Smart energy operation management system based on cloud computing
Through the intelligent energy operation management system based on cloud computing, integrating real-time data monitoring, trend analysis and energy optimization scheduling and other functions, the problem of lack of real-time data monitoring and intelligent optimization in traditional energy operation management is solved, and the improvement of energy operation efficiency and the achievement of green and low-carbon goals has been achieved.
Patent Information
- Application Number
- CN202411899031.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-23
- Publication Date
- 2025-05-16
AI Technical Summary
Traditional energy operation management lacks real-time data monitoring and intelligent optimization, resulting in increased energy waste and operational costs, inconcentration of information and lack of real-time and accuracy in decision-making.
It provides a smart energy operation management system based on cloud computing, integrating real-time data monitoring, trend analysis, energy optimization scheduling, equipment efficiency monitoring and other functions, and improves energy operation efficiency through modules such as data acquisition, storage, energy efficiency analysis and intelligent scheduling.
Through real-time data monitoring and intelligent optimization, energy waste is reduced, operating costs are reduced, and green production and low-carbon operation goals are achieved.
Smart Images
Figure CN120010305A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of energy operation management, and more specifically, to a smart energy operation management system based on cloud computing. Background Art
[0002] In traditional energy operation and management, many companies often rely on manual experience and outdated management models to monitor and dispatch energy use. However, due to the lack of real-time data monitoring and intelligent optimization in traditional energy operation methods, it is difficult for companies to promptly discover energy waste and unreasonable use. At the same time, due to the lack of centralized information and the lack of real-time and precision in decision-making, scheduling and equipment management are often inefficient and difficult to flexibly respond to changes, which in turn increases energy waste and operating costs. Summary of the invention
[0003] In response to the technical problems existing in the prior art, the present invention provides a smart energy operation and management system based on cloud computing, which integrates real-time data monitoring, trend analysis, energy optimization scheduling, equipment efficiency monitoring and other functions, aiming to improve the energy operation efficiency of enterprises and help enterprises achieve green production and low-carbon operation goals, so as to solve the problems raised in the above background technology.
[0004] The technical solution of the present invention to solve the above technical problems is as follows: specifically comprising: a data acquisition module, a data storage and data warehouse module, an energy efficiency analysis and diagnosis module, and an intelligent scheduling and energy-saving optimization module;
[0005] Data collection module: Deploy sensor components in key energy consumption equipment and areas to realize real-time data collection of multiple energy forms such as electricity, water, gas and steam, and upload the collected sensor data to the cloud platform through 5G technology;
[0006] Data storage and data warehouse module: After obtaining the sensor data transmitted by the data acquisition module, based on the elastic storage technology of the cloud platform, the data is stored in a distributed database, and a data warehouse is established on the cloud platform to store data on demand and preserve historical data for a long time;
[0007] Energy efficiency analysis and diagnosis module: Set energy efficiency benchmarks for each equipment or production link of the enterprise, use machine learning methods to analyze energy efficiency, identify equipment or production links with low energy utilization efficiency, and put forward optimization suggestions;
[0008] Intelligent scheduling and energy-saving optimization module: Based on historical energy demand data, linear regression analysis is used to predict energy load, predict energy demand in different time periods, and schedule energy resources based on this data. Based on the prediction results and real-time data, production scheduling is adjusted or equipment operation status is optimized.
[0009] In a preferred embodiment, in the data acquisition module, key energy consuming equipment and areas include motors, pumps, air conditioners, boilers and related pipe networks, and substation facilities.
[0010] In a preferred embodiment, the sensor assembly includes a power meter, a water pressure sensor, a gas flow meter, and a steam flow meter.
[0011] In a preferred embodiment, the calculation formula of the energy efficiency benchmark value is:
[0012]
[0013] Among them, E baselinc Represents the energy efficiency benchmark of the equipment, P avg Indicates the average power of the device, T oper It represents the working time of the equipment, and Output represents the output of the equipment or production link.
[0014] In a preferred embodiment, in the energy efficiency analysis and diagnosis module, the specific steps of analyzing the energy efficiency are:
[0015] S1. Collect the energy efficiency data of each device and clean and standardize it. The energy efficiency data includes power consumption, working time and output;
[0016] S2. Use machine learning methods to analyze the collected data and evaluate the energy efficiency status of the equipment;
[0017] S3. Identification and reference value E baselinc Compared with equipment or production links with energy efficiency differences, especially the ones with low energy efficiency.
