller the energy consumption of the building. On the contrary, the smaller the energy efficiency ratio, the larger the energy consumption of the building. i The heating capacity provided by each heating device during the heating process, HW i The electric energy consumed by each heating device during the heating process, x the number of heating devices in the building, CQ i CW is the cooling capacity provided by each refrigeration equipment during the refrigeration process. i is the electrical energy consumed by each refrigeration equipment during the refrigeration process, and y is the number of refrigeration equipment in the building.

[0055] Finally, the data receiving module receives the detection and judgment results of the abnormality detection unit, the calculation results of the computing unit, and the generated performance report on the building through the display reminder unit, and the detection and judgment results of the abnormality detection unit received by the data receiving module, as well as the calculation results of the computing unit and the generated performance report on the building are displayed through the display module, and when the detection result judged by the receiving abnormality detection unit is abnormal, the warning reminder module issues a warning reminder to promptly notify the maintenance personnel to deal with the problem.

[0056] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An energy efficiency management and control system based on smart building operation and maintenance data, characterized in that: include: Data collection unit, cleaning and processing unit, network transmission unit, abnormality detection unit, calculation and processing unit, and display and reminder unit; Data collection unit: used to collect various data required by the energy efficiency management and control system; Cleaning and processing unit: used to clean and process various data required by the energy efficiency management and control system collected by the data collection unit so that the data meets the requirements for use of the energy efficiency management and control system; Network transmission unit: used to transmit the data collected by the data collection unit and cleaned and processed by the cleaning and processing unit to the abnormality detection unit and the calculation processing unit, and transmit the detection and judgment results of the abnormality detection unit and the calculation results of the calculation processing unit to the display reminder unit; Abnormality detection unit: used to set the threshold of the data to be detected, and to acquire the data collected by the data acquisition unit and cleaned and processed by the cleaning and processing unit and needing abnormality detection, and to perform detection and judgment according to the set threshold of the data to be detected; Calculation and processing unit: used for receiving the data collected by the data collection unit and cleaned and processed by the cleaning and processing unit and calculating the corresponding results, and regularly monitoring the calculation results to generate an efficiency report on the building; Display reminder unit: used to receive and display the detection and judgment results of the abnormality detection unit, the calculation results of the calculation processing unit, and the generated performance report on the building, and when the detection result judged by the abnormality detection unit is abnormal, it will issue a warning reminder to promptly notify the maintenance personnel to deal with the problem.

2. The energy efficiency management and control system based on smart building operation and maintenance data according to claim 1 is characterized by: The data collection unit includes: an energy consumption collection module, an area collection module, a heating and cooling quantity collection module and an environmental parameter collection module; Energy consumption collection module: used to collect energy consumption data required by the energy efficiency management and control system; Area collection module: used to collect area data required by the energy efficiency management and control system; Heating and cooling capacity collection module: used to collect data on heating and cooling capacity required by the energy efficiency management and control system; Environmental parameter collection module: used to collect indoor and outdoor environmental parameters of buildings required by the energy efficiency management and control system.

3. The energy efficiency management and control system based on smart building operation and maintenance data according to claim 2 is characterized by: The abnormality detection unit includes: a threshold setting module, a data acquisition module and a detection judgment module; Threshold setting module: used to set the threshold of the data to be detected; Data acquisition module: used to acquire data collected by the data acquisition unit and cleaned and processed by the cleaning and processing unit and requiring abnormality detection; Detection and judgment module: used to detect the data acquired by the data acquisition module and judge whether the detected data is abnormal data according to the threshold value set by the threshold setting module.

4. The energy efficiency management and control system based on smart building operation and maintenance data according to claim 3 is characterized by: The calculation processing unit includes: a data receiving module, a data calculation module and a report generating module; Data receiving module: used for receiving the data collected by the data collection unit and cleaned and processed by the cleaning and processing unit that needs to be calculated and processed; Data calculation module: used to calculate the data received by the data receiving module to obtain corresponding results; Report generation module: used to regularly monitor the results calculated by the data calculation module and generate performance reports on the building.

