Building energy management system and method

Abstract

The invention belongs to the technical field of energy management, and particularly provides a building energy management system and method, the system comprises data acquisition modules and a management platform, a building is divided into a plurality of areas, and each area is provided with a data acquisition module for acquiring data generated by energy consumption equipment; the data acquisition module of each area is connected to the management platform through a local terminal; the data acquisition module detects various energy consumption data in a building in real time, and the local terminal acquires the data acquired by the data acquisition module through a local communication channel and transmits the data to the management platform through a remote communication channel; and the management platform performs building energy consumption statistics, equipment operation statistics and parameter statistics on the energy consumption data according to regions, outputs a statistical distribution diagram, performs energy consumption prediction based on a neural network according to the collected energy consumption data, and outputs a prediction result. The integrity and accuracy of data are ensured, and a solid foundation is provided for subsequent energy consumption analysis and management.

Description

Background Art

[0002] In the current widespread practice of building automation system design, although energy conservation has been repeatedly emphasized as a core concept, in actual operation, the importance of energy monitoring and measurement is far from what it should be. This phenomenon has led to a common problem: many built building automation systems, despite their rich functions, are difficult to access the core data of building energy consumption. This lack of data directly limits the depth and breadth of energy management, leaving it at a vague and qualitative level of understanding.

[0003] Specifically, the lack of an effective energy monitoring and metering mechanism means that the building automation system cannot accurately capture and record the energy consumption of various areas in the building in real time, including but not limited to electricity, water, gas, HVAC systems, etc. This data black hole not only deprives managers of an intuitive understanding of the energy usage of the building, but also deprives them of valuable tools for optimizing energy distribution, identifying energy consumption anomalies, and evaluating the effectiveness of energy-saving measures.

[0004] Furthermore, due to the lack of basic data, it is impossible to clarify the energy consumption characteristics of each region and identify energy consumption hotspots based on detailed data analysis, and then set scientific and reasonable energy-saving goals. In addition, ignoring energy monitoring and measurement may also lead to the long-term existence of energy waste without being detected. Some potential high-energy consumption equipment or systems may continue to operate in an inefficient state without data support, which not only increases operating costs, but also creates unnecessary burdens on the environment. Summary of the invention

[0005] In view of the above problems existing in building energy management, the present invention provides a building energy management system and method.

[0006] In a first aspect, the technical solution of the present invention provides a building energy management system, which includes a data collection module and a management platform. The building is divided into multiple areas, and a data collection module is set in each area to collect data generated by energy-consuming equipment. The data collection module in each area is connected to the management platform through a local terminal; The data acquisition module performs real-time detection of various energy consumption data in the building, including equipment information, electricity meter data, water meter data, pressure value, temperature and humidity; The local terminal uses a local communication channel to obtain the data collected by the data collection module, and uses a remote communication channel to transmit the data to the management platform; The management platform conducts building energy consumption statistics, equipment operation statistics, and parameter statistics by area and outputs statistical distribution diagrams. At the same time, it conducts energy consumption prediction based on neural networks according to the collected energy consumption data and outputs the prediction results.

[0007] As a further limitation of the technical solution of the present invention, the data acquisition module includes an electric meter monitoring unit, a wiring circuit monitoring unit and a power distribution equipment monitoring unit; The meter monitoring unit monitors all meters in the building and obtains real-time and historical data on the meter's electricity quantity, current, voltage, power, and power quality; The wiring circuit monitoring unit monitors the main wiring circuit of the power in the building, and displays the line connection status of the distribution system and the operating status of the equipment in the distribution room through the main wiring circuit monitoring; The power distribution equipment monitoring unit monitors the power distribution equipment in the building, including monitoring the on-site conditions of the power distribution room and the operating parameters of the transformers, capacitors, and DC panels in the power distribution room.

[0008] As a further limitation of the technical solution of the present invention, the management platform includes a statistical analysis module, which summarizes and analyzes the data collected by the data acquisition module, and displays the real-time energy consumption, the energy consumption of the day, the building energy consumption of the month, and the cumulative energy consumption of the year.

