Regional building carbon emission optimization system and method

Abstract

The invention discloses a regional building carbon emission optimization system and method, and aims to solve the problem of multiple optimization objectives of balancing carbon emission, economic cost and user comfort of a regional building. The system comprises a sensing layer, a transmission layer, a platform layer and an application layer, the sensing layer collects multi-source data such as building energy consumption, environment states and regional energy, and an energy consumption load prediction module of the platform layer predicts future load based on historical data; the multi-target collaborative optimization module constructs a target function with the minimum regional total carbon emission, the lowest total energy cost and the highest user average comfort as targets, a non-dominated sorting genetic algorithm is adopted for solving to obtain an optimal regional energy scheduling strategy, and finally the strategy is executed through the regional energy hub module. Through data-driven collaborative scheduling, the contradiction among emission reduction, economy and comfort is scientifically balanced, and the comprehensive energy efficiency and the low-carbon operation level of regional building groups are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of regional building energy management and carbon emission control technology, specifically to a regional building carbon emission optimization system and method. Background Technology

[0002] Currently, building carbon emission optimization mainly targets individual buildings, such as adjusting the air conditioning and lighting systems of a single building to reduce carbon emissions by reducing the energy consumption of that building. However, this approach has the following key shortcomings: Lack of regional collaboration: The sharing of energy resources among multiple buildings within the region is not considered, such as photovoltaic power from photovoltaic power plants, charging and discharging of energy storage batteries, and waste heat from waste heat recovery systems, resulting in low utilization of regional energy resources. Insufficient data integration: Data collection in existing systems is scattered across different devices or systems, lacking unified preprocessing and integration, making it difficult to support real-time dynamic optimization; Single optimization objective: Existing energy-saving control strategies often take the lowest energy consumption or lowest operating cost as the single optimization objective, making it difficult to effectively quantify and coordinate the balance among the three mutually restrictive and conflicting objectives of carbon emissions, economic costs and user comfort.

[0003] Therefore, there is an urgent need for a regional building carbon emission optimization system and method that can integrate regional energy resources, balance multiple objectives, and support real-time dynamic optimization to address the shortcomings of existing technologies. Summary of the Invention

[0004] To address the problems existing in the background technology, this invention proposes a regional building carbon emission optimization system and method to solve the problems of lack of regional collaboration, low resource utilization, and insufficient practicality of optimization schemes caused by single-building optimization, data dispersion, and single-objective orientation in regional building energy management. It realizes the unified scheduling of energy resources of multiple buildings in the region, the integration and preprocessing of multi-source data, and the dynamic balance of multiple objectives such as carbon emissions, energy costs, and user comfort.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A regional building carbon emission optimization system, applied in a smart city community, comprises a sensing layer, a transmission layer, a platform layer, and an application layer connected in sequence, wherein: The perception layer is used to collect multi-source data of buildings in the area, including building energy consumption data, environmental status data, regional energy resource data, and user behavior data. The transport layer is used to receive multi-source data collected by the perception layer, perform edge preprocessing on the multi-source data, and transmit the preprocessed data to the platform layer through communication protocol adaptation. The platform layer is used to store, process, and optimize the data transmitted by the transport layer, including: The data storage module is used to store the preprocessed data; The carbon emission accounting module calculates the carbon emissions of a single building and the total carbon emissions of the region based on building energy consumption data and carbon emission factors. The energy load forecasting module predicts the energy load of buildings in the area for future periods based on building energy consumption data and environmental status data. The multi-objective collaborative optimization module, based on energy load forecasting results and combined with user comfort constraints, balances carbon emissions, energy costs, and comfort objectives to generate a regional energy dispatch strategy. The regional energy hub module is used to execute regional energy dispatch strategies and dispatch the transmission and distribution of energy between buildings within the region; The application layer is used to display the results of the platform layer, including a visualization interface, to present regional carbon emission data, predicted energy load for future periods, energy cost-effectiveness, and user comfort status.

