Carbon emission management system for building energy carbon supervision platform

Abstract

The invention relates to a carbon emission management system for a building energy carbon supervision platform, and belongs to the technical field of carbon resource management. The system comprises a data acquisition module, a data processing module, a carbon emission monitoring module, an emission reduction measure making module, a target setting and tracking module and a data display and interaction module. The data acquisition module supports a plurality of communication protocols and standard data models, is used for acquiring energy consumption data, activity level data and unstructured data sources in a building, and has a preliminary data processing capability. According to the invention, the computational statistics, analysis and evaluation and planning and expectation processes of building carbon emission can be communicated, a complete building carbon emission management system is formed, the problems in the prior art are effectively solved, and a comprehensive and accurate carbon emission management service is provided for building energy carbon supervision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon resource management, in particular to a carbon emission management system for a building energy carbon supervision platform. BACKGROUND

[0002] The building energy consumption supervision platform is an important tool for realizing real-time monitoring and management of building energy consumption. In recent years, it has been widely promoted with the support of policies, but it has exposed many problems in the process of building energy carbon supervision transformation. The existing platform's carbon emission management function technology is not mature, mainly focusing on energy management, and there are obvious shortcomings in carbon emission management. Carbon emission calculation usually uses fixed emission factors, which does not fully consider the dynamic differences of regional and temporal nature, resulting in large calculation result errors. At the same time, the carbon emission calculation method lacks modular design, and the influencing factors are not fully considered independently, which affects the flexibility and precision of the calculation. In addition, the carbon emission management module has single function, poor compatibility, lacks planning for the dual-carbon process, and the analysis results are difficult to effectively apply to actual decision-making and optimization process.

[0003] For example, CN119398608A proposes a carbon emission management system for a building energy carbon supervision platform, which includes a carbon emission statistics module, a carbon emission calculation and evaluation module, and a carbon planning module. However, this system still has some shortcomings. For example, although its carbon emission factor can be dynamically adjusted, its adaptability in different energy forms and complex building scenarios still needs to be improved; the modular design improves the calculation flexibility in actual application, but the efficiency and accuracy of data interaction and fusion in the multi-module collaborative processing of complex carbon emission scenarios still need to be optimized; the system's planning for the dual-carbon process is involved, but its ability to accurately predict and dynamically regulate building life cycle carbon emissions in actual operation is still insufficient. SUMMARY

[0004] (I) Technical problems solved In view of the deficiencies of the prior art, the present application provides a carbon emission management system for a building energy carbon supervision platform, which solves the problems raised in the background art.

[0005] (II) Technical solutions To achieve the above object, the present application is implemented by the following technical solutions: A carbon emission management system for a building energy carbon supervision platform, comprising a data acquisition module, a data processing module, a carbon emission monitoring module, an emission reduction measure development module, a target setting and tracking module, and a data display and interaction module. The data acquisition module is used to acquire energy consumption data in the building, activity level data of the building, and unstructured data sources, supports multiple communication protocols and standard data models, to ensure seamless access to different brands, different ages of metering equipment and building automation systems, and has preliminary data processing capabilities at the metering equipment or local gateway level; the data processing module is used to integrate, analyze and process the collected data, supports dynamic emission factors, AI / ML driven analysis, advanced anomaly detection and diagnosis, pattern recognition and benchmark comparison, and energy consumption or emission prediction functions, and also supports multiple mainstream building carbon accounting standards and guidelines; the carbon emission monitoring module is used to monitor the carbon emission of the building in real time, and visualize the carbon emission data, which can realize real-time or near-real-time visualization of the "carbon flow" in the building, and increase dynamic comparative analysis with predicted values, benchmark values, average or excellent values of the same type of building, and root cause analysis integration; the emission reduction measure development module is used to develop corresponding emission reduction measures according to the carbon emission monitoring results, has a cost-benefit analysis engine, a measure library and knowledge graph, simulation and optimization, and tracking measure implementation and verification functions; the target setting and tracking module is used to set the carbon emission reduction target of the building, and track and evaluate the implementation of the target, provides tools or guidance based on scientific carbon targets (SBTi), allows users to set different emission reduction scenarios, simulates the path to achieve long-term goals, and supports dynamic review and adjustment suggestions for medium and long-term targets; the data display and interaction module is used to display carbon emission data, emission reduction measure suggestions, target tracking results and other information to the user in the form of intuitive charts, graphs, etc., and provides user interaction functions.

[0006] Preferably, the data acquisition module supports multiple communication protocols such as Modbus, BACnet, MQTT, OPC UA, and standard data models such as Haystack, Brick Schema, the unstructured data sources include operation and maintenance work orders, equipment maintenance records, weather data API access, etc., and has data aggregation, simple anomaly detection and other preliminary data processing functions at the metering equipment or local gateway level. In this way, the collected data can be ensured to be extensive and accurate, providing a reliable basis for subsequent data processing and analysis.