[0018] In a preferred implementation, the optimization suggestions specifically include equipment maintenance, load adjustment, and operation time control.
[0019] In a preferred embodiment, in the intelligent scheduling and energy-saving optimization module, the prediction model formula of linear regression analysis is:
[0020] E forecast =α+β1·t1+β2·t2+...+β n ·t n
[0021] Among them, E forecast represents the predicted energy load, α represents a constant term, β1,β2,...,β n represents the regression coefficient, t1, t2, ..., t n Indicates a time period.
[0022] In a preferred embodiment, in the intelligent scheduling and energy-saving optimization module, an energy-saving objective function is set to optimize equipment energy consumption to reduce energy waste:
[0023]
[0024] Among them, f opt represents the energy-saving optimization objective function, E i represents the actual energy consumption of device i, E taeget represents the energy saving target consumption of device i, and m represents the total number of devices.
[0025] In a preferred embodiment, in the intelligent scheduling and energy-saving optimization module, the optimization steps are specifically as follows:
[0026] S1. Actual energy consumption E of all equipment i and target energy consumption E taeget Compare and identify less energy-efficient equipment;
[0027] S2. Formulate energy-saving strategies based on the energy efficiency differences of equipment and combined with energy load forecast results;
[0028] S3, using genetic algorithm to minimize energy saving optimization objective function f opt , and adjust the equipment's operating status and production scheduling according to the optimization results;
[0029] S4. After implementing the optimization strategy, monitor the energy efficiency of the equipment in real time, evaluate the energy-saving effect, and check whether the actual energy consumption reaches the predetermined target energy consumption; based on the feedback data, further adjust and improve the energy-saving strategy.
[0030] In a preferred implementation, the energy saving strategy is specifically formulated as follows:
[0031] S1. Adjust production scheduling: optimize the production process schedule, balance the load, and avoid excessive energy consumption during peak hours.
[0032] S2. Optimize equipment operation: Reduce unnecessary energy consumption by adjusting the equipment's operating mode, load rate, and process parameters.
[0033] The beneficial effect of the present invention is that a highly integrated energy management system is provided for enterprises through a cloud computing-based big data platform. The system integrates real-time data monitoring, trend analysis, energy optimization scheduling, equipment efficiency monitoring and other functions to improve the energy operation efficiency of enterprises and help enterprises achieve green production and low-carbon operation goals. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] Figure 1 is a flow chart of the method of the present invention;
[0035] Figure 2 This is a system structure block diagram of the present invention. DETAILED DESCRIPTION
[0036] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.
[0037] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.
[0038] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.
[0039] Example 1
[0040] This embodiment provides Figure 1-2 A cloud computing-based smart energy operation and management system is shown, which specifically includes: a data acquisition module, a data storage and data warehouse module, an energy efficiency analysis and diagnosis module, and an intelligent scheduling and energy-saving optimization module;
[0041] Data collection module: Deploy sensor components in key energy consumption equipment and areas to realize real-time data collection of multiple energy forms such as electricity, water, gas and steam, and upload the collected sensor data to the cloud platform through 5G or Wi-Fi technology to ensure the real-time and reliability of the data;
[0042] Data storage and data warehouse module: After obtaining the sensor data transmitted by the data acquisition module, the data is stored in a distributed database based on the elastic storage technology of the cloud platform, supporting data backup and rapid recovery. A data warehouse is established on the cloud platform, such as AWS Redshift, Google BigQuery, and Azure Synapse Analytics, to store data on demand and preserve historical data for a long time, so as to facilitate subsequent analysis and decision support;
[0043] Energy efficiency analysis and diagnosis module: Set energy efficiency benchmarks for each equipment or production link of the enterprise to evaluate and compare actual energy efficiency, use machine learning methods to analyze energy efficiency, identify equipment or production links with low energy utilization efficiency, and put forward optimization suggestions;
[0044] Intelligent scheduling and energy-saving optimization module: Based on historical energy demand data, linear regression analysis is used to predict energy load, predict energy demand in different time periods, and schedule energy resources based on this data. Based on the prediction results and real-time data, production scheduling is adjusted or equipment operation status is optimized to reduce energy waste and implement energy-saving measures.