[0006] Specifically, in the sensing layer, the building energy consumption data includes the electricity consumption data, gas consumption data, and heat consumption data of each building in the area, which are collected through NB-IoT smart meters, gas meters, and heat meters; Environmental data includes building indoor environmental data and regional outdoor meteorological data. Indoor environmental data includes building indoor temperature and humidity data. Concentration data was collected via LoRa wireless sensors, while outdoor meteorological data, including regional outdoor light intensity, wind speed, and outdoor temperature data, was obtained from regional weather stations. Regional energy resource data includes photovoltaic power resource data of photovoltaic power plants in the region, charging and discharging power data of energy storage batteries, and waste heat resource data of waste heat recovery systems, which are collected through Modbus and OPC UA standardized interfaces. User behavior data includes user comfort preference data, which is collected through smart terminal devices within the building.

[0007] Specifically, the transmission layer preprocesses the multi-source data collected by the perception layer, including data cleaning and data aggregation. Data cleaning removes outliers from the multi-source data, and data aggregation aggregates minute-level data from the multi-source data into 15-minute or hour-level data. The preprocessed multi-source data is transmitted to the platform layer through an adapted communication protocol, including 5G, LoRa, and NB-IoT protocols.

[0008] Specifically, the carbon emission accounting module of the platform layer is used to calculate the carbon emissions of a single building and the total carbon emissions of the region. The formula for calculating the carbon emissions of a single building is as follows: ; in, For the first Electricity consumption data for the building As a carbon emission factor for electricity, For the first Gas consumption data for the building Carbon emission factors from fuel gas For the first Heat consumption data of the building Thermal carbon emission factor; The formula for calculating total regional carbon emissions is: ; in This represents the number of buildings within the area.

[0009] Specifically, the energy load forecasting module of the platform layer includes a data processing unit, a model training unit, and a load forecasting unit connected in sequence, wherein: The data processing unit is used to standardize historical building energy consumption data and outdoor meteorological data collected by the sensing layer and stored in the data storage module. The processing steps specifically include data alignment and normalization. The data alignment step will standardize the data within the region. Historical building energy consumption data and corresponding outdoor meteorological data for each building are synchronized and aligned with timestamps at fixed intervals to form a dataset linking energy consumption and the environment. The normalization process uses Min-Max normalization to map the aligned dataset to... Intervals, eliminating dimensional differences, are expressed by the following formula: ; in, The raw data includes building energy consumption data and outdoor meteorological data. , These are the minimum and maximum values ​​of the data sequence, respectively. The data is after normalization; The model training unit uses a long short-term memory neural network to train the energy load prediction model. The input is a historical feature vector, and the output is the predicted future energy load value. For the th For each building, the historical feature vector at time step t is: ; in, For the first Building The building energy consumption data vector after the data processing unit has normalized it for the specified time period. They are respectively The outdoor temperature, light intensity, and wind speed data for the specified time period are normalized by the data processing unit. Training samples are constructed using a sliding window method with a 24-hour window length. Each sample includes a historical feature vector sequence from the past 24 hours. The horizontal prediction is for the next 6 hours, with one prediction point per hour. The loss function is as follows: ; in, The number of training samples. The first Building Forecast values ​​of electricity, gas, and heat energy consumption during the specified time period. For the first Building The actual values ​​of electricity, gas, and heat consumption data for each time period; The load forecasting unit uses a trained energy load forecasting model to predict the energy load of buildings in the area for future periods. ; in, , For the first Building The energy load forecast vector for each time period is generated; finally, the forecast values ​​in the energy load forecast vector are converted into actual building energy consumption forecast data through inverse normalization.

