Public building carbon emission limit management method and system

Abstract

The invention discloses a public building carbon emission quota management method and system. The method comprises the steps that energy data and building attribute data, associated with carbon emission, of a public building are acquired; calculating a carbon emission reference value for a carbon emission limit according to the energy data and the building attribute data; acquiring current energy consumption data of the public building within a preset duration in real time, and predicting the current carbon emission of the public building within the preset duration by using a carbon emission prediction model according to the current energy consumption data; and acquiring a preset carbon emission limit, and adjusting the preset carbon emission limit according to the preset carbon emission limit, the current carbon emission and the carbon emission reference value. By utilizing the embodiment of the invention, the energy consumption condition of the public building can be monitored in real time, and the current carbon emission of the building can be predicted and evaluated through deep learning analysis of real-time data, so that a basis is provided for dynamic adjustment of the carbon emission limit to adapt to the actual operation requirement of the building, and more scientific and accurate carbon emission management is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission, and in particular to a method and system for managing carbon emission quotas of public buildings. Background Art

[0002] As the global climate change problem becomes increasingly serious, reducing greenhouse gas emissions has become an important goal for governments and international organizations. As one of the main areas of energy consumption and carbon emissions, the construction industry faces tremendous pressure and challenges. Public buildings are important places for people's daily life and work, and their carbon emissions affect the environmental quality of the city. Therefore, effective management of carbon emissions from public buildings and the formulation of reasonable carbon emission limits have become urgent needs for building management today. At present, although some countries and regions have begun to establish building carbon emission management systems, most methods still rely on traditional energy consumption data statistics and analysis methods, which is a problem that needs to be solved urgently. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for managing carbon emission limits for public buildings to address the deficiencies in the prior art. The method and system can monitor the energy consumption of public buildings in real time, and can also predict and evaluate the current carbon emissions of buildings through deep learning analysis of real-time data, thereby providing a basis for the dynamic adjustment of carbon emission limits to adapt to the actual operational needs of buildings and achieve more scientific and accurate carbon emission management.

[0004] An embodiment of the present application provides a method for managing carbon emission quotas for public buildings, the method comprising: Obtain energy data and building attribute data related to carbon emissions for public buildings; Calculating a carbon emission baseline value for a carbon emission limit based on the energy data and the building attribute data; Collecting the current energy consumption data of the public building for a preset period of time in real time, and predicting the current carbon emissions of the public building within the preset period of time based on the current energy consumption data and using a pre-trained carbon emission prediction model based on a deep learning network; A preset carbon emission limit is obtained, and the preset carbon emission limit is adjusted according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage carbon emissions of public buildings based on the adjusted carbon emission limit.

[0005] Optionally, the calculation formula for the carbon emission baseline value is: Among them, the is the carbon emission benchmark value, is the consumption of the jth energy used in public buildings, is the carbon emission factor of the jth energy used in public buildings, is the weight of the j-th energy used in the k-th usage environment scenario in a public building, is the time impact factor of the kth usage environment scenario, reflecting the energy consumption contribution of the kth usage environment scenario in different time periods. , A parameter that adjusts the impact of a specific energy consumption level on the overall emission baseline value. To adjust the coefficient of the relative contribution of the kth usage environment scenario to the total carbon emissions, is the number of types of energy used, is the number of different usage environment scenarios in public buildings. It is the total area of ​​public buildings.

[0006] Optionally, adjusting the preset carbon emission limit according to the preset carbon emission limit, the current carbon emission amount and the carbon emission baseline value includes: Dynamic adjustment is performed using the following formula: Among them, the is the adjusted carbon emission limit value, To set a carbon emission limit, is the adjustment factor, is the predicted current carbon emissions.

[0007] Optionally, adjusting the preset carbon emission limit according to the preset carbon emission limit, the current carbon emission amount and the carbon emission baseline value includes: Dynamic adjustment is performed using the following formula: Among them, the is the adjusted carbon emission limit value, To set a carbon emission limit, is the adjustment factor, is the predicted current carbon emissions, is the historical carbon emissions of the ith public building, is a historical feedback factor for enhancing the response to historical carbon emission behavior, is the number of historical carbon emissions, is the seasonal influence coefficient, is the holiday impact coefficient.