[0009] As a further limitation of the technical solution of the present invention, the statistical analysis module includes an energy consumption analysis unit and a carbon emission analysis unit; The energy consumption analysis unit completes year-on-year and month-on-month analysis of energy in equipment and energy-consuming areas, and completes analysis of the overall energy consumption, sub-item energy consumption, and average area of ​​the building; The carbon emission analysis unit converts electricity into standard coal through statistics of building energy consumption, and calculates the comprehensive carbon emissions and classified carbon emissions.

[0010] As a further limitation of the technical solution of the present invention, the management platform also includes a report generation module, which generates an energy report based on the statistical analysis results of the statistical analysis module; the report includes the total power consumption of the entire building, the power consumption of the air-conditioning system, the power consumption of the distribution system, and the power consumption of the equipment, and is displayed through an energy consumption curve. By performing comprehensive statistical processing on the historical data recorded at various information points, a report is generated to characterize the operation and use of the equipment in the building.

[0011] As a further limitation of the technical solution of the present invention, the statistical analysis module is also used to perform statistics on the cold source system, the heat source system, and each air-conditioning area respectively; including cooling water flow, cooling water supply / return water temperature, cooling water pump inlet / outlet pressure, chiller inlet / outlet temperature, chiller inlet / outlet pressure, chilled water pump inlet / outlet pressure, chilled water main supply / return water temperature, chilled water main return water flow, chilled water supply / return main pressure difference telemetry, outdoor temperature, and outdoor humidity.

[0012] As a further limitation of the technical solution of the present invention, the management platform includes a prediction module, which determines the regression coefficient by establishing a univariate linear regression model and training based on historical data and a set relative error range, and then determines the linear regression prediction model. The prediction result output by the linear regression prediction model is input into the trained BP neural network prediction model to predict air conditioning energy consumption, equipment power consumption and lighting power consumption respectively.

[0013] As a further limitation of the technical solution of the present invention, the prediction module is also used to predict monthly energy consumption by establishing an exponential smoothing prediction model.

[0014] In a second aspect, the technical solution of the present invention further provides a building energy management method, comprising the following steps: Divide buildings into zones and collect energy consumption data generated by energy-consuming equipment in each zone; Obtain the collected energy consumption data, and perform statistics on the energy consumption data by area, and output the statistical distribution map; According to the collected energy consumption data, the energy consumption of air conditioning, equipment and lighting is predicted based on the linear regression-BP neural network; According to the collected energy consumption data, monthly energy consumption is predicted based on the exponential smoothing prediction model.

[0015] As a further limitation of the technical solution of the present invention, the steps of predicting air conditioning energy consumption, equipment power consumption and lighting power consumption based on the collected energy consumption data based on the linear regression-BP neural network include: Establish a univariate linear regression model and train it based on historical data and the set relative error range to determine the regression coefficient and then determine the linear regression prediction model; The prediction results output by the linear regression prediction model are input into the trained BP neural network prediction model to predict the air conditioning energy consumption, equipment power consumption and lighting power consumption respectively.

[0016] It can be seen from the above technical scheme that the present invention has the following advantages: by setting a data acquisition module in each building area, a comprehensive real-time detection of various energy consumption data in the building is realized, including equipment information, electric meter data, water meter data, pressure value, temperature and humidity, etc., to ensure the integrity and accuracy of the data, and provide a solid foundation for subsequent energy consumption analysis and management. The local terminal uses a local communication channel to quickly obtain the data of the data acquisition module, and transmits the data to the management platform through a remote communication channel, so as to realize the rapid transmission and centralized processing of data and improve the efficiency and accuracy of data processing. The management platform can carry out refined statistics and analysis of building energy consumption, equipment operation and various parameters by region, and output intuitive statistical distribution maps to help managers quickly understand the energy consumption status of each region and provide a scientific basis for formulating energy-saving measures. Based on advanced algorithms such as neural networks, the management platform can make accurate energy consumption predictions based on historical energy consumption data and output prediction results. This helps to plan energy use plans in advance, arrange equipment operation time reasonably, reduce energy waste, and achieve energy consumption optimization and conservation. Overall, the building energy management system can significantly improve the energy efficiency of buildings, reduce operating costs, improve energy management level, and promote sustainable development of buildings. Through intelligent and information-based means, it can achieve comprehensive, accurate, and efficient management of building energy, providing strong support for energy conservation, emission reduction, and green development of modern buildings.

