CN114169570A - Smart energy management platform based on Internet of things and cloud computing technology - Google Patents
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Abstract
The invention relates to an energy management platform, in particular to a smart energy management platform based on the Internet of things and cloud computing technology, which is characterized in that centralized collection and analysis of water, gas, ambient temperature, cold and heat quantity and the like are added on the basis of a traditional electric energy management system, all energy consumption of a user side is subdivided and counted, and the energy consumption condition of the system is displayed to managers or a decision layer by an intuitive chart, so that effective data support is provided for further energy-saving transformation or equipment upgrading; energy audit can be implemented according to relevant national regulations, the current situation is analyzed, problems are searched, energy-saving potential is mined, feasible energy utilization measures are provided, and an energy audit report is reported to an audit department; the technical scheme provided by the invention can effectively overcome the defects that the prior art can not help enterprises to adjust energy utilization measures in time and can not effectively predict the energy consumption of the system.
Description
Technical Field
The invention relates to an energy management platform, in particular to an intelligent energy management platform based on the Internet of things and cloud computing technology.
Background
The enterprise energy management platform is an information platform which organically combines energy metering, energy management and enterprise management of enterprises in the modes of energy consumption units, secondary energy consumption units, main energy consumption equipment and energy consumption units, carries out energy review by contrasting energy performance by establishing energy standards related to the enterprises, continuously improves and optimizes energy consumption conditions of the enterprises, scientifically plans, reasonably schedules and effectively utilizes energy resources and aims at optimizing energy consumption.
At present, many enterprises begin to respond to government calls and develop energy management system construction. However, the conventional energy management systems adopted by most enterprises still have many disadvantages, which are mainly expressed as follows:
energy consumption equipment cannot be sensed in real time, the running state of the energy consumption equipment cannot be mastered in real time, and inspection is performed only by virtue of maintenance personnel, so that not only is a large amount of manpower and material resources required to be input, but also the problems of untimely information updating, low efficiency and the like exist;
various energy sources cannot be monitored in a centralized manner, data among various energy management systems are not communicated, and the overall energy use can not be detected;
the system only has the function of data collection, cannot reasonably analyze and process data, cannot find abnormal energy consumption conditions in time, cannot mine energy utilization potential, and cannot help enterprises to adjust energy utilization measures in time.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides the intelligent energy management platform based on the Internet of things and the cloud computing technology, which can effectively overcome the defects that an enterprise cannot be helped to adjust energy utilization measures in time and the energy consumption of a system cannot be effectively predicted in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
the intelligent energy management platform based on the Internet of things and the cloud computing technology comprises detection equipment for detecting energy consumption of energy consumption equipment, wherein the detection equipment uploads detection data to a monitoring host through a server node, and
the real-time monitoring display module is used for analyzing and displaying the operation condition of the system in real time based on the detection data and binding and displaying the detection data and the alarm information with the corresponding energy consumption equipment;
the energy consumption statistical module is used for analyzing and researching the system energy consumption according to the detection data, uploading the analysis and research result to the data center, and adjusting the energy distribution strategy according to the analysis and research result;
the energy consumption prediction module is used for constructing a deep learning neural network model, carrying out model training optimization based on system energy consumption data of the data center, predicting the system energy consumption in a future period of time according to the analysis and research result of the energy consumption statistical module, and uploading the prediction result to the scheduling center;
the energy consumption analysis module is used for knowing the energy consumption condition of the system in a multi-dimensional way based on the analysis research result of the energy consumption statistical module and optimizing the energy consumption of the production process and the energy consumption equipment;
the energy consumption auditing module is used for formulating energy transformation target data based on the analysis research result of the energy consumption statistical module and uploading the data to an auditing department;
the report analysis generation module is used for generating various energy consumption analysis reports and charts based on the detection data, displaying relevant indexes of energy consumption equipment in order and presenting the energy consumption condition of the system;
and the energy consumption equipment management module is used for performing classified management on the energy consumption equipment, constructing a management framework about the energy consumption equipment and timely eliminating the energy consumption equipment with larger energy consumption or faults.
Preferably, the real-time monitoring display module constructs a simulation model corresponding to the energy consumption equipment through 2D simulation, and binds and displays the received detection data and alarm information with the corresponding simulation model, so as to present the operating state of the energy consumption equipment and system abnormal information.
Preferably, the real-time monitoring display module counts the alarm information received on the same day, and displays the alarm information in a real-time alarm list according to the receiving time sequence.
Preferably, after receiving the detection data sent by the server node, the monitoring host analyzes and judges the operating state and the energy consumption condition of each energy consumption device;
when the monitoring host analyzes and judges that the energy consumption equipment is in an abnormal operation state or an abnormal energy consumption state, the monitoring host generates alarm information including the equipment number of the energy consumption equipment.