[0045] In this embodiment, what needs to be explained specifically is the data acquisition module. In the data acquisition module, key energy-consuming equipment and areas include motors, pumps, air conditioners, boilers and related pipe networks, and substation facilities. The sensor components include power meters, water pressure sensors, gas flow meters, and steam flow meters. The installation position of each sensor is determined according to the equipment layout. For example: power sensor: installed at the distribution board and the entrance of the power load; water flow sensor: installed on the water inlet and outlet pipes of the water pipe; gas sensor: installed on the gas pipeline and near the leakage point; steam sensor: installed on the boiler outlet and steam pipeline.
[0046] In this embodiment, what needs to be specifically explained is the data storage and data warehouse module. The distributed database service preferably uses any one of AWS Aurora, Google Bigtable, and Azure Cosmos DB to store data to support distributed management and query of large-scale data. At the same time, data sharding or partitioning strategies are implemented in the distributed database to store sensor data in a dispersed manner by time, region or other dimensions to improve reading and writing efficiency and data management flexibility.
[0047] In this embodiment, the energy efficiency analysis and diagnosis module is specifically required to be explained. The calculation formula of the energy efficiency benchmark value is:
[0048]
[0049] Among them, E baselincIndicates the energy efficiency benchmark of the equipment, unit: kWh / unit output, that is, the amount of energy consumed per unit output, P avg Indicates the average power of the device, in kW, that is, the average power consumption of the device during normal operation, T oper Indicates the working time of the equipment, in hours, that is, the running time of the equipment in a specific period of time. Output indicates the output of the equipment or production link, in units of product quantity or other measurement units, that is, the output of the equipment or production link in a certain period of time;
[0050] In the energy efficiency analysis and diagnosis module, the specific steps for analyzing energy efficiency are as follows:
[0051] S1. Collect the energy efficiency data of each device and clean and standardize it. The energy efficiency data includes power consumption, working time and output;
[0052] S2. Analyze the collected data using a machine learning method. In this application, the machine learning method may use any one of regression analysis, cluster analysis, and support vector machine to evaluate the energy efficiency status of the equipment;
[0053] S3. Identification and reference value E baselinc Compared with equipment or production links with energy efficiency differences, especially those with low energy efficiency;
[0054] Optimization suggestions specifically include equipment maintenance, load adjustment, and operating time control.
[0055] In this embodiment, what needs to be explained specifically is the intelligent scheduling and energy-saving optimization module. In the intelligent scheduling and energy-saving optimization module, the prediction model formula of the linear regression analysis is:
[0056] E forecast =α+β1·t1+β2·t2+...+β n ·t n
[0057] Among them, E forecast represents the predicted energy load, α represents a constant term, β1,β2,...,β n represents the regression coefficient, t1, t2, ..., t n Indicates time period or other influencing factors;
[0058] In the intelligent scheduling and energy-saving optimization module, the energy-saving objective function is set to optimize equipment energy consumption to reduce energy waste:
[0059]
[0060] Among them, f opt represents the energy-saving optimization objective function, that is, the overall goal of equipment energy efficiency optimization, E iIndicates the actual energy consumption of device i, unit: kWh, E taeget The energy consumption target of device i is expressed in kWh, usually set according to the device's baseline energy efficiency or optimization strategy. m represents the total number of devices.
[0061] In the intelligent scheduling and energy-saving optimization module, the optimization steps are as follows:
[0062] S1. Actual energy consumption E of all equipment i and target energy consumption E taeget Compare and identify less energy-efficient equipment;
[0063] S2. Formulate energy-saving strategies based on the energy efficiency differences of equipment and combined with energy load forecast results;
[0064] S3, using genetic algorithm to minimize energy saving optimization objective function f opt , and adjust the equipment's operating status and production scheduling according to the optimization results;
[0065] S4. After implementing the optimization strategy, monitor the energy efficiency of the equipment in real time, evaluate the energy-saving effect, and check whether the actual energy consumption reaches the predetermined target energy consumption; based on the feedback data, further adjust and improve the energy-saving strategy to achieve continuous optimization;
[0066] The specific steps for formulating energy-saving strategies are as follows:
[0067] S1. Adjust production scheduling: optimize the production process schedule, balance the load, and avoid excessive energy consumption during peak hours.
[0068] S2. Optimize equipment operation: Reduce unnecessary energy consumption by adjusting the equipment's operating mode, load rate, and process parameters.
[0069] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0070] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0071] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0072] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0074] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.
[0075] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.