[0017] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a schematic block diagram of a system according to an embodiment of the present invention.

[0020] Figure 2 is a schematic flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] like Figure 1 As shown, an embodiment of the present invention provides a building energy management system, which includes a data collection module and a management platform. The building is divided into multiple areas, and a data collection module is set in each area to collect data generated by energy-consuming equipment. The data collection module in each area is connected to the management platform through a local terminal; The data acquisition module performs real-time detection of various energy consumption data in the building, including equipment information, electricity meter data, water meter data, pressure value, temperature and humidity; The local terminal uses a local communication channel to obtain the data collected by the data collection module, and uses a remote communication channel to transmit the data to the management platform; The management platform conducts building energy consumption statistics, equipment operation statistics, and parameter statistics by area and outputs statistical distribution diagrams. At the same time, it conducts energy consumption prediction based on neural networks according to the collected energy consumption data and outputs the prediction results.

[0023] In some embodiments, the data acquisition module includes an electric meter monitoring unit, a wiring circuit monitoring unit, and a power distribution equipment monitoring unit; The meter monitoring unit monitors all meters in the building and obtains real-time and historical data on the meter's electricity quantity, current, voltage, power, and power quality; The wiring circuit monitoring unit monitors the main wiring circuit of the power in the building, and displays the line connection status of the distribution system and the operating status of the equipment in the distribution room through the main wiring circuit monitoring; The power distribution equipment monitoring unit monitors the power distribution equipment in the building, including the on-site conditions of the power distribution room and the operating parameters of the transformer, capacitor and DC panel in the power distribution room. It should be noted that the data acquisition module includes an energy metering module for measuring low-voltage outgoing line current, voltage, active power and active electric energy, a building control equipment metering module for measuring electric energy of three-phase four-wire power supply equipment, and an electrical equipment metering module for measuring the voltage, current, power factor, active power, active electric energy and reactive electric energy of electronic products.

[0024] In some embodiments, the management platform includes a statistical analysis module that summarizes and analyzes the data collected by the data collection module, and displays the real-time energy consumption, the energy consumption of the day, the building energy consumption of the month, and the cumulative energy consumption of the year.

[0025] It should be further explained that the statistical analysis module includes an energy consumption analysis unit and a carbon emission analysis unit; The energy consumption analysis unit completes year-on-year and month-on-month analysis of energy in equipment and energy-consuming areas, and completes analysis of the overall energy consumption, sub-item energy consumption, and average area of ​​the building; The carbon emission analysis unit converts electricity into standard coal through statistics of building energy consumption, and calculates the comprehensive carbon emissions and classified carbon emissions.

[0026] Correspondingly, the management platform also includes a report generation module, which generates an energy report based on the statistical analysis results of the statistical analysis module; the report includes the total power consumption of the entire building, the power consumption of the air-conditioning system, the power consumption of the transmission and distribution system, and the power consumption of the equipment, and is displayed through an energy consumption curve. By performing comprehensive statistical processing on the historical data recorded at various information points, a report is generated to characterize the operation and use of the equipment in the building.

[0027] In an embodiment of the present invention, the statistical analysis module is also used to perform statistics on the cold source system, the heat source system, and each air-conditioning area respectively; including cooling water flow, cooling water supply / return water temperature, cooling water pump inlet / outlet pressure, chiller inlet / outlet temperature, chiller inlet / outlet pressure, chilled water pump inlet / outlet pressure, chilled water main supply / return water temperature, chilled water main return water flow, chilled water supply / return main pressure difference telemetry, outdoor temperature, and outdoor humidity.

[0028] In some embodiments, the management platform includes a prediction module, which determines the regression coefficient by establishing a univariate linear regression model and training based on historical data and a set relative error range, and then determines the linear regression prediction model. The prediction result output by the linear regression prediction model is input into the trained BP neural network prediction model to predict air conditioning energy consumption, equipment power consumption and lighting power consumption respectively.

[0029] When applying the linear regression prediction model to predict the sub-item energy consumption data, in order to eliminate the impact of the error caused by the influencing factors, the influencing factors are generally taken between 0 and 1. The closer the value is to 0, the closer the prediction result is to the actual result. After applying the linear regression prediction model to predict energy consumption, the secondary prediction is performed through the trained BP neural network, which can greatly reduce the error between the predicted data and the actual data and improve the prediction accuracy.