Preferably, the real-time monitoring display module presents power supply, water supply, gas supply, ambient temperature and cold and heat in the system in a chart form based on the detection data.
Preferably, the system further comprises an early warning information generation module for generating early warning information according to the warning information, the early warning information generation module sends the generated early warning information to the monitoring host, and the monitoring host sends the early warning information to the mobile terminal of the relevant responsible person through the communication module.
Preferably, the energy consumption prediction module intercepts and selects system energy consumption data in a continuous time period from a data center, takes the previous part of the system energy consumption data in the continuous time period as a prediction training set, and takes the rest part of the system energy consumption data as a prediction test set;
inputting the prediction training set into the constructed deep learning neural network model to obtain a preliminary prediction result of the system energy consumption;
determining an error between the preliminary prediction result and the prediction test set according to the loss function, and performing iterative training on the deep learning neural network model based on the error;
when the error reaches a preset error range, finishing the training optimization of the deep learning neural network model;
and inputting the analysis and research result of the energy consumption statistical module into the trained deep learning neural network model to obtain a system energy consumption prediction result in a period of time in the future.
Preferably, the iteratively training the deep learning neural network model based on the error includes:
performing feature extraction by using a deep learning neural network model degree error to obtain a feature sequence;
and on the basis of the characteristic sequence, learning parameters in an output layer of the deep learning neural network model by combining a prediction training set.
Preferably, the energy consumption analysis module optimizes the energy consumption states of different production processes according to energy consumption indexes required to be consumed by different production processes and resource conditions owned by enterprises, compares the energy consumption states with energy consumption equipment, and selects the energy consumption equipment with lower energy consumption.
Preferably, the system further comprises a system configuration module for performing user management, role management, authority management, alarm policy configuration, early warning policy configuration and system parameter maintenance.
(III) advantageous effects
Compared with the prior art, the intelligent energy management platform based on the Internet of things and the cloud computing technology has the following beneficial effects:
1) on the basis of a traditional electric energy management system, centralized collection and analysis of water, gas, ambient temperature, cold and heat and the like are added, and all energy consumption of a user side is subdivided and counted, so that the energy consumption condition of the system is displayed to managers or decision layers by visual charts, and effective data support is provided for further energy-saving transformation or equipment upgrading;
2) energy audit can be implemented according to relevant national regulations, the current situation is analyzed, problems are searched, energy-saving potential is mined, feasible energy utilization measures are provided, and an energy audit report is reported to an audit department;
3) through training optimization of the deep learning neural network model, the deep learning neural network model can accurately predict system energy consumption in a future period of time, and a prediction result is uploaded to a dispatching center, so that energy reasonable dispatching of the dispatching center is realized, and decentralized or centralized control over energy is strengthened.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The utility model provides a wisdom energy management platform based on thing networking and cloud computing technology, as shown in fig. 1, including the check out test set that is used for detecting energy consumption equipment energy consumption, check out test set uploads the testing data to monitoring host computer through the server node to and
and the real-time monitoring display module is used for analyzing and displaying the operation condition of the system in real time based on the detection data and binding and displaying the detection data and the alarm information with the corresponding energy consumption equipment.
The real-time monitoring display module displays power supply, water supply, gas supply, ambient temperature and cold and heat quantity in the system in a chart form based on the detection data. For example, the power supply can adopt a hyperbolic diagram to show the power consumption trend, the real-time value and the limit value; the gas supply can adopt a hyperbolic diagram to show the gas consumption trend, and the real-time value is compared with a limit value; the environmental temperature can adopt a hyperbolic diagram to show the trend change of the environmental temperature, and whether the environmental temperature exceeds the limit or not is visually displayed.
And after receiving the detection data sent by the server node, the monitoring host analyzes and judges the running state and the energy consumption condition of each energy consumption device. When the monitoring host analyzes and judges that the energy consumption equipment is in an abnormal operation state or an abnormal energy consumption state, the monitoring host generates alarm information including the equipment number of the energy consumption equipment.
The real-time monitoring display module constructs a simulation model corresponding to the energy consumption equipment through 2D simulation, and binds and displays the received detection data and alarm information with the corresponding simulation model so as to present the running state of the energy consumption equipment and system abnormal information. And when the alarm information exists, the simulation model corresponding to the consumption equipment is subjected to flash prompt in the current user interface.
And the real-time monitoring display module counts the alarm information received on the same day and uniformly displays the alarm information in a real-time alarm list according to the receiving time sequence.