[0010] Specifically, the multi-objective collaborative optimization module of the platform layer is used to generate regional energy dispatch strategies, and its workflow includes: (1) Receive the necessary input data for performing multi-objective optimization, including the energy load forecast value from the energy load forecasting module, as well as user comfort preference data and regional energy resource data collected by the perception layer; (2) Individual coding is performed on the regional energy dispatch strategy, and the population size is set. Several individuals are generated by uniformly distributed random initialization. Each individual represents a dispatch strategy. The coding structure is as follows: ; in, This is the waste heat transfer ratio matrix. [Indicates factory] Towards Architecture The proportion of transferred waste heat to the total waste heat of the factory meets the requirements. , , , The number of factories in the region. The number of buildings in the area; A photovoltaic power distribution ratio matrix. [Indicates photovoltaic power station] Towards Architecture The proportion of allocated photovoltaic power to the total photovoltaic power generation of the photovoltaic power station meets the requirements. , , The number of photovoltaic power stations in the region; Power matrix for charging energy storage batteries, Represents architecture For energy storage batteries The charging power, This is the energy storage battery discharge power matrix. Indicates energy storage battery Towards Architecture The discharge power; (3) To minimize the total carbon emissions, the total energy cost, and the average user comfort in the future region, a multi-objective optimization objective function is constructed: ; ; ; ; in, For architecture Carbon emissions from electricity are calculated using the following formula: ; For photovoltaic power station Total photovoltaic power resources Carbon emission factor for electricity; For architecture Carbon emissions from natural gas are calculated using the following formula: ; For the factory Total waste heat resources Carbon emission factors from fuel gas; For architecture The formula for calculating thermal carbon emissions is: ; Thermal carbon emission factor; , , Buildings Electricity costs, gas costs, and heating costs; electricity costs The calculation formula is: ; Real-time electricity price for the power grid; For architecture User comfort rating The calculation formula is: ; in, , , As a weighting factor for comfort index, , , Indoor environmental data including temperature, humidity, and carbon dioxide concentration. , , These are the allowable boundary values ​​for the indoor environment. , , Comfort preference data set for users; (4) After decoding the scheduling strategy for each individual, calculate the corresponding objective function value; (5) Individual fitness is assessed using non-dominated ordination and crowding distance. Based on the objective function value, individuals in the population are divided into different non-dominated layers, and the crowding distance of each individual in its layer is calculated: ; in, Corresponding to the objective function , For individuals The One target value, For individuals The first in the layer The maximum and minimum values ​​of the target values; (6) Randomly select two individuals from the population, and select the individual with better non-dominant layer or greater crowding distance to enter the next generation. Repeat this step until all individuals in the population have been selected. The selected individuals are subjected to single-point cross-intersection in pairs. The specific logic of single-point cross-intersection is as follows: randomly select a cross-intersection point between the individual codes and swap the partial codes after the cross-intersection point of the two individuals. Gaussian mutation is performed on the crossover individuals with a mutation probability of 0.1. The mutation logic is as follows: randomly select an encoding parameter and add Gaussian noise to the parameter. (7) When the number of iterations reaches the maximum value, output the optimal solution in the first non-dominated layer, and select the optimal individual from them according to the user priority to decode the regional energy scheduling strategy.

[0011] A method for optimizing regional building carbon emissions based on the above system includes the following steps: S1. Collect multi-source data of buildings within the area, including: Building energy consumption data: Electricity consumption data, gas consumption data, and heat consumption data for each building in the area; Environmental status data: indoor environmental data of the building and outdoor meteorological data of the area. Indoor environmental data includes indoor temperature data, humidity data, etc. Concentration data, outdoor meteorological data including regional outdoor light intensity data, wind speed data and outdoor temperature data; Regional energy resource data: photovoltaic power resources data of photovoltaic power plants in the region, charging and discharging power data of energy storage batteries, and waste heat resources data of waste heat recovery systems; User behavior data: User comfort preference data; S2. Perform edge preprocessing on multi-source data to remove outliers and aggregate minute-level data into 15-minute or hour-level data to reduce data transmission volume. S3. Store preprocessed multi-source data; S4. Calculate the carbon emissions of a single building and the total carbon emissions of the region based on building energy consumption data and carbon emission factors; S5. Based on historical building energy consumption data and outdoor meteorological data, predict future energy load using a long short-term memory neural network (LSTM). S6. Based on the energy load forecast results and combined with user comfort constraints, balance carbon emissions, energy costs and comfort objectives, and generate a regional energy dispatch strategy through a non-dominated sorting genetic algorithm. S7. Implement regional energy dispatching strategies to dispatch the transmission and distribution of energy between buildings within the region; S8 displays regional carbon emission data, future energy load forecasts, energy cost-effectiveness, and user comfort status through a visual interface.