[0008] Another embodiment of the present application provides a public building carbon emission quota management system, the system comprising: An acquisition module, used for acquiring energy data and building attribute data associated with carbon emissions of public buildings; A calculation module, for calculating a carbon emission baseline value for a carbon emission limit based on the energy data and the building attribute data; A prediction module is used to collect the current energy consumption data of the public building for a preset period of time in real time, and predict the current carbon emissions of the public building within the preset period of time based on the current energy consumption data and using a pre-trained carbon emission prediction model based on a deep learning network; The adjustment module is used to obtain a preset carbon emission limit, and adjust the preset carbon emission limit according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage the carbon emissions of public buildings based on the adjusted carbon emission limit.

[0009] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0010] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0011] Compared with the prior art, the present invention provides a method for managing carbon emission limits for public buildings, which obtains energy data and building attribute data associated with carbon emissions of public buildings; calculates a carbon emission baseline value for a carbon emission limit based on the energy data and the building attribute data; collects current energy consumption data of the public building for a preset period of time in real time, and predicts the current carbon emissions of the public building within the preset period of time based on the current energy consumption data using a pre-trained carbon emission prediction model based on a deep learning network; obtains a preset carbon emission limit, and adjusts the preset carbon emission limit based on the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage the carbon emissions of the public building based on the adjusted carbon emission limit, thereby enabling real-time monitoring of the energy consumption status of the public building, and predicting and evaluating the current carbon emissions of the building through deep learning analysis of real-time data, thereby providing a basis for dynamic adjustment of the carbon emission limit, so as to adapt to the actual operation needs of the building and achieve more scientific and accurate carbon emission management. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A hardware structure block diagram of a computer terminal for a method for managing carbon emission quotas for public buildings provided in an embodiment of the present invention; Figure 2A schematic diagram of a process flow of a method for managing carbon emission quotas for public buildings provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of a public building carbon emission quota management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0014] The embodiment of the present invention first provides a method for managing carbon emission quotas for public buildings. The method can be applied to electronic devices, such as computer terminals, specifically ordinary computers, etc.

[0015] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a public building carbon emission quota management method provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0016] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the public building carbon emission quota management method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0017] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0018] See also Figure 2 , an embodiment of the present invention provides a method for managing carbon emission quotas for public buildings, which may include the following steps: S201, obtaining energy data and building attribute data associated with carbon emissions of public buildings; When implementing a carbon emission quota management method for public buildings, obtaining energy data and building attribute data related to carbon emissions of public buildings is the first and most important step. This process requires the collection of various energy usage including electricity, heating, cooling, natural gas, as well as building attribute data such as building structure information, equipment type and specifications, usage functions, occupancy rate, etc. These data not only provide basic information for evaluating building energy consumption and carbon emissions, but also lay a solid foundation for the subsequent calculation of carbon emission baseline values ​​and the training of carbon emission prediction models.

[0019] Specifically, smart energy consumption monitoring devices such as electricity meters, heat meters, and gas flow meters can be installed in public buildings. These devices can monitor and record the consumption of different energy sources in real time, and transmit the data to a centralized management platform via the Internet. In combination with the building management system, environmental monitoring sensors are deployed to collect real-time data such as temperature, humidity, air quality, and occupancy inside the building. This helps to assess the impact of different environmental factors on energy consumption and carbon emissions. Collect basic information about the building, including building area, floor height, room layout, usage functions, and equipment types, to form a complete database of building attributes. Data can be obtained through building design drawings, asset management systems, or property management platforms. Integrate data from different sources onto a unified platform, and use data cleaning and integration techniques to ensure data integrity and accuracy. Through data processing and analysis, extract indicators related to carbon emissions to provide data support for subsequent carbon emission baseline value calculations and prediction models.

[0020] Through the above steps, energy data and building attribute data related to carbon emissions can be effectively obtained, providing a scientific basis and data support for subsequent carbon emission management.