The system also comprises an early warning information generation module used for generating early warning information according to the warning information, the early warning information generation module sends the generated early warning information to the monitoring host, and the monitoring host sends the early warning information to the mobile terminal of the relevant responsible person in time in the modes of short messages, mails, voices and the like.
And the energy consumption statistical module is used for analyzing and researching the system energy consumption according to the detection data, uploading the analysis and research result to the data center, and adjusting the energy distribution strategy according to the analysis and research result.
The analysis of energy consumption state needs to use various analysis methods to analyze, study, analyze and summarize the energy use condition from quality to quality, and the common methods are as follows: comparative analysis, statistical grouping, structural analysis, dynamic analysis, and factor analysis. By using different analysis methods, holes and unreasonables in the energy use process are found out, so that the energy distribution strategy is adjusted, and the waste in the energy use process is reduced.
The energy consumption prediction module is used for constructing a deep learning neural network model, carrying out model training optimization based on system energy consumption data of the data center, predicting the system energy consumption in a future period of time according to the analysis and research result of the energy consumption statistical module, and uploading the prediction result to the dispatching center.
The energy consumption prediction module intercepts and selects system energy consumption data in a continuous time period from a data center, takes the previous part of the system energy consumption data in the continuous time period as a prediction training set, and takes the rest part of the system energy consumption data as a prediction test set;
inputting the prediction training set into the constructed deep learning neural network model to obtain a preliminary prediction result of the system energy consumption;
determining an error between the preliminary prediction result and the prediction test set according to the loss function, and performing iterative training on the deep learning neural network model based on the error;
when the error reaches a preset error range, finishing the training optimization of the deep learning neural network model;
and inputting the analysis and research result of the energy consumption statistical module into the trained deep learning neural network model to obtain a system energy consumption prediction result in a period of time in the future.
The iterative training of the deep learning neural network model based on the errors comprises the following steps:
performing feature extraction by using a deep learning neural network model degree error to obtain a feature sequence;
and on the basis of the characteristic sequence, learning parameters in an output layer of the deep learning neural network model by combining a prediction training set.
According to the technical scheme, the deep learning neural network model can accurately predict the system energy consumption in a future period through training optimization of the deep learning neural network model, the prediction result is uploaded to the dispatching center, reasonable energy dispatching of the dispatching center is achieved, and decentralized or centralized control over energy is strengthened.
And the energy consumption analysis module is used for knowing the energy consumption condition of the system in a multi-dimensional way based on the analysis research result of the energy consumption statistical module and optimizing the energy consumption of the production process and the energy consumption equipment.
The energy consumption analysis module optimizes the energy consumption states of different production processes according to the energy consumption indexes required to be consumed by different production processes and the resource conditions of enterprises, compares the energy consumption states with energy consumption equipment, and selects the energy consumption equipment with lower energy consumption.
The system energy consumption data of the enterprise in the year and the quarter are analyzed, the energy consumption of a single product, the energy consumption of the whole factory and the energy consumption of a product production process are known, the energy consumption analysis is carried out from multiple dimensions, the difference between the enterprise and other enterprises in the same industry is known through a data center, the production process is optimized, and the energy consumption equipment is improved and upgraded.
And the energy consumption auditing module is used for formulating energy transformation target data based on the analysis research result of the energy consumption statistical module and uploading the data to an auditing department.
The method comprises the steps of collecting system energy consumption data of an enterprise, auditing the energy consumption condition of the enterprise, knowing defects of the enterprise in the aspect of saving energy consumption, drawing up an energy transformation target, submitting the energy transformation target data to an auditing department, and ensuring that the enterprise strives for realizing the energy transformation target.
And the report analysis generation module is used for generating various energy consumption analysis reports and charts based on the detection data, displaying relevant indexes of the energy consumption equipment in order and presenting the energy consumption condition of the system.
The report statistical analysis is a measurement basis of the operation and maintenance quality of the energy management platform, the report is flexible to generate and diverse in form, constantly changing statistical requirements can be met, the report analysis can orderly show relevant indexes of energy consumption equipment, various energy consumption analysis reports and charts can be generated, the system energy consumption condition, the system operation condition and the like can be presented, and a scientific and quantifiable basis is provided for fault diagnosis and leadership decision making.
And the report analysis generation module visually displays the energy consumption condition of the system in a chart mode. A number of charts are used in the system, including line graphs, area graphs, pie charts, 3D graphs, and the like. Echarts is a diagram library written by pure JavaScript and can be added with interactive diagrams on a Web website or a Web application program simply and conveniently. The Echarts interface is beautiful, and the operation can be performed without plug-ins as Flash and Java because of the use of JavaScript writing, and the operation speed is high. In addition, Echarts has good compatibility and can support most current browsers.