5. The energy efficiency management and control system based on smart building operation and maintenance data according to claim 4 is characterized by: The display reminder unit includes: a data receiving module, a display module and a warning reminder module; Data receiving module: used to receive the detection and judgment results of the abnormality detection unit and the calculation results and generated performance reports on the building by the calculation processing unit; Display module: used to display the detection and judgment results of the abnormality detection unit received by the data receiving module, the calculation results of the calculation processing unit, and the generated performance report on the building; Warning reminder module: used to issue a warning reminder when the detection result received by the abnormal detection unit is judged to be abnormal, so as to promptly notify the maintenance personnel to deal with the problem.

6. The energy efficiency management and control system based on smart building operation and maintenance data according to claim 1 is characterized by: The cleaning and processing unit performs the following method on cleaning and processing data: perform basic data quality verification and filtering on the pre-processed data, obtain basic data of the energy efficiency management and control system based on preliminary processing and data quality verification, and then push the basic data and some equipment and product data to the self-developed data processing platform for further data processing, data quality verification, and data processing. The data processed and processed is the energy efficiency management and control system business data with data quality.

7. The energy efficiency management and control system based on smart building operation and maintenance data according to claim 5 is characterized by: The method for implementing the functions of the detection and judgment module is to use the Pandas library to clean and prepare data, fill in missing values, process outliers, perform feature scaling and selection, then select a suitable machine learning model (such as random forest or XGBoost), divide the data into training sets and test sets, train the model and tune the parameters, evaluate the model performance through the Scikit-learn library to ensure its prediction accuracy, and then deploy the trained model to the actual environment, develop an API interface to receive real-time data and return prediction results, and integrate it into the energy efficiency management and control system to provide decision support. At the same time, use the isolation forest algorithm to build an anomaly detection model, prepare a data set containing normal and abnormal data points, use the IsolationForest class in Scikit-learn to train the model, detect abnormal situations of new data points, and judge the anomalies according to the set thresholds, and promptly notify maintenance personnel to handle the problems.

8. The energy efficiency management and control system based on smart building operation and maintenance data according to claim 5 is characterized by: During the calculation process, the data calculation module can obtain the energy consumption of each part of the building and the area of ​​each part of the building, and obtain the annual energy consumption per unit area of ​​the building based on the comprehensive analysis of the above data: Therefore, the energy consumption of a building can be judged based on the annual energy consumption per unit area. The larger the annual energy consumption per unit area, the higher the annual energy consumption of the building. On the contrary, the lower the annual energy consumption of the building, the lower the annual energy consumption of the building. i is the annual energy consumption of each energy-consuming device, n is the total number of energy-consuming devices in the building, S i is the area of ​​each part in the building, and m is the total number of parts in the building.

9. The energy efficiency management and control system based on smart building operation and maintenance data according to claim 5 is characterized by: When performing calculations, the calculation module can obtain the energy efficiency ratio of the building by comprehensively analyzing the heat or cold provided by the equipment during the heating or cooling process and the electric energy consumed: Therefore, the energy consumption of a building can be judged based on the energy efficiency ratio. The larger the energy efficiency ratio, the smaller the energy consumption of the building. On the contrary, the smaller the energy efficiency ratio, the larger the energy consumption of the building. i The heating capacity provided by each heating device during the heating process, HW i The electric energy consumed by each heating device during the heating process, x the number of heating devices in the building, CQ i CW is the cooling capacity provided by each refrigeration equipment during the refrigeration process. i is the electrical energy consumed by each refrigeration equipment during the refrigeration process, and y is the number of refrigeration equipment in the building.

10. An energy efficiency control method based on smart building operation and maintenance data, characterized by: The method is implemented based on an energy efficiency management and control system based on smart building operation and maintenance data as described in any one of claims 1 to 9, and specifically includes the following steps: S1. Data collection required by the system; S2, data cleaning and processing; S3, threshold setting and anomaly detection; S4, parameter data calculation; S5. Data, result display and abnormal warning reminder.

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