[0021] S202, calculating a carbon emission baseline value for a carbon emission limit based on the energy data and the building attribute data; In the carbon emission quota management method for public buildings, calculating the carbon emission baseline value is a key step. It analyzes and processes the energy data and building attribute data of the building to derive a benchmark indicator that can be used to evaluate and manage the carbon emissions of public buildings. This benchmark value reflects the carbon emission level of the building under specific conditions, and can fully take into account the consumption of different energy sources and its carbon emission characteristics. During the calculation process, the consumption of various types of energy in the building, carbon emission factors, usage environment scenarios and other information will be used, and the interaction between these factors will be comprehensively considered. The calculation will be performed through a certain mathematical model, and finally a clear carbon emission baseline value will be generated to provide a reference for subsequent carbon emission management decisions.

[0022] The calculated carbon emission baseline value has important significance. First, it provides a scientific basis for setting carbon emission limits for public buildings. Second, it helps identify the main contributors to energy use in buildings, allowing managers to implement energy-saving measures and optimize operating strategies in a more targeted manner, thereby reducing carbon emissions. Furthermore, the application of carbon emission baseline values ​​can also provide quantitative data for building energy efficiency assessments, compliance inspections, and environmental impact assessments. Finally, the calculation and monitoring of baseline values ​​helps to achieve dynamic management and timely adjust management measures to respond to changes in building usage patterns and the impact of the external environment.

[0023] Specifically, a calculation formula for a carbon emission baseline value may be: Among them, the It is the carbon emission benchmark value, which indicates the carbon emission benchmark level of a building under specific conditions, and provides an important reference for subsequent carbon emission management. For example, CEB = 200 tCO2 / year (200 tons of carbon dioxide per year).

[0024] Said The consumption of the jth energy used in public buildings indicates the actual consumption of the jth energy (such as electricity and natural gas) used in public buildings, which directly affects the carbon emission level. For example, EC_1 (electricity consumption) = 100,000 kWh, EC_2 (natural gas consumption) = 50,000 m³.

[0025] Said The carbon emission factor of the jth energy used in public buildings reflects the carbon emissions generated per unit of energy consumption and is a key factor in carbon emission calculations. For example, EF_1 (electricity) = 0.5 kgCO2 / kWh, EF_2 (natural gas) = ​​2.5 kgCO2 / m³.

[0026] Said It is the weight of the j-th energy used in the k-th environment scenario in a public building, indicating the usage ratio of the j-th energy in the k-th environment scenario (such as office, conference room, etc.), reflecting the impact of different environments on energy consumption. For example, if the weight of electricity used in the first office environment is 0.8, that is, U_{1k} = 0.8.

[0027] Said is the time impact factor of the kth usage environment scenario, reflecting the energy consumption contribution of the kth usage environment scenario in different time periods, taking into account the impact of time period changes on energy efficiency. For example, if the second conference room environment scenario is used more frequently on weekdays, T_2 = 1.2. , A parameter that adjusts the impact of a specific energy consumption level on the overall emission baseline value. To adjust the coefficient of the relative contribution of the kth usage environment scenario to the total carbon emissions, is the number of types of energy used, is the number of different usage environment scenarios in a public building, which refers to the number of different environment scenarios in the building, providing the diversity of the environment dimension for calculation. For example, if there are offices, meeting rooms and lounges in the building, then p = 3. It is the total area of ​​public buildings.

[0028] The purpose of calculating the carbon emission baseline (CEB) is to integrate multiple factors in a scientific and systematic way to reflect the carbon emission level of public buildings under specific operating conditions. Through this formula, the impact of different types of energy and environmental scenarios used in buildings on overall carbon emissions can be quantified, making the calculation of carbon emission baselines more accurate and effective. This model can not only help evaluate the carbon emission level of buildings under current conditions, but also provide a basis for setting reasonable carbon emission limits.

[0029] S203, collecting the current energy consumption data of the public building for a preset period of time in real time, and predicting the current carbon emissions of the public building within the preset period of time according to the current energy consumption data using a pre-trained carbon emission prediction model based on a deep learning network; Real-time collection of current energy consumption data of public buildings refers to the dynamic recording of the consumption of various types of energy in the building through monitoring equipment within a preset time period. These energy consumption data usually include information on electricity, heat, cold energy and gas, which can be achieved through the intelligent energy consumption monitoring system installed in the building. The purpose of this step is to obtain the actual energy consumption of the building in a specific period of time, so as to more accurately calculate and predict carbon emissions. On this basis, using a pre-trained deep learning-based carbon emission prediction model, the current collected data can be used to analyze the carbon emission trend and specific values ​​of the building in this time period. This prediction capability can be based on the accumulation of historical data and can also take into account real-time changing environmental factors, thereby providing a scientific basis for building energy consumption management and carbon emission control.