[0012] In summary, the beneficial technical effects of the present invention are as follows: 1. Regional collaborative optimization to improve energy resource utilization: By integrating energy resources such as photovoltaic power stations, energy storage batteries, and waste heat recovery systems within the region, energy sharing among multiple buildings can be achieved, reducing energy waste.

[0013] 2. Multi-source data integration to support real-time dynamic optimization: By collecting multi-source data such as building energy consumption, environmental status, regional energy resources and user behavior through the perception layer, and after edge preprocessing and unified storage, data support is provided for carbon emission accounting, energy load prediction and optimization, and dynamic scheduling strategy updates are realized.

[0014] 3. Multi-objective balance to improve the practicality of the solution: The non-dominated sorting genetic algorithm is used for multi-objective collaborative optimization to minimize the total carbon emissions in the region, reduce energy costs and maintain user comfort, avoiding the problem of decreased user experience caused by single-objective optimization. Attached Figure Description

[0015] Figure 1 This is a system overview diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0016] To make the technical means, creative features, objectives and effects of this invention clearer and easier to understand, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0017] Example like Figure 1 As shown, the regional building carbon emission optimization system provided by the present invention specifically includes a sensing layer, a transmission layer, a platform layer, and an application layer connected in sequence, wherein: The perception layer is used to collect multi-source data on buildings within the area, including building energy consumption data, environmental status data, regional energy resource data, and user behavior data; Building energy consumption data includes electricity consumption data, gas consumption data, and heat consumption data for each building in the area, which are collected through NB-IoT smart meters, gas meters, and heat meters; Environmental data includes building indoor environmental data and regional outdoor meteorological data. Indoor environmental data includes building indoor temperature and humidity data. Concentration data was collected via LoRa wireless sensors, while outdoor meteorological data, including regional outdoor light intensity, wind speed, and outdoor temperature data, was obtained from regional weather stations. Regional energy resource data includes photovoltaic power resource data of photovoltaic power plants in the region, charging and discharging power data of energy storage batteries, and waste heat resource data of waste heat recovery systems, which are collected through Modbus and OPC UA standardized interfaces. User behavior data includes user comfort preference data, which is collected through smart terminal devices within the building.

[0018] The transport layer is used to receive multi-source data collected by the perception layer and perform edge preprocessing on the multi-source data, including data cleaning and data aggregation. Data cleaning removes outliers from the multi-source data, and data aggregation aggregates minute-level data into 15-minute or hour-level data. The preprocessed multi-source data is transmitted to the platform layer through an adapted communication protocol, including 5G protocol, LoRa protocol and NB-IoT protocol.

[0019] The platform layer is used to store, process, and optimize the data transmitted by the transport layer, including: The data storage module is used to store the preprocessed data; The carbon emission accounting module calculates the carbon emissions of a single building and the total carbon emissions of the region based on building energy consumption data and carbon emission factors. The formula for calculating carbon emissions from a single building is: ; in, For the first Electricity consumption data for the building As a carbon emission factor for electricity, For the first Gas consumption data for the building Carbon emission factors from fuel gas For the first Heat consumption data of the building Thermal carbon emission factor; The formula for calculating total regional carbon emissions is: ; in This represents the number of buildings within the area.

[0020] The energy load forecasting module includes a data processing unit, a model training unit, and a load forecasting unit connected in sequence, wherein: The data processing unit is used to standardize historical building energy consumption data and outdoor meteorological data collected by the sensing layer and stored in the data storage module. The processing steps specifically include data alignment and normalization. The data alignment step will standardize the data within the region. Historical building energy consumption data and corresponding outdoor meteorological data for each building are synchronized and aligned with timestamps at fixed intervals to form a dataset linking energy consumption and the environment. The normalization process uses Min-Max normalization to map the aligned dataset to... Intervals, eliminating dimensional differences, are expressed by the following formula: ; in, The raw data includes building energy consumption data and outdoor meteorological data. , These are the minimum and maximum values ​​of the data sequence, respectively. The data is after normalization; The model training unit uses a long short-term memory neural network to train the energy load prediction model. The input is a historical feature vector, and the output is the predicted future energy load value. For the th For each building, the historical feature vector at time step t is: ; in, For the first Building The building energy consumption data vector after the data processing unit has normalized it for the specified time period. They are respectively The outdoor temperature, light intensity, and wind speed data for the specified time period are normalized by the data processing unit. Training samples are constructed using a sliding window method with a 24-hour window length. Each sample includes a historical feature vector sequence from the past 24 hours. The horizontal prediction is for the next 6 hours, with one prediction point per hour. The loss function is as follows: ; in, The number of training samples. The first Building Forecast values ​​of electricity, gas, and heat energy consumption during the specified time period. For the first Building The actual values ​​of electricity, gas, and heat consumption data for each time period; The load forecasting unit uses a trained energy load forecasting model to predict the energy load of buildings in the area for future periods. ; in, , For the first Building The energy load forecast vector for each time period is generated; finally, the forecast values ​​in the energy load forecast vector are converted into actual building energy consumption forecast data through inverse normalization.

