Comprehensive energy management system

Abstract

The invention provides a comprehensive energy management system which can be widely applied to the technical field of energy coordinated optimization management. The system specifically comprises a data acquisition module, a big data analysis module, an AI algorithm module, an intelligent control module and a network connection module. The data acquisition module acquires operation data of the full-energy system in real time; the big data analysis module performs deep analysis in combination with geographical environment information, accurate meteorological data, building load characteristic analysis and multi-source information of user behavior habits; the AI algorithm module is used for generating an optimal operation scheme by fusing a building environment and a heat energy professional algorithm based on an analysis result; and the intelligent control module realizes accurate execution and dynamic adjustment of the scheme through the network connection module.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy coordinated optimization management, and in particular relates to an integrated energy management system. Background Art

[0002] With the increasing energy consumption and the enhancement of environmental protection awareness, the optimization and upgrading of energy management systems have become particularly important. Traditional energy management systems are often limited to the management of a single energy type and lack cross-energy type collaborative optimization and intelligent control. Therefore, the development of an integrated energy management system that can comprehensively consider multiple energy types and achieve cross-domain collaborative optimization is of great significance for improving energy utilization efficiency, reducing energy consumption costs, and protecting the environment. Summary of the invention

[0003] The purpose of the present invention is to solve the deficiencies of the above-mentioned technologies and provide an integrated energy management system.

[0004] To this end, the present invention provides a comprehensive energy management system, including a data acquisition module, a big data analysis module, an AI algorithm module, an intelligent control module and a network connection module; the data acquisition module collects the operating data of the entire energy system in real time; the big data analysis module combines geographical environment information, accurate meteorological data, building load characteristics analysis, and multi-source information of user behavior habits for in-depth analysis; the AI ​​algorithm module, based on the analysis results, integrates the building environment and thermal energy professional algorithms to generate an optimal operation plan; the intelligent control module realizes the precise execution and dynamic adjustment of the plan through the network connection module.

[0005] The present invention provides a comprehensive energy management system, which has the following beneficial effects:

[0006] Improve energy efficiency: Through in-depth analysis and AI algorithm optimization, the coordinated management and optimization of the entire energy system are achieved, improving energy efficiency;

[0007] Reduce energy consumption costs: Through precise execution and dynamic adjustment of operation plans, energy consumption costs are reduced and economic benefits are improved;

[0008] Enhance system stability: Enhance system stability and reliability through real-time monitoring and dynamic adjustment;

[0009] Improve user experience: Through the user interaction interface module and customized setting functions, the user experience and system personalized service capabilities are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0011] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments to help understand the content of the present invention. The methods used in the present invention are all conventional methods unless otherwise specified; the raw materials and devices used are all conventional commercial products unless otherwise specified.

[0012] like Figure 1 As shown, the present invention provides a comprehensive energy management system, including a data acquisition module, a big data analysis module, an advanced AI algorithm module, an intelligent control module and an efficient network connection module, forming a closed-loop intelligent management system. The data acquisition module is like the eyes and ears of the system, capturing all kinds of operating data of the full energy system in real time and comprehensively. The full energy system covers heating, cooling, hot water supply and electricity consumption, including but not limited to the current status of the equipment, detailed energy consumption data and key environmental parameters, laying a solid foundation for subsequent in-depth analysis.

[0013] The big data analysis module integrates geographic environment information, including geographical location, altitude, topography, soil type and vegetation cover, and deeply integrates accurate real-time and historical meteorological data, including temperature, humidity, air pressure, wind speed, wind direction, rainfall, etc., combined with the unique load characteristics of the building such as detailed analysis based on structure, materials, thermal insulation performance, population density and equipment configuration and user behavior habits such as lighting usage habits, temperature adjustment frequency and water use patterns, through data mining, machine learning, statistical analysis and other advanced technical means, in-depth exploration of the inherent connections and potential laws between data, providing a scientific basis for the optimization strategy and control recommendations of the energy system.

[0014] On this basis, the AI ​​algorithm module receives the in-depth analysis results of the big data analysis module and integrates with professional algorithms in the field of building environment and thermal energy, such as building heat transfer model, air conditioning system energy efficiency optimization algorithm, heating system heat load accurate calculation model and hot water system thermal efficiency improvement strategy. By setting multiple optimization goals such as minimizing energy consumption, maximizing energy efficiency, improving user comfort and enhancing system stability, the multi-objective optimization technology is used to solve the optimal operation plan that not only meets actual needs but also takes into account high efficiency and energy saving. After simulation evaluation and necessary adjustment and optimization, these plans ensure the maximum benefit of energy utilization and the dual improvement of user satisfaction.