And the energy consumption equipment management module is used for performing classified management on the energy consumption equipment, constructing a management framework about the energy consumption equipment and timely eliminating the energy consumption equipment with larger energy consumption or faults.
Enterprises need to carry out unified management on energy consumption equipment, classify the energy consumption equipment, make identification clear, use the identification as a link for managing the energy consumption equipment, construct a management framework about the energy consumption equipment, and realize management in the aspects of ledgers, overhaul, defects and change. When an enterprise manages the energy consumption equipment in real time, the enterprise can know the running state and the efficiency of the equipment, and timely eliminates the equipment with larger energy consumption or faults, so that safety accidents caused by equipment faults are avoided.
In the technical scheme of the application, the system configuration module is used for performing user management, role management, authority management, alarm strategy configuration, early warning strategy configuration and system parameter maintenance. In addition, the energy management platform can upload the basic information of energy consumption enterprises and system energy consumption data to a provincial or national platform according to the requirements of a system platform interface protocol specification defined by the energy consumption unit energy consumption online monitoring system technical specification, and the uploaded data are encrypted and transmitted through an HTTPS protocol. If the data transmission fails or times out (network failure) the data will be retransmitted until a successful feedback message is received.
According to the technical scheme, the monitoring host, the communication module and the detection equipment are used as basic tools, one or more combined networking modes of a field bus, an optical fiber ring network or wireless communication are adopted according to actual field conditions, a basic platform is provided for real-time data acquisition and remote management and control of a large building, the monitoring system can form an arbitrary complex monitoring system with the detection equipment, and an object-oriented layered, hierarchical and distributed intelligent structure is adopted in an open, networked, unitized and organized mode.
The energy management platform architecture adopts an MVC design mode based on a J2EE framework and using a B/S structure. The B/S structure has strong portability, can run in different operating systems (Windows, RedHatLinux and the like), and realizes cross-platform deployment.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. The utility model provides a wisdom energy management platform based on thing networking and cloud computing technique which characterized in that: including being used for detecting the check out test set of energy consumption equipment energy consumption, check out test set uploads the testing data to the monitoring host computer through the server node to and
the real-time monitoring display module is used for analyzing and displaying the operation condition of the system in real time based on the detection data and binding and displaying the detection data and the alarm information with the corresponding energy consumption equipment;
the energy consumption statistical module is used for analyzing and researching the system energy consumption according to the detection data, uploading the analysis and research result to the data center, and adjusting the energy distribution strategy according to the analysis and research result;
the energy consumption prediction module is used for constructing a deep learning neural network model, carrying out model training optimization based on system energy consumption data of the data center, predicting the system energy consumption in a future period of time according to the analysis and research result of the energy consumption statistical module, and uploading the prediction result to the scheduling center;
the energy consumption analysis module is used for knowing the energy consumption condition of the system in a multi-dimensional way based on the analysis research result of the energy consumption statistical module and optimizing the energy consumption of the production process and the energy consumption equipment;
the energy consumption auditing module is used for formulating energy transformation target data based on the analysis research result of the energy consumption statistical module and uploading the data to an auditing department;
the report analysis generation module is used for generating various energy consumption analysis reports and charts based on the detection data, displaying relevant indexes of energy consumption equipment in order and presenting the energy consumption condition of the system;
and the energy consumption equipment management module is used for performing classified management on the energy consumption equipment, constructing a management framework about the energy consumption equipment and timely eliminating the energy consumption equipment with larger energy consumption or faults.
2. The intelligent energy management platform based on the internet of things and cloud computing technology of claim 1, wherein: the real-time monitoring display module constructs a simulation model corresponding to the energy consumption equipment through 2D simulation, and binds and displays the received detection data and alarm information with the corresponding simulation model so as to present the running state of the energy consumption equipment and system abnormal information.
3. The intelligent energy management platform based on the internet of things and cloud computing technology of claim 2, wherein: and the real-time monitoring display module counts the alarm information received on the same day and uniformly displays the alarm information in a real-time alarm list according to the receiving time sequence.
4. The intelligent energy management platform based on the internet of things and cloud computing technology of claim 2, wherein: after receiving the detection data sent by the server node, the monitoring host analyzes and judges the running state and the energy consumption condition of each energy consumption device;
when the monitoring host analyzes and judges that the energy consumption equipment is in an abnormal operation state or an abnormal energy consumption state, the monitoring host generates alarm information including the equipment number of the energy consumption equipment.