[0021] The multi-objective collaborative optimization module is used to generate regional energy dispatch strategies. Its workflow includes: (1) Receive the necessary input data for performing multi-objective optimization, including the energy load forecast value from the energy load forecasting module, as well as user comfort preference data and regional energy resource data collected by the perception layer; (2) Individual coding is performed on the regional energy dispatch strategy, and the population size is set. Several individuals are generated by uniformly distributed random initialization. Each individual represents a dispatch strategy. The coding structure is as follows: ; in, This is the waste heat transfer ratio matrix. [Indicates factory] Towards Architecture The proportion of transferred waste heat to the total waste heat of the factory meets the requirements. , , , The number of factories in the region. The number of buildings in the area; A photovoltaic power distribution ratio matrix. [Indicates photovoltaic power station] Towards Architecture The proportion of allocated photovoltaic power to the total photovoltaic power generation of the photovoltaic power station meets the requirements. , , The number of photovoltaic power stations in the region; Power matrix for charging energy storage batteries, Represents architecture For energy storage batteries The charging power, This is the energy storage battery discharge power matrix. Indicates energy storage battery Towards Architecture The discharge power; (3) To minimize the total carbon emissions, the total energy cost, and the average user comfort in the future region, a multi-objective optimization objective function is constructed: ; ; ; ; in, For architecture Carbon emissions from electricity are calculated using the following formula: ; For photovoltaic power station Total photovoltaic power resources Carbon emission factor for electricity; For architecture Carbon emissions from natural gas are calculated using the following formula: ; For the factory Total waste heat resources Carbon emission factors from fuel gas; For architecture The formula for calculating thermal carbon emissions is: ; Thermal carbon emission factor; , , Buildings Electricity costs, gas costs, and heating costs; electricity costs The calculation formula is: ; Real-time electricity price for the power grid; For architecture User comfort rating The calculation formula is: ; in, , , As a weighting factor for comfort index, , , Indoor environmental data including temperature, humidity, and carbon dioxide concentration. , , These are the allowable boundary values ​​for the indoor environment. , , Comfort preference data set for users; (4) After decoding the scheduling strategy for each individual, calculate the corresponding objective function value; (5) Individual fitness is assessed using non-dominated ordination and crowding distance. Based on the objective function value, individuals in the population are divided into different non-dominated layers, and the crowding distance of each individual in its layer is calculated: ; in, Corresponding to the objective function , For individuals The One target value, For individuals The first in the layer The maximum and minimum values ​​of the target values; (6) Randomly select two individuals from the population, and select the individual with better non-dominant layer or greater crowding distance to enter the next generation. Repeat this step until all individuals in the population have been selected. The selected individuals are subjected to single-point cross-intersection in pairs. The specific logic of single-point cross-intersection is as follows: randomly select a cross-intersection point between the individual codes and swap the partial codes after the cross-intersection point of the two individuals. Gaussian mutation is performed on the crossover individuals with a mutation probability of 0.1. The mutation logic is as follows: randomly select an encoding parameter and add Gaussian noise to the parameter. (7) When the number of iterations reaches the maximum value, output the optimal solution in the first non-dominated layer, and select the optimal individual from them according to the user priority to decode the regional energy scheduling strategy.