[0007] Preferably, the dynamic emission factors in the data processing module can predict the changes in carbon emission factors of the power grid in the future period of time according to the power grid dispatching information and weather forecasts, and the AI / ML driven analysis includes advanced anomaly detection and diagnosis, pattern recognition and benchmark comparison, and energy consumption or emission prediction functions. In this way, not only can the carbon emissions be calculated in real time, but also the future carbon emission trend can be predicted to provide forward-looking decision support for building energy management.

[0008] Preferably, the cost-benefit analysis engine in the emission reduction measure development module can perform preliminary cost estimation and carbon emission reduction or cost saving amount prediction for the proposed emission reduction measures, calculate simple payback period, net present value or carbon emission reduction cost. The measure library and knowledge graph contain information such as measure description, applicable conditions, expected effect range, typical cost, implementation case, related standard specification link, and use knowledge graph technology to associate measures with building characteristics, energy consumption patterns, diagnosis results. This helps users quickly find suitable emission reduction measures for their own situation and assess their economic and effectiveness.

[0009] Preferably, the system further includes an open API and integration module, providing powerful, secure and well-documented APIs to support two-way data exchange and functional integration with ERP systems, property management platforms, smart city platforms, carbon trading platforms, renewable energy certificate systems, etc. This enables the system to seamlessly integrate with other related systems, achieve data sharing and business collaboration, and improve overall management efficiency.

[0010] Preferably, the system further includes a security and compliance module to ensure compliance with relevant security and privacy regulations such as Cyberspace Protection, GDPR, etc., with data encryption, fine-grained access control, audit logs, security vulnerability management mechanisms, and consideration of blockchain technology for key carbon emission data evidence. This can effectively protect the security and privacy of data, prevent data leakage and tampering, and enhance user trust in the system.

[0011] Preferably, the system further includes a carbon asset management and reporting module to support automatic generation of carbon emission reports and emission reduction certification report templates that meet the requirements of domestic and foreign carbon markets or voluntary emission reduction standards. This provides convenience for users to participate in carbon trading and energy saving and emission reduction activities, and helps to achieve effective management and value maximization of carbon assets.

[0012] Preferably, the overall system adopts a cloud-native, containerized, and microservices architecture design to ensure high availability, elastic scalability, easy maintenance, and rapid iterative upgrades. It supports expansion from single buildings to building clusters, campuses, and even city levels. The functional modules are highly decoupled, supporting on-demand subscription or deployment, and also supporting public cloud, private cloud, hybrid cloud, and local deployment modes. This architecture design can meet the needs of different users, adapt to various scales and complex building environments, while ensuring the stability and scalability of the system.

[0013] (III) Beneficial Effects The present invention provides a carbon emission management system for a building energy and carbon supervision platform, having the following beneficial effects: The present invention adopts advanced multi-module collaborative processing technology. Through an intelligent scheduling and efficient communication mechanism between functional modules, real-time data sharing and seamless docking are achieved, improving the overall operating efficiency of the system. At the same time, a data fusion algorithm is introduced to integrate and analyze multi-source data, fully mining the data value, providing a more comprehensive and in-depth insight for building energy and carbon supervision, and helping to formulate more targeted and effective emission reduction measures. [[ID=⑧]]

[0014] The present invention strengthens the dual-carbon process planning function and constructs a complete carbon emission prediction and dynamic regulation system for the entire life cycle of buildings. Based on multi-dimensional information such as historical data, real-time monitoring data, and building operation plans, advanced prediction models are used to accurately predict the building carbon emission trend, and according to the emission reduction targets and actual progress in different stages, the emission reduction strategy is dynamically adjusted. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0017] As Figure 1 [[ID=⑧]]Shown, the embodiment of the present invention provides a carbon emission management system for a building energy and carbon supervision platform, including the following modules: The data acquisition module is used to collect energy consumption data (such as electricity, gas, and heat) within the building, building activity level data (such as building area, number of people, and equipment uptime), and unstructured data sources (such as maintenance work orders, equipment maintenance records, and weather data API access). This module supports multiple communication protocols (such as Modbus, BACnet, MQTT, and OPC UA) and standard data models (such as Haystack and Brick Schema) to ensure seamless integration with metering devices and building automation systems of different brands and ages. Furthermore, it possesses preliminary data processing capabilities at the metering device or local gateway level, such as data aggregation and simple anomaly detection, effectively reducing the computational burden on the cloud or central platform and improving the real-time performance of data processing.