[0030] The implementation of this step has significant effects and significance. First, by real-time monitoring of energy consumption data, the energy consumption patterns and changes of buildings can be identified in a timely manner, providing decision-making support for managers and taking effective energy-saving measures. Secondly, the carbon emission prediction model based on deep learning can accurately predict carbon emissions, allowing building managers to flexibly allocate resources within the carbon emission limit. In addition, through real-time data collection and analysis, the operational efficiency of the building can be improved and the operating costs can be reduced. Finally, this process also enhances the building's ability to adapt to external policy changes, energy price fluctuations and environmental factors, ensuring the stable operation of the building in a dynamic environment.

[0031] Specifically, multiple smart energy consumption monitoring sensors can be installed in public buildings. These devices can record the consumption of electricity, heating, cooling and gas in real time. The sensors should have data collection and transmission functions and be able to send data to the centralized management platform in a timely manner. For example, power sensors can be installed at the electricity meter interface, heat meters can be installed in the heating system, and gas flow meters can be installed on the gas pipeline.

[0032] A preset duration (such as 1 hour, 1 day, etc.) can be set, during which the monitoring device will continue to collect energy consumption data. The system uses IoT technology to transmit this data to the data processing center in real time to ensure the timeliness and accuracy of the data. Data transmission can use a cloud computing platform for later storage and analysis.

[0033] The collected raw energy consumption data is cleaned and preprocessed to remove noise and outliers to ensure data quality. This step may include filtering invalid data (such as outliers generated by equipment failure) and filling missing values. The preprocessed data will be organized into a data format suitable for analysis to ensure the standardization of subsequent model inputs.

[0034] Input the cleaned current energy consumption data into a pre-trained deep learning carbon emission prediction model. The model should be able to handle multiple types of input features, including historical energy consumption data, environmental factors (such as temperature, humidity), and building usage. Deep learning models can include neural network structures, such as long short-term memory networks (LSTM), which are suitable for processing time series data.

[0035] During the model operation, the model generates a real-time forecast of carbon emissions based on the current energy consumption data. The forecast may take into account different usage scenarios and time periods to provide a more detailed carbon emission status. For example, the model can output that the building's expected carbon emissions in the next hour are 150 kg CO2. The forecast results are fed back to the management system and provided to building managers for reference and decision-making. Managers can identify potential energy-saving opportunities or necessary adjustment strategies based on this.

[0036] Through the above steps, the energy consumption data of public buildings can be effectively collected and analyzed in real time to accurately predict the current carbon emissions and provide precise data support and decision-making basis for carbon emission management.

[0037] S204, obtaining a preset carbon emission limit, and adjusting the preset carbon emission limit according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage carbon emissions of public buildings based on the adjusted carbon emission limit.

[0038] In the carbon emission quota management method for public buildings, the step of obtaining a preset carbon emission quota involves setting a target value as the basis for carbon emission monitoring and management. The preset carbon emission quota is usually determined based on a variety of factors such as policy regulations, environmental regulations, building characteristics and historical data. Then, this preset quota is dynamically adjusted based on the current carbon emissions collected in real time and the calculated carbon emission baseline value. This ensures that the management strategy can respond in a timely manner and make adaptive adjustments under the influence of actual energy consumption changes and carbon emission behavior changes, thereby achieving effective carbon emission management.

[0039] This process is of great significance in the sustainable development of public buildings. First, by reasonably setting and dynamically adjusting carbon emission limits, it can effectively promote the rational use of energy in buildings, reduce energy waste and carbon emissions, and thus support the global goals of environmental protection and climate change response. Second, the implementation of this method can enhance building managers' awareness of energy consumption trends, enabling them to take timely adjustment measures, optimize operational strategies, and achieve energy conservation and emission reduction results.