[0022] The regional energy hub module is used to execute regional energy dispatch strategies and dispatch the transmission and distribution of energy between buildings within the region; The application layer is used to display the results of the platform layer, including a visualization interface, to present regional carbon emission data, predicted energy load for future periods, energy cost-effectiveness, and user comfort status.

[0023] like Figure 2 As shown, the present invention provides a method for optimizing regional building carbon emissions based on the above system, comprising the following steps: S1. Collect multi-source data of buildings within the area, including: Building energy consumption data: Electricity consumption data, gas consumption data, and heat consumption data for each building in the area; Environmental status data: indoor environmental data of the building and outdoor meteorological data of the area. Indoor environmental data includes indoor temperature data, humidity data, etc. Concentration data, outdoor meteorological data including regional outdoor light intensity data, wind speed data and outdoor temperature data; Regional energy resource data: photovoltaic power resources data of photovoltaic power plants in the region, charging and discharging power data of energy storage batteries, and waste heat resources data of waste heat recovery systems; User behavior data: User comfort preference data; S2. Perform edge preprocessing on multi-source data to remove outliers and aggregate minute-level data into 15-minute or hour-level data to reduce data transmission volume. S3. Store preprocessed multi-source data; S4. Calculate the carbon emissions of a single building and the total carbon emissions of the region based on building energy consumption data and carbon emission factors; S5. Based on historical building energy consumption data and outdoor meteorological data, predict future energy load using a long short-term memory neural network (LSTM). S6. Based on the energy load forecast results and combined with user comfort constraints, balance carbon emissions, energy costs and comfort objectives, and generate a regional energy dispatch strategy through a non-dominated sorting genetic algorithm. S7. Implement regional energy dispatching strategies to dispatch the transmission and distribution of energy between buildings within the region; S8 displays regional carbon emission data, future energy load forecasts, energy cost-effectiveness, and user comfort status through a visual interface.

[0024] Therefore, the regional building carbon emission optimization system and method provided by this invention integrates regional energy resources such as photovoltaics, energy storage, and waste heat, performs multi-source data preprocessing and dynamic scheduling, and multi-objective collaborative optimization. This solves the problems of lack of regional collaboration, data dispersion preventing real-time optimization, and insufficient practicality of single-objective solutions caused by traditional single-building optimization. It achieves efficient utilization of regional energy resources, real-time dynamic carbon emission management, and a balance between carbon emissions, costs, and user comfort. This improves the energy utilization efficiency of regional building clusters and the practicality of optimization solutions, providing an efficient solution for carbon emission control in smart city communities.

[0025] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A regional building carbon emission optimization system, applied to smart city communities, characterized in that, It includes the perception layer, transport layer, platform layer, and application layer connected in sequence, wherein: The perception layer is used to collect multi-source data of buildings in the area, including building energy consumption data, environmental status data, regional energy resource data, and user behavior data. The transport layer is used to receive multi-source data collected by the perception layer, perform edge preprocessing on the multi-source data, and transmit the preprocessed data to the platform layer through communication protocol adaptation. The platform layer is used to store, process, and optimize the data transmitted by the transport layer, including: The data storage module is used to store the preprocessed data; The carbon emission accounting module calculates the carbon emissions of a single building and the total carbon emissions of the region based on building energy consumption data and carbon emission factors. The energy load forecasting module predicts the energy load of buildings in the area for future periods based on building energy consumption data and environmental status data. The multi-objective collaborative optimization module, based on energy load forecasting results and combined with user comfort constraints, balances carbon emissions, energy costs, and comfort objectives to generate a regional energy dispatch strategy. The regional energy hub module is used to execute regional energy dispatch strategies and dispatch the transmission and distribution of energy between buildings within the region; The application layer is used to display the results of the platform layer, including a visualization interface, to present regional carbon emission data, predicted energy load for future periods, energy cost-effectiveness, and user comfort status.