Claims
1. A smart energy operation and management system based on cloud computing, characterized in that: Specifically include: Data acquisition module, data storage and data warehouse module, energy efficiency analysis and diagnosis module, and intelligent scheduling and energy-saving optimization module; Data collection module: Deploy sensor components in key energy consumption equipment and areas to realize real-time data collection of multiple energy forms such as electricity, water, gas and steam, and upload the collected sensor data to the cloud platform through 5G technology; Data storage and data warehouse module: After obtaining the sensor data transmitted by the data acquisition module, based on the elastic storage technology of the cloud platform, the data is stored in a distributed database, and a data warehouse is established on the cloud platform to store data on demand and preserve historical data for a long time; Energy efficiency analysis and diagnosis module: Set energy efficiency benchmarks for each equipment or production link of the enterprise, use machine learning methods to analyze energy efficiency, identify equipment or production links with low energy utilization efficiency, and put forward optimization suggestions; Intelligent scheduling and energy-saving optimization module: Based on historical energy demand data, linear regression analysis is used to predict energy load, predict energy demand in different time periods, and schedule energy resources based on this data. Based on the prediction results and real-time data, production scheduling is adjusted or equipment operation status is optimized.
2. The cloud computing-based smart energy operation and management system according to claim 1, characterized in that: In the data acquisition module, key energy consuming equipment and areas include motors, pumps, air conditioners, boilers and related pipeline networks and substation facilities.
3. The cloud computing-based smart energy operation and management system according to claim 2 is characterized in that: The sensor assembly includes a power meter, a water pressure sensor, a gas flow meter, and a steam flow meter.
4. The cloud computing-based smart energy operation and management system according to claim 3 is characterized in that: The calculation formula of the energy efficiency benchmark value is: Among them, E baselinc Represents the energy efficiency benchmark of the equipment, P avg Indicates the average power of the device, T oper It represents the working time of the equipment, and Output represents the output of the equipment or production link.
5. The cloud computing-based smart energy operation and management system according to claim 4 is characterized in that: In the energy efficiency analysis and diagnosis module, the specific steps for analyzing energy efficiency are as follows: S1. Collect the energy efficiency data of each device and clean and standardize it. The energy efficiency data includes power consumption, working time and output; S2. Use machine learning methods to analyze the collected data and evaluate the energy efficiency status of the equipment; S3. Identification and reference value E baselinc Compared with equipment or production links with energy efficiency differences, especially the ones with low energy efficiency.
6. The cloud computing-based smart energy operation and management system according to claim 5, characterized in that: The optimization suggestions specifically include equipment maintenance, load adjustment, and operating time control.
7. The cloud computing-based smart energy operation and management system according to claim 6, characterized in that: In the intelligent scheduling and energy-saving optimization module, the prediction model formula of linear regression analysis is: E forecast =α+β1·t1+β2·t2+...+β n ·t n Among them, E forecast represents the predicted energy load, α represents a constant term, β1,β2,...,β n represents the regression coefficient, t1, t2, ..., t n Indicates a time period.
8. The cloud computing-based smart energy operation and management system according to claim 7, characterized in that: In the intelligent scheduling and energy-saving optimization module, an energy-saving objective function is set to optimize equipment energy consumption to reduce energy waste: Among them, f opt represents the energy-saving optimization objective function, E i represents the actual energy consumption of device i, E taeget represents the energy saving target consumption of device i, and m represents the total number of devices.
9. The cloud computing-based smart energy operation and management system according to claim 8, characterized in that: In the intelligent scheduling and energy-saving optimization module, the optimization steps are specifically as follows: S1. Actual energy consumption E of all equipment i and target energy consumption E taeget Compare and identify less energy-efficient equipment; S2. Formulate energy-saving strategies based on the energy efficiency differences of equipment and combined with energy load forecast results; S3, using genetic algorithm to minimize energy saving optimization objective function f opt , and adjust the equipment's operating status and production scheduling according to the optimization results; S4. After implementing the optimization strategy, monitor the energy efficiency of the equipment in real time, evaluate the energy-saving effect, and check whether the actual energy consumption reaches the predetermined target energy consumption; based on the feedback data, further adjust and improve the energy-saving strategy.
10. The cloud computing-based smart energy operation and management system according to claim 9, characterized in that: The specific steps for formulating the energy-saving strategy are as follows: S1. Adjust production scheduling: optimize the production process schedule, balance the load, and avoid excessive energy consumption during peak hours. S2. Optimize equipment operation: Reduce unnecessary energy consumption by adjusting the equipment's operating mode, load rate, and process parameters.