[0018] The data processing module integrates, analyzes, and processes collected energy consumption and activity level data. It supports dynamic emission factors, enabling prediction of future changes in the grid's carbon emission factors based on grid dispatch information and weather forecasts. Simultaneously, it utilizes AI / ML-driven analysis technology to achieve advanced anomaly detection and diagnosis (such as accurately identifying complex anomaly patterns like equipment failure, control malfunction, and abnormal usage behavior), pattern recognition and benchmark comparison (automatically identifying building energy consumption / emission patterns and comparing them with similar buildings or historical best performance to identify discrepancies), and energy consumption / emission prediction (accurately predicting building energy consumption and carbon emissions in the short and medium term based on historical data, weather forecasts, activity plans, and other multi-dimensional information). Furthermore, it supports various mainstream building carbon accounting standards and guidelines (such as GHG Protocol Scope 1 & 2, ISO 14064, and local standards).

[0019] The carbon emission monitoring module is used to monitor the building's carbon emissions in real time and visualize the data. It enables real-time or near-real-time visualization of the building's "carbon flow," clearly showing the carbon emission contributions of different areas and systems (such as HVAC, lighting, and plug-in loads). In the monitoring summary layer, in addition to displaying basic information such as total carbon emissions and intensity, it also introduces dynamic comparative analysis functions with predicted values, benchmark values, and average or excellent values ​​for similar buildings, allowing users to more intuitively understand the actual performance of the building's carbon emissions. Simultaneously, it can directly link the anomaly diagnoses and pattern discrepancies identified by AI analysis in the data processing module to the monitoring summary report, providing users with more direct insights.

[0020] The emission reduction measure formulation module is used to develop corresponding emission reduction measures based on carbon emission monitoring results. It features a cost-benefit analysis engine that can accurately perform preliminary cost estimates (covering installation costs and changes in operation and maintenance costs) and predict carbon emission reductions or cost savings for suggested emission reduction measures. This allows for the calculation of key indicators such as simple payback period, net present value, or carbon emission reduction costs, greatly improving the operability and decision support of emission reduction measure recommendations. A structured emission reduction measure knowledge base has been built, containing detailed information such as measure descriptions, applicable conditions, expected effect range, typical costs, implementation cases, and links to relevant standards and specifications. Knowledge graph technology is used to deeply correlate emission reduction measures with building characteristics, energy consumption patterns, and diagnostic results, enabling intelligent recommendations of the most relevant and effective emission reduction measures. Furthermore, it integrates building energy consumption simulation software (such as EnergyPlus) or a lightweight simulation engine, allowing users to simulate proposed emission reduction measures (such as replacing equipment or adjusting control strategies) on the platform and predict their energy-saving and carbon-reduction effects in advance. It also has the function of tracking and verifying the implementation of measures, which can record the implementation status and actual cost of selected measures, and continuously track and verify their actual effects after implementation, forming a complete closed-loop management.

[0021] The target setting and tracking module is used to set carbon emission reduction targets for buildings and to track and evaluate the achievement of these targets. Combining the Scientific Carbon Targeting (SBTi) methodology, it provides enterprises with tools and guidance for setting building decarbonization targets that conform to the 1.5°C decarbonization pathway. Users can set different emission reduction scenarios (such as aggressive or conservative), and comprehensively assess the feasibility, cost, and risks of achieving long-term goals by simulating different combinations of measures. Furthermore, it can provide users with timely and reasonable target adjustment suggestions based on actual progress, changes in external factors (such as policy updates and energy price fluctuations), and the verification of the effects of new measures.

[0022] The data visualization and interaction module presents carbon emission data, emission reduction recommendations, and target tracking results to users in intuitive charts and graphs, and provides interactive features. Employing a responsive design, it optimizes the mobile app or H5 experience, ensuring managers can conveniently view key indicators and receive alerts anytime, anywhere. It offers highly customizable dashboards to meet the needs of different user roles in creating personalized views. Report generation is also highly customizable, including time range, indicators, dimensions, and formats. Innovatively, it utilizes natural language generation technology to transform key data changes, anomalies, and target achievement status into concise text summaries or "data stories," lowering the barrier to user comprehension. Furthermore, it integrates collaboration features, supporting data commenting and annotation, and integrating discussions and approval processes for emission reduction recommendations, efficiently translating insights into action. Example

[0023] This embodiment is basically the same as Embodiment 1, with the main difference being the optimization of the data acquisition module and the enhancement of the data processing module's functions.

[0024] The data acquisition module, building upon the first embodiment, further enhances support for emerging communication protocols and data models, such as the CoAP protocol and the JSON data model, to accommodate a wider range of device connectivity needs. Simultaneously, the data acquisition frequency can be more flexibly adjusted according to actual application scenarios. In addition to real-time acquisition, it can also perform scheduled acquisition (e.g., every 15 minutes or hour). Furthermore, the data acquisition scheduling algorithm has been optimized, improving the stability and efficiency of data acquisition and further balancing data accuracy and system load.