2. The regional building carbon emission optimization system according to claim 1, characterized in that: In the sensing layer, building energy consumption data includes electricity consumption data, gas consumption data, and heat consumption data for each building in the area, which are collected through NB-IoT smart meters, gas meters, and heat meters. Environmental data includes building indoor environmental data and regional outdoor meteorological data. Indoor environmental data includes building indoor temperature and humidity data. Concentration data was collected via LoRa wireless sensors, while outdoor meteorological data, including regional outdoor light intensity, wind speed, and outdoor temperature data, was obtained from regional weather stations. Regional energy resource data includes photovoltaic power resource data of photovoltaic power plants in the region, charging and discharging power data of energy storage batteries, and waste heat resource data of waste heat recovery systems, which are collected through Modbus and OPC UA standardized interfaces. User behavior data includes user comfort preference data, which is collected through smart terminal devices within the building.

3. The regional building carbon emission optimization system according to claim 1, characterized in that, The transmission layer preprocesses the multi-source data collected by the perception layer, including data cleaning and data aggregation. Data cleaning removes outliers from the multi-source data, and data aggregation aggregates minute-level data from the multi-source data into 15-minute or hour-level data. The preprocessed multi-source data is transmitted to the platform layer through adapted communication protocols, including 5G, LoRa, and NB-IoT protocols.

4. The regional building carbon emission optimization system according to claim 2, characterized in that, The carbon emission accounting module of the platform layer is used to calculate the carbon emissions of a single building and the total carbon emissions of the region. The formula for calculating the carbon emissions of a single building is as follows: ； in, For the first Electricity consumption data for the building As a carbon emission factor for electricity, For the first Gas consumption data for the building Carbon emission factors from fuel gas For the first Heat consumption data of the building Thermal carbon emission factor; The formula for calculating total regional carbon emissions is: ； in This represents the number of buildings within the area.

5. A regional building carbon emission optimization system according to claim 2, characterized in that, The energy load forecasting module of the platform layer includes a data processing unit, a model training unit, and a load forecasting unit connected in sequence, wherein: The data processing unit is used to standardize historical building energy consumption data and outdoor meteorological data collected by the sensing layer and stored in the data storage module. The processing steps specifically include data alignment and normalization. The data alignment step will standardize the data within the region. Historical building energy consumption data and corresponding outdoor meteorological data for each building are synchronized and aligned with timestamps at fixed intervals to form a dataset linking energy consumption and the environment. The normalization process uses Min-Max normalization to map the aligned dataset to... Intervals, eliminating dimensional differences, are expressed by the following formula: ； in, The raw data includes building energy consumption data and outdoor meteorological data. , These are the minimum and maximum values ​​of the data sequence, respectively. The data is after normalization; The model training unit uses a long short-term memory neural network to train the energy load prediction model. The input is a historical feature vector, and the output is the predicted future energy load value. For the th For each building, the historical feature vector at time step t is: ； in, For the first Building The building energy consumption data vector after the data processing unit has normalized it for the specified time period. They are respectively The outdoor temperature, light intensity, and wind speed data for the specified time period are normalized by the data processing unit. Training samples are constructed using a sliding window method with a 24-hour window length. Each sample includes a historical feature vector sequence from the past 24 hours. The horizontal prediction is for the next 6 hours, with one prediction point per hour. The loss function is as follows: ； in, The number of training samples. The first Building Forecast values ​​of electricity, gas, and heat energy consumption during the specified time period. For the first Building The actual values ​​of electricity, gas, and heat consumption data for each time period; The load forecasting unit uses a trained energy load forecasting model to predict the energy load of buildings in the area for future periods. ； in, , For the first Building The energy load forecast vector for each time period is generated; finally, the forecast values ​​in the energy load forecast vector are converted into actual building energy consumption forecast data through inverse normalization.