[0030] It should be noted that the activation function of the BP neural network is ; The input sample sequence is , the output of the middle layer is H, and the output layer outputs Y; ; ; The number of nodes in the input layer is m, and the number of nodes in the middle layer is n. The weight coefficient from the input layer to the middle layer is , the deviation from the input layer to the middle layer is , the weight coefficient from the intermediate layer to the output layer is , the deviation from the intermediate layer to the output layer is , the learning rate is .

[0031] Relative error , where For the expected output, is the actual output value.

[0032] The update formula for weights and biases is as follows:

[0033]

[0034] In the formula, s is the number of iterations, and the number of nodes in the output layer is , is the weight coefficient from the input layer to the intermediate layer for the sth iteration.

[0035] In some embodiments, the prediction module is also used to predict monthly energy consumption by establishing an exponential smoothing prediction model. ; , is the weighting coefficient, is the actual data at time k; and The predicted data and statistical data at time k and time k+1 are , determined through training The prediction model is then determined.

[0036] like Figure 2 As shown, the embodiment of the present invention also provides a building energy management method, comprising the following steps: Step 1: Divide the building into zones and collect energy consumption data generated by energy-consuming equipment in each zone; Step 2: Obtain the collected energy consumption data, and perform statistics on the energy consumption data by area, and output a statistical distribution map; Step 3: According to the collected energy consumption data, the air conditioning energy consumption, equipment power consumption and lighting power consumption are predicted based on the linear regression-BP neural network; Step 4: Forecast monthly energy consumption based on the exponential smoothing forecasting model according to the collected energy consumption data.

[0037] It should be noted that the steps of predicting air conditioning energy consumption, equipment power consumption and lighting power consumption based on the collected energy consumption data and the linear regression-BP neural network include: Establish a univariate linear regression model and train it based on historical data and the set relative error range to determine the regression coefficient and then determine the linear regression prediction model; The prediction results output by the linear regression prediction model are input into the trained BP neural network prediction model to predict the air conditioning energy consumption, equipment power consumption and lighting power consumption respectively.

[0038] In the subsequent process, solutions can be automatically matched according to the prediction results, and the generated solutions can be pushed to users in the form of reports; management and control measures for the operation of different energy consumption systems can be generated according to the solutions to form control strategy instructions; and the control strategy instructions can be transmitted to the corresponding energy-consuming equipment for control.

[0039] When conducting statistical analysis, the power consumption curve of the whole building is output. The daily power consumption is counted by hour, distinguishing between day and night power consumption, the total power consumption of the day, daytime power consumption, nighttime power consumption, daily average power consumption, daily maximum power consumption, and daily minimum power consumption. The monthly power consumption distribution column curve of the whole building is counted by day, and the monthly power consumption is distinguished between working days and non-working days. Through the collection and statistics of real-time operation data, the statistical data of the whole and each part are obtained, and the proportion of all energy consumption and each part to the whole is calculated. The condition for the energy control strategy to maintain the most comfortable environment on the basis of the most reasonable energy consumption is the relevant information of a certain type of equipment or a certain equipment located in a certain area within a certain time period.

[0040] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0041] The embodiment of the building energy management method provided in the embodiment of the present invention belongs to the same inventive concept as the building energy management system of the above-mentioned embodiments. For details not described in detail in the embodiment of the building energy management method, reference can be made to the embodiments of the above-mentioned building energy management system.

[0042] The building energy management system is a combination of the units and algorithm steps of each example described in the embodiments disclosed herein, which can be implemented by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0043] Those skilled in the art will appreciate that various aspects of the building energy management method can be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which can be collectively referred to as "circuit", "module" or "system".

[0044] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person skilled in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, and they shall be within the scope of protection of the present invention.

Claims

1. A building energy management system, characterized in that: The system includes a data collection module and a management platform. The building is divided into multiple areas. Each area is equipped with a data collection module to collect data generated by energy-consuming equipment. The data collection module in each area is connected to the management platform through a local terminal. The data acquisition module performs real-time detection of various energy consumption data in the building, including equipment information, electricity meter data, water meter data, pressure value, temperature and humidity; The local terminal uses a local communication channel to obtain the data collected by the data collection module, and uses a remote communication channel to transmit the data to the management platform; The management platform conducts building energy consumption statistics, equipment operation statistics, and parameter statistics by area and outputs statistical distribution graphs. At the same time, it conducts energy consumption prediction based on neural networks according to the collected energy consumption data and outputs the prediction results.