[0040] In one implementation, dynamic adjustment can be performed using the following formula: Among them, the is the adjusted carbon emission limit value, It is a preset carbon emission limit, usually determined with the support of policies or regulations, and is an initial target value. is an adjustment factor, which is used to control the sensitivity to the difference between the current carbon emissions and the baseline value, affecting the adjustment range. The example value can be set to 0.5, indicating a moderate adjustment. is the predicted current carbon emissions.

[0041] This formula is used to dynamically adjust the original carbon emission limit (CE_limit) to adapt to the difference between the current carbon emissions (CE_dynamic) predicted in real time and the baseline carbon emission value (CEB). In this way, managers can respond to actual carbon emissions in a timely manner, ensuring that the building's carbon emission management is more flexible and effective.

[0042] More preferably, in another implementation, dynamic adjustment can be performed using the following formula: Among them, the is the adjusted carbon emission limit value, To set a carbon emission limit, is the adjustment factor, is the predicted current carbon emissions, is the historical carbon emissions of the ith public building, In order to enhance the historical feedback coefficient for the response to historical carbon emission behavior and control the impact of historical carbon emissions on current adjustments, the example value can be set to 0.1. is the number of historical carbon emissions, is the seasonal influence coefficient, which takes into account the impact of different seasons on energy consumption. The example value can be set to 0.2. It is the holiday impact coefficient, which reflects the potential impact of holidays on carbon emissions. The example value can be set to 1 (such as holidays) or 0 (non-holidays).

[0043] This formula takes into account historical carbon emission behavior and the impact of seasonality and holidays when adjusting carbon emission limits, making limit adjustments more comprehensive and forward-looking. By introducing historical feedback coefficients (w_1) and seasonal impact coefficients (w_2), managers can better respond to future carbon emission challenges based on past carbon emission performance and current special circumstances (such as holidays).

[0044] The former adjustment formula simply adjusts the preset carbon emission limit by the difference between the current predicted carbon emissions and the baseline value, which is suitable for relatively static adjustment scenarios. It mainly focuses on the prediction of real-time carbon emissions and the comparison of baseline values, and lacks the use of historical behavior. The latter formula adds feedback on historical carbon emissions and corrections for seasonal and holiday effects on this basis, thereby achieving more comprehensive dynamic adjustments. This makes the formula more flexible in dealing with the complexity of building carbon emissions and can better adapt to changing environments and usage conditions.

[0045] Designing different adjustment formulas reflects the deep understanding and response capabilities of carbon emission management. The first formula is suitable for rapid response to current conditions and is applicable to daily management; while the second formula provides an analysis of historical data, allowing managers to consider the impact of seasonality and special events from a long-term perspective, thus providing a more scientific and reasonable basis for carbon emission management of public buildings. This management method that combines dynamics and history helps to achieve more accurate and responsive carbon emission limit adjustments.

[0046] It can be seen that the energy data and building attribute data associated with carbon emissions of public buildings are obtained; the carbon emission baseline value for the carbon emission limit is calculated based on the energy data and the building attribute data; the current energy consumption data of the public building for a preset period of time is collected in real time, and according to the current energy consumption data, a pre-trained carbon emission prediction model based on a deep learning network is used to predict the current carbon emissions of the public building within the preset period of time; the preset carbon emission limit is obtained, and the preset carbon emission limit is adjusted according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage the carbon emissions of public buildings based on the adjusted carbon emission limit, thereby enabling real-time monitoring of the energy consumption status of public buildings, and predicting and evaluating the current carbon emissions of buildings through deep learning analysis of real-time data, thereby providing a basis for dynamic adjustment of carbon emission limits to adapt to the actual operational needs of buildings and achieve more scientific and accurate carbon emission management.

[0047] Another embodiment of the present invention provides a public building carbon emission quota management system, see Figure 3 , the system may include: An acquisition module 301 is used to acquire energy data and building attribute data associated with carbon emissions of public buildings; A calculation module 302, for calculating a carbon emission baseline value for a carbon emission limit based on the energy data and the building attribute data; The prediction module 303 is used to collect the current energy consumption data of the public building for a preset period of time in real time, and predict the current carbon emissions of the public building within the preset period of time according to the current energy consumption data using a pre-trained carbon emission prediction model based on a deep learning network; The adjustment module 304 is used to obtain a preset carbon emission limit, and adjust the preset carbon emission limit according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage the carbon emissions of public buildings based on the adjusted carbon emission limit.