6. A regional building carbon emission optimization system according to claim 5, characterized in that, The multi-objective collaborative optimization module of the platform layer is used to generate regional energy dispatch strategies, and its workflow includes: (1) Receive input data for performing multi-objective optimization. The input data includes the energy load forecast value from the energy load forecasting module, as well as user comfort preference data and regional energy resource data collected by the perception layer. (2) Individual coding is performed on the regional energy dispatch strategy, and the population size is set. Several individuals are generated by uniformly distributed random initialization. Each individual represents a dispatch strategy. The coding structure is as follows: ； in, This is the waste heat transfer ratio matrix. [Indicates factory] Towards Architecture The proportion of transferred waste heat to the total waste heat of the factory meets the requirements. , , , The number of factories in the region. The number of buildings in the area; A photovoltaic power distribution ratio matrix. [Indicates photovoltaic power station] Towards Architecture The proportion of allocated photovoltaic power to the total photovoltaic power generation of the photovoltaic power station meets the requirements. , , The number of photovoltaic power stations in the region; Power matrix for charging energy storage batteries, Represents architecture For energy storage batteries The charging power, This is the energy storage battery discharge power matrix. Indicates energy storage battery Towards Architecture The discharge power; (3) To minimize the total carbon emissions, the total energy cost, and the average user comfort in the future region, a multi-objective optimization objective function is constructed: ； ； ； ； in, For architecture Carbon emissions from electricity are calculated using the following formula: ； For photovoltaic power station Total photovoltaic power resources Carbon emission factor for electricity; For architecture Carbon emissions from natural gas are calculated using the following formula: ； For the factory Total waste heat resources Carbon emission factors from fuel gas; For architecture The formula for calculating thermal carbon emissions is: ； Thermal carbon emission factor; , , Buildings Electricity costs, gas costs, and heating costs; electricity costs The calculation formula is: ； Real-time electricity price for the power grid; For architecture User comfort rating The calculation formula is: ； in, , , As a weighting factor for comfort index, , , Indoor environmental data including temperature, humidity, and carbon dioxide concentration. , , These are the allowable boundary values ​​for the indoor environment. , , Comfort preference data set for users; (4) After decoding the scheduling strategy for each individual, calculate the corresponding objective function value; (5) Individual fitness is assessed using non-dominated ordination and crowding distance. Based on the objective function value, individuals in the population are divided into different non-dominated layers, and the crowding distance of each individual in its layer is calculated: ； in, Corresponding to the objective function , For individuals The One target value, For individuals The first in the layer The maximum and minimum values ​​of the target values; (6) Randomly select two individuals from the population, and select the individual with better non-dominant layer or greater crowding distance to enter the next generation. Repeat this step until all individuals in the population have been selected. The selected individuals are subjected to single-point cross-intersection in pairs. The specific logic of single-point cross-intersection is as follows: randomly select a cross-intersection point between the individual codes and swap the partial codes after the cross-intersection point of the two individuals. Gaussian mutation is performed on the crossover individuals with a mutation probability of 0.

1. The mutation logic is as follows: randomly select an encoding parameter and add Gaussian noise to the parameter. (7) When the number of iterations reaches the maximum value, output the optimal solution in the first non-dominated layer, and select the optimal individual from it according to the user priority to decode the regional energy scheduling strategy.

7. A method for optimizing regional building carbon emissions based on a regional building carbon emission optimization system according to any one of claims 1-6, characterized in that, Includes the following steps: S1. Collect multi-source data of buildings within the area, including: Building energy consumption data: Electricity consumption data, gas consumption data, and heat consumption data for each building in the area; Environmental status data: indoor environmental data of the building and outdoor meteorological data of the area. Indoor environmental data includes indoor temperature data, humidity data, etc. Concentration data, outdoor meteorological data including regional outdoor light intensity data, wind speed data and outdoor temperature data; Regional energy resource data: photovoltaic power resources data of photovoltaic power plants in the region, charging and discharging power data of energy storage batteries, and waste heat resources data of waste heat recovery systems; User behavior data: User comfort preference data; S2. Perform edge preprocessing on multi-source data to remove outliers and aggregate minute-level data into 15-minute or hour-level data to reduce data transmission volume. S3. Store preprocessed multi-source data; S4. Calculate the carbon emissions of a single building and the total carbon emissions of the region based on building energy consumption data and carbon emission factors; S5. Based on historical building energy consumption data and outdoor meteorological data, predict future energy load using a long short-term memory neural network (LSTM). S6. Based on the energy load forecast results and combined with user comfort constraints, balance carbon emissions, energy costs and comfort objectives, and generate a regional energy dispatch strategy through a non-dominated sorting genetic algorithm. S7. Implement regional energy dispatching strategies to dispatch the transmission and distribution of energy between buildings within the region; S8 displays regional carbon emission data, future energy load forecasts, energy cost-effectiveness, and user comfort status through a visual interface.

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