[0015] The intelligent control module plays the role of the executor. It receives the optimal operation plan carefully formulated by the AI ​​algorithm module through the network connection module, and commands various equipment in the energy system such as air conditioners, heating systems, water heaters and power distribution devices to perform precise operations through precise control signals, including the start and stop of equipment, dynamic adjustment of power and flexible switching of working modes, to ensure that each link can be strictly executed according to the predetermined plan. At the same time, the intelligent control module also has a real-time monitoring function. Once it detects that the system operation status deviates from the preset range, it immediately starts the dynamic adjustment mechanism and requests the AI ​​algorithm module to recalculate or optimize the plan to achieve continuous optimization and precise control of the system.

[0016] In addition, the system also incorporates a user interaction interface module, which is designed to be intuitive and friendly, and provides rich information display and operation functions, allowing users to easily view the system operating status, historical energy consumption data, optimization plans and their execution effects, and supports users to make personalized settings according to their own needs, such as preference adjustment and energy-saving target setting, greatly enhancing the user experience and the level of personalization of system services.

[0017] The big data analysis module also adds a data cleaning and verification mechanism to ensure the accuracy of the data used for analysis. At the same time, through data visualization, complex analysis results are presented in the form of charts and curves, providing intuitive and powerful support for management decisions. The AI ​​algorithm module uses cutting-edge machine learning algorithms, which have the ability to continuously learn and self-optimize. It can automatically adjust algorithm parameters according to the actual situation of system operation, and continuously improve the accuracy and practicality of the optimization plan.

[0018] The intelligent control module adopts an advanced distributed control architecture, distributing the control logic to each subsystem, achieving a significant improvement in response speed and control flexibility. At the same time, the module also supports dual modes of remote and local control, ensuring that the system can still maintain basic operation and control capabilities in special circumstances such as communication network failures, ensuring the stability and security of energy supply.

[0019] Finally, the system also introduced an energy audit and evaluation module to conduct regular comprehensive audits and evaluations of the system's energy consumption, optimization effects, and economic benefits, providing solid data support for the system's continuous optimization and upgrades. In addition, the system also supports seamless docking and data sharing with other energy management systems or platforms, broadening the scope of energy management and optimization services, and making important contributions to building a greener, more efficient, and smarter energy ecosystem.

[0020] However, what is described above is only a specific embodiment of the present invention and should not be used to limit the scope of implementation of the present invention. Therefore, the replacement of equivalent components, or equivalent changes and modifications made according to the scope of protection of the present invention should still fall within the scope covered by the claims of the present invention.

Claims

1. An integrated energy management system, characterized in that: It includes a data acquisition module, a big data analysis module, an AI algorithm module, an intelligent control module and a network connection module; the data acquisition module collects the operating data of the entire energy system in real time; the big data analysis module conducts in-depth analysis based on multi-source information such as geographic environment information, accurate meteorological data, building load characteristics analysis, and user behavior habits; the AI ​​algorithm module generates an optimal operating plan based on the analysis results, integrating the building environment and thermal energy professional algorithms; the intelligent control module realizes accurate execution and dynamic adjustment of the plan through the network connection module.

2. The integrated energy management system according to claim 1, characterized in that: The full energy system includes data on heating, cooling, hot water, and electricity consumption, and the operating data includes equipment status, energy consumption data, and environmental parameters.

3. The integrated energy management system according to claim 1, characterized in that: The in-depth analysis process of the big data analysis module includes the following steps: Integration of geographical environment information: Collect and analyze geographical location, altitude, topography, soil type, and vegetation coverage information to assess its impact on the external conditions of the energy system; Meteorological data processing: collect meteorological data such as temperature, humidity, air pressure, wind speed, wind direction, rainfall, etc. in real time or historically, and use these data to predict energy demand and optimize energy supply strategies; Building load characteristic modeling: Based on the building's structural type, material, thermal insulation performance, population density, equipment configuration information, combined with building energy consumption data, indoor temperature, humidity, and light intensity parameters, a building energy consumption model is constructed to identify energy consumption patterns; User behavior analysis: Collect and analyze user usage data, including light on / off times, temperature adjustment frequency, and water usage habits, build user behavior models, and predict future energy demand trends; In-depth analysis and strategy generation: Use analytical techniques such as data mining, machine learning, and statistical analysis to reveal the inherent connections and patterns between data, form an in-depth understanding of the operating status, energy efficiency level, and future trends of the entire energy system, and generate optimization strategies and control recommendations based on this.