[0048] It can be seen that the energy data and building attribute data associated with carbon emissions of public buildings are obtained; the carbon emission baseline value for the carbon emission limit is calculated based on the energy data and the building attribute data; the current energy consumption data of the public building for a preset period of time is collected in real time, and according to the current energy consumption data, a pre-trained carbon emission prediction model based on a deep learning network is used to predict the current carbon emissions of the public building within the preset period of time; the preset carbon emission limit is obtained, and the preset carbon emission limit is adjusted according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage the carbon emissions of public buildings based on the adjusted carbon emission limit, thereby enabling real-time monitoring of the energy consumption status of public buildings, and predicting and evaluating the current carbon emissions of buildings through deep learning analysis of real-time data, thereby providing a basis for dynamic adjustment of carbon emission limits to adapt to the actual operational needs of buildings and achieve more scientific and accurate carbon emission management.

[0049] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0050] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, obtaining energy data and building attribute data associated with carbon emissions of public buildings; S202, calculating a carbon emission baseline value for a carbon emission limit based on the energy data and the building attribute data; S203, collecting the current energy consumption data of the public building for a preset period of time in real time, and predicting the current carbon emissions of the public building within the preset period of time according to the current energy consumption data using a pre-trained carbon emission prediction model based on a deep learning network; S204, obtaining a preset carbon emission limit, and adjusting the preset carbon emission limit according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage carbon emissions of public buildings based on the adjusted carbon emission limit.

[0051] It can be seen that the energy data and building attribute data associated with carbon emissions of public buildings are obtained; the carbon emission baseline value for the carbon emission limit is calculated based on the energy data and the building attribute data; the current energy consumption data of the public building for a preset period of time is collected in real time, and according to the current energy consumption data, a pre-trained carbon emission prediction model based on a deep learning network is used to predict the current carbon emissions of the public building within the preset period of time; the preset carbon emission limit is obtained, and the preset carbon emission limit is adjusted according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage the carbon emissions of public buildings based on the adjusted carbon emission limit, thereby enabling real-time monitoring of the energy consumption status of public buildings, and predicting and evaluating the current carbon emissions of buildings through deep learning analysis of real-time data, thereby providing a basis for dynamic adjustment of carbon emission limits to adapt to the actual operational needs of buildings and achieve more scientific and accurate carbon emission management.

[0052] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0053] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0054] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, obtaining energy data and building attribute data associated with carbon emissions of public buildings; S202, calculating a carbon emission baseline value for a carbon emission limit based on the energy data and the building attribute data; S203, collecting the current energy consumption data of the public building for a preset period of time in real time, and predicting the current carbon emissions of the public building within the preset period of time according to the current energy consumption data using a pre-trained carbon emission prediction model based on a deep learning network; S204, obtaining a preset carbon emission limit, and adjusting the preset carbon emission limit according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage carbon emissions of public buildings based on the adjusted carbon emission limit.

[0055] It can be seen that the energy data and building attribute data associated with carbon emissions of public buildings are obtained; the carbon emission baseline value for the carbon emission limit is calculated based on the energy data and the building attribute data; the current energy consumption data of the public building for a preset period of time is collected in real time, and according to the current energy consumption data, a pre-trained carbon emission prediction model based on a deep learning network is used to predict the current carbon emissions of the public building within the preset period of time; the preset carbon emission limit is obtained, and the preset carbon emission limit is adjusted according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage the carbon emissions of public buildings based on the adjusted carbon emission limit, thereby enabling real-time monitoring of the energy consumption status of public buildings, and predicting and evaluating the current carbon emissions of buildings through deep learning analysis of real-time data, thereby providing a basis for dynamic adjustment of carbon emission limits to adapt to the actual operational needs of buildings and achieve more scientific and accurate carbon emission management.