[0025] The data processing module features optimized and upgraded built-in AI / ML algorithms, incorporating deep learning technology to more accurately identify complex energy consumption patterns and potential energy-saving opportunities. For example, by learning from long-term building energy consumption data, the model can automatically detect abnormally high energy consumption in specific equipment during specific periods and issue early warnings. Furthermore, edge computing capabilities have been enhanced, enabling more complex preprocessing of data at the data acquisition end, such as data cleaning and feature extraction, significantly reducing cloud computing pressure and improving the overall system response speed and processing efficiency.

[0026] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A carbon emission management system for a building energy carbon monitoring platform, characterized in that, include: The data acquisition module is used to collect energy consumption data, building activity level data and unstructured data sources in the building. It supports multiple communication protocols and standard data models to connect to metering devices and building automation systems of different brands and ages, and has preliminary data processing capabilities at the metering device or local gateway level. The data processing module is used to integrate, analyze and process the collected data. It supports dynamic emission factors, AI / ML-driven analysis, advanced anomaly detection and diagnosis, pattern recognition and benchmark comparison, as well as energy consumption / emission prediction functions. It also supports a variety of mainstream building carbon accounting standards and guidelines. The carbon emission monitoring module is used to monitor the carbon emissions of buildings in real time and display them visually. It can realize real-time or near-real-time visualization of carbon flow within buildings, as well as dynamic comparative analysis and root cause analysis integration with predicted values, benchmark values, average / excellent values ​​of similar buildings. The emission reduction measure formulation module is used to formulate corresponding emission reduction measures based on carbon emission monitoring results. It has a cost-benefit analysis engine, a measure library and knowledge graph, simulation and optimization, and the ability to track and verify the implementation of measures. The target setting and tracking module is used to set carbon emission targets for buildings and track the achievement of these targets. It provides tools or guidance based on scientific carbon targets (SBTi), allows users to set different emission reduction scenarios, simulate the path to achieve long-term targets, and supports suggestions for dynamic review and adjustment of medium- and long-term targets. The data display and interaction module is used to present users with information such as carbon emission data, emission reduction measures recommendations, and target tracking results, and to provide user interaction functions.

2. A carbon emission management system for a building energy carbon monitoring platform according to claim 1, characterized in that: The data acquisition module supports multiple communication protocols such as Modbus, BACnet, MQTT, and OPC UA, as well as standard data models such as Haystack and Brick Schema. The unstructured data sources include operation and maintenance work orders, equipment maintenance records, weather data API access, etc., and have preliminary data processing functions such as data aggregation and simple anomaly detection.

3. A carbon emission management system for a building energy carbon monitoring platform according to claim 1, characterized in that: The dynamic emission factor in the data processing module can predict the change of the carbon emission factor of the power grid in the future period based on power grid dispatch information and weather forecasts. The AI / ML driven analysis includes advanced anomaly detection and diagnosis, pattern recognition and benchmark comparison, and energy consumption / emission prediction functions.

4. A carbon emission management system for a building energy carbon monitoring platform according to claim 1, characterized in that: The cost-benefit analysis engine in the emission reduction measure formulation module can estimate the cost of emission reduction measures and predict carbon emission reduction / cost savings, calculate the investment payback period, net present value or carbon emission reduction cost. The measure library and knowledge graph contain information such as measure description, applicable conditions, expected effect range, typical costs, implementation cases, and links to relevant standards and specifications, and associate measures with building characteristics, energy consumption patterns, and diagnostic results.

5. A carbon emission management system for a building energy carbon monitoring platform according to claim 1, characterized in that: The system also includes an open API and integration module, providing API support for two-way data exchange and functional integration with ERP systems, property management platforms, smart city platforms, carbon trading platforms, and renewable energy certificate systems.

6. A carbon emission management system for a building energy carbon monitoring platform according to claim 1, characterized in that: The system also includes an open API and integration module, providing API support for two-way data exchange and functional integration with ERP systems, property management platforms, smart city platforms, carbon trading platforms, and renewable energy certificate systems.

7. A carbon emission management system for a building energy carbon monitoring platform according to claim 1, characterized in that: The system also includes a carbon asset management and reporting module, which supports the generation of carbon emission reports and emission reduction verification report templates that meet the requirements of domestic and international carbon markets or voluntary emission reduction standards.

8. A carbon emission management system for a building energy carbon monitoring platform according to claim 1, characterized in that: The system adopts a cloud-native, containerized, and microservice architecture, supporting expansion from single buildings to building clusters, parks, and cities. The functional modules are highly decoupled, supporting on-demand subscription or deployment, and supporting public cloud, private cloud, hybrid cloud, and localized deployment modes.

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