4. The integrated energy management system according to claim 3, characterized in that: The process of generating the optimal operation plan of the AI ​​algorithm module includes the following steps: Receiving analysis results: receiving the in-depth analysis results of geographical environment information, accurate meteorological data, building load characteristics analysis and multi-source information of user behavior habits output by the big data analysis module; Professional algorithm integration: The received analysis results are combined with professional algorithms in the field of building environment and thermal energy, including building heat transfer models, air conditioning system energy efficiency optimization algorithms, heating system heat load calculation models, and hot water system thermal efficiency improvement algorithms, so as to comprehensively consider the physical characteristics and operation requirements of the energy system; Multi-objective optimization: Based on the fused algorithm, multiple optimization goals are set, including minimizing energy consumption, maximizing energy efficiency, improving user comfort, and enhancing system stability. Multi-objective optimization technology is used to find the optimal solution or approximate optimal solution. Operation plan generation: Based on the optimization results, the optimal operation plan for the entire energy system is generated, including equipment scheduling strategy, energy allocation plan, and system parameter settings, to ensure efficient energy utilization and stable system operation while meeting user needs; Program evaluation and adjustment: Conduct simulation evaluation on the generated operation program to predict its energy efficiency performance and user satisfaction in actual operation, and make necessary adjustments and optimizations to the program based on the evaluation results.

5. The integrated energy management system according to claim 1, characterized in that: The precise execution and dynamic adjustment process includes the following steps: Solution reception: Receive the optimal operation solution generated by the AI ​​algorithm module through the network connection module. The solution includes equipment scheduling strategy, energy allocation plan, and system parameter settings. Precise execution: Based on the optimal operation plan received, the intelligent control module sends instructions to various devices in the energy system through control signals, accurately controls the start and stop, power adjustment, and mode switching of the equipment to ensure that the plan is accurately executed; Real-time monitoring: Obtain the operating status data of the energy system in real time through the network connection module, including but not limited to the working status of equipment, energy consumption data, and environmental parameters, and conduct comprehensive monitoring of the system; Dynamic adjustment: Based on real-time monitoring data and preset thresholds or conditions, the intelligent control module automatically determines whether the current operation plan needs to be adjusted. If necessary, it sends a request to the AI ​​algorithm module through the network connection module to request recalculation or optimization of the operation plan; Plan update and execution: Receive the updated operation plan returned by the AI ​​algorithm module, and send the control signal to the energy system again through the network connection module to achieve dynamic adjustment and precise execution of the plan.

6. The integrated energy management system according to claim 1, characterized in that: The system also includes a user interaction interface module, which provides an intuitive operation interface and rich information display functions, allowing users to easily view the system operation status, historical energy consumption data, optimization plans and execution effects, while supporting user customized settings, such as preference settings and energy-saving target settings, to enhance user experience and system personalized service capabilities.

7. The integrated energy management system according to claim 1, characterized in that: The big data analysis module also has data cleaning and verification functions, which can automatically identify and eliminate abnormal or erroneous data to ensure the accuracy and reliability of the analysis results. The module also supports data visualization, and intuitively displays data analysis results in the form of charts and curves to provide decision support for managers.

8. The integrated energy management system according to claim 1, characterized in that: The AI ​​algorithm module adopts advanced machine learning algorithms to continuously learn and optimize its own decision-making capabilities. The module also has an adaptive learning function that can automatically adjust algorithm parameters based on actual feedback from system operation, further improving the accuracy and practicality of the optimization solution.

9. The integrated energy management system according to claim 1, characterized in that: The intelligent control module adopts a distributed control architecture to disperse the control logic into various subsystems to achieve faster and more flexible control response. The module also supports a combination of remote control and local control to ensure that the basic operation and control capabilities of the system can be maintained in the event of a communication network failure.

10. The integrated energy management system according to claim 1, characterized in that: The system also includes an energy audit and assessment module, which regularly audits and assesses the system's energy consumption, optimization effects and economic benefits, provides data support for the continuous optimization and upgrading of the system, and supports docking and data sharing with other energy management systems or platforms to achieve a wider range of energy management and optimization services.

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