[0056] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A method for managing carbon emission quotas for public buildings, characterized in that: The method comprises: Obtain energy data and building attribute data related to carbon emissions for public buildings; Calculating a carbon emission baseline value for a carbon emission limit based on the energy data and the building attribute data; Collecting the current energy consumption data of the public building for a preset period of time in real time, and predicting the current carbon emissions of the public building within the preset period of time based on the current energy consumption data and using a pre-trained carbon emission prediction model based on a deep learning network; A preset carbon emission limit is obtained, and the preset carbon emission limit is adjusted according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage carbon emissions of public buildings based on the adjusted carbon emission limit.

2. The method according to claim 1, characterized in that The calculation formula for the carbon emission baseline value is: Among them, the is the carbon emission benchmark value, is the consumption of the jth energy used in public buildings, is the carbon emission factor of the jth energy used in public buildings, is the weight of the j-th energy used in the k-th usage environment scenario in a public building, is the time impact factor of the kth usage environment scenario, reflecting the energy consumption contribution of the kth usage environment scenario in different time periods. , A parameter that adjusts the impact of a specific energy consumption level on the overall emission baseline value. To adjust the coefficient of the relative contribution of the kth usage environment scenario to the total carbon emissions, is the number of types of energy used, is the number of different usage environment scenarios in public buildings. The total area of ​​public buildings.

3. The method according to claim 2, characterized in that The adjusting the preset carbon emission limit according to the preset carbon emission limit, the current carbon emission amount and the carbon emission baseline value includes: Dynamic adjustment is performed using the following formula: Among them, the is the adjusted carbon emission limit value, To set a carbon emission limit, is the adjustment factor, is the predicted current carbon emissions.

4. The method according to claim 2, characterized in that The adjusting the preset carbon emission limit according to the preset carbon emission limit, the current carbon emission amount and the carbon emission baseline value includes: Dynamic adjustment is performed using the following formula: Among them, the is the adjusted carbon emission limit value, To set a carbon emission limit, is the adjustment factor, is the predicted current carbon emissions, is the historical carbon emissions of the ith public building, is a historical feedback factor for enhancing the response to historical carbon emission behavior, is the number of historical carbon emissions, is the seasonal influence coefficient, is the holiday impact coefficient.

5. A public building carbon emission quota management system, characterized in that: The system comprises: An acquisition module, used for acquiring energy data and building attribute data associated with carbon emissions of public buildings; A calculation module, for calculating a carbon emission baseline value for a carbon emission limit based on the energy data and the building attribute data; A prediction module is used to collect the current energy consumption data of the public building for a preset period of time in real time, and predict the current carbon emissions of the public building within the preset period of time based on the current energy consumption data and using a pre-trained carbon emission prediction model based on a deep learning network; The adjustment module is used to obtain a preset carbon emission limit, and adjust the preset carbon emission limit according to the preset carbon emission limit, the current carbon emissions and the carbon emission baseline value, so as to manage the carbon emissions of public buildings based on the adjusted carbon emission limit.

6. The system according to claim 5, characterized in that The calculation formula for the carbon emission baseline value is: Among them, the is the carbon emission benchmark value, is the consumption of the jth energy used in public buildings, is the carbon emission factor of the jth energy used in public buildings, is the weight of the j-th energy used in the k-th usage environment scenario in a public building, is the time impact factor of the kth usage environment scenario, reflecting the energy consumption contribution of the kth usage environment scenario in different time periods. , A parameter that adjusts the impact of a specific energy consumption level on the overall emission baseline value. To adjust the coefficient of the relative contribution of the kth usage environment scenario to the total carbon emissions, is the number of types of energy used, is the number of different usage environment scenarios in public buildings. The total area of ​​public buildings.

7. The system according to claim 6, characterized in that The adjustment module is specifically used for: Dynamic adjustment is performed using the following formula: Among them, the is the adjusted carbon emission limit value, To set a carbon emission limit, is the adjustment factor, is the predicted current carbon emissions.

8. The system according to claim 6, characterized in that The adjustment module is specifically used for: Dynamic adjustment is performed using the following formula: Among them, the is the adjusted carbon emission limit value, To set a carbon emission limit, is the adjustment factor, is the predicted current carbon emissions, is the historical carbon emissions of the ith public building, is a historical feedback factor for enhancing the response to historical carbon emission behavior, is the number of historical carbon emissions, is the seasonal influence coefficient, is the holiday impact coefficient.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

Images