2. The building energy management system according to claim 1, characterized in that: The data acquisition module includes an electric meter monitoring unit, a wiring circuit monitoring unit and a distribution equipment monitoring unit; The meter monitoring unit monitors all meters in the building and obtains real-time and historical data on the meter's electricity quantity, current, voltage, power, and power quality; The wiring circuit monitoring unit monitors the main wiring circuit of the power in the building, and displays the line connection status of the distribution system and the operating status of the equipment in the distribution room through the main wiring circuit monitoring; The power distribution equipment monitoring unit monitors the power distribution equipment in the building, including monitoring the on-site conditions of the power distribution room and the operating parameters of the transformers, capacitors, and DC panels in the power distribution room.

3. The building energy management system according to claim 2, characterized in that: The management platform includes a statistical analysis module, which summarizes and analyzes the data collected by the data acquisition module, and displays the real-time energy consumption, daily energy consumption, monthly building energy consumption, and cumulative energy consumption for the year.

4. The building energy management system according to claim 2, characterized in that: The statistical analysis module includes an energy consumption analysis unit and a carbon emission analysis unit; The energy consumption analysis unit completes year-on-year and month-on-month analysis of energy in equipment and energy-consuming areas, and completes analysis of the overall energy consumption, sub-item energy consumption, and average area of ​​the building; The carbon emission analysis unit converts electricity into standard coal through statistics of building energy consumption, and calculates the comprehensive carbon emissions and classified carbon emissions.

5. The building energy management system according to claim 4, characterized in that: The management platform also includes a report generation module, which generates energy reports based on the statistical analysis results of the statistical analysis module; the report includes the total power consumption of the building, the power consumption of the air-conditioning system, the power consumption of the transmission and distribution system, and the power consumption of the equipment, and is displayed through an energy consumption curve. By performing comprehensive statistical processing on the historical data recorded at various information points, a report is generated to represent the operation and usage of the equipment in the building.

6. The building energy management system according to claim 5, characterized in that: The statistical analysis module is also used to collect statistics on the cold source system, the heat source system, and each air-conditioning area; Including cooling water flow, cooling water supply / return water temperature, cooling water pump inlet / outlet pressure, chiller inlet / outlet temperature, chiller inlet / outlet pressure, chilled water pump inlet / outlet pressure, chilled water main supply / return water temperature, chilled water main return water flow, chilled water supply / return main pressure difference telemetry, outdoor temperature, and outdoor humidity.

7. The building energy management system according to claim 6, characterized in that: The management platform includes a prediction module, which establishes a univariate linear regression model and trains it based on historical data and a set relative error range to determine the regression coefficient and then determine the linear regression prediction model. The prediction results output by the linear regression prediction model are input into the trained BP neural network prediction model to predict air conditioning energy consumption, equipment power consumption and lighting power consumption respectively.

8. The building energy management system according to claim 7, characterized in that: The forecasting module is also used to forecast monthly energy consumption by establishing an exponential smoothing forecasting model.

9. A building energy management method, characterized in that: The steps include: Divide buildings into zones and collect energy consumption data generated by energy-consuming equipment in each zone; Obtain the collected energy consumption data, and perform statistics on the energy consumption data by area, and output the statistical distribution map; According to the collected energy consumption data, the energy consumption of air conditioning, equipment and lighting is predicted based on the linear regression-BP neural network; According to the collected energy consumption data, monthly energy consumption is predicted based on the exponential smoothing prediction model.

10. The building energy management method according to claim 9, characterized in that: The steps of predicting air conditioning energy consumption, equipment power consumption and lighting power consumption based on the collected energy consumption data and linear regression-BP neural network include: Establish a univariate linear regression model and train it based on historical data and the set relative error range to determine the regression coefficient and then determine the linear regression prediction model; The prediction results output by the linear regression prediction model are input into the trained BP neural network prediction model to predict the air conditioning energy consumption, equipment power consumption and lighting power consumption respectively.

Images