5. The intelligent energy management platform based on the internet of things and cloud computing technology of claim 4, wherein: and the real-time monitoring display module displays power supply, water supply, gas supply, ambient temperature and cold and heat quantity in the system in a chart form based on the detection data.
6. The intelligent energy management platform based on the internet of things and cloud computing technology of claim 4, wherein: the system comprises a monitoring host and a communication module, and is characterized by further comprising an early warning information generation module used for generating early warning information according to the warning information, wherein the early warning information generation module sends the generated early warning information to the monitoring host, and the monitoring host sends the early warning information to the mobile terminal of the relevant responsible person through the communication module.
7. The intelligent energy management platform based on the internet of things and cloud computing technology as claimed in claim 1 or 6, wherein: the energy consumption prediction module intercepts and selects system energy consumption data in a continuous time period from a data center, takes the previous part of the system energy consumption data in the continuous time period as a prediction training set, and takes the rest part of the system energy consumption data as a prediction test set;
inputting the prediction training set into the constructed deep learning neural network model to obtain a preliminary prediction result of the system energy consumption;
determining an error between the preliminary prediction result and the prediction test set according to the loss function, and performing iterative training on the deep learning neural network model based on the error;
when the error reaches a preset error range, finishing the training optimization of the deep learning neural network model;
and inputting the analysis and research result of the energy consumption statistical module into the trained deep learning neural network model to obtain a system energy consumption prediction result in a period of time in the future.
8. The internet of things and cloud computing technology based smart energy management platform of claim 7, wherein: the iterative training of the deep learning neural network model based on the errors comprises the following steps:
performing feature extraction by using a deep learning neural network model degree error to obtain a feature sequence;
and on the basis of the characteristic sequence, learning parameters in an output layer of the deep learning neural network model by combining a prediction training set.
9. The internet of things and cloud computing technology based smart energy management platform of claim 8, wherein: the energy consumption analysis module optimizes the energy consumption states of different production procedures according to the energy consumption indexes required to be consumed by different production procedures and the resource conditions of enterprises, compares the energy consumption states with energy consumption equipment, and selects the energy consumption equipment with lower energy consumption.
10. The internet of things and cloud computing technology based smart energy management platform of claim 9, wherein: the system also comprises a system configuration module used for carrying out user management, role management, authority management, alarm strategy configuration, early warning strategy configuration and system parameter maintenance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202111301720.8A CN114169570A (en) | 2021-11-04 | 2021-11-04 | Smart energy management platform based on Internet of things and cloud computing technology |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115081723A (en) * | 2022-07-04 | 2022-09-20 | 中用科技有限公司 | Intelligent factory energy management system and management method |
| CN116048023A (en) * | 2023-02-02 | 2023-05-02 | 广东亿炼智能科技有限公司 | Fine energy management and control method, system, internet of things cloud management and control server and storage medium thereof |
| CN116523276A (en) * | 2023-07-04 | 2023-08-01 | 天津市蓟州区民力新能源科技有限公司 | High-efficiency energy utilization management platform based on intelligent control system |
| CN116647819A (en) * | 2023-07-27 | 2023-08-25 | 深圳市中科智联有限公司 | Instrument energy consumption monitoring method and system based on sensor network |
| CN116702424A (en) * | 2023-04-26 | 2023-09-05 | 淮阴工学院 | Big data intelligence emission reduction system |
| CN117151701A (en) * | 2023-10-31 | 2023-12-01 | 山东欣历能源有限公司 | Industrial waste heat recycling system for cogeneration |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115081723A (en) * | 2022-07-04 | 2022-09-20 | 中用科技有限公司 | Intelligent factory energy management system and management method |
| CN116048023A (en) * | 2023-02-02 | 2023-05-02 | 广东亿炼智能科技有限公司 | Fine energy management and control method, system, internet of things cloud management and control server and storage medium thereof |
| CN116048023B (en) * | 2023-02-02 | 2024-01-26 | 广东亿炼智能科技有限公司 | Fine energy management and control method, system, internet of things cloud management and control server and storage medium thereof |
| CN116702424A (en) * | 2023-04-26 | 2023-09-05 | 淮阴工学院 | Big data intelligence emission reduction system |
| CN116702424B (en) * | 2023-04-26 | 2024-06-14 | 淮阴工学院 | Big data intelligence emission reduction system |
| CN116523276A (en) * | 2023-07-04 | 2023-08-01 | 天津市蓟州区民力新能源科技有限公司 | High-efficiency energy utilization management platform based on intelligent control system |
| CN116523276B (en) * | 2023-07-04 | 2023-09-08 | 天津市蓟州区民力新能源科技有限公司 | High-efficiency energy utilization management platform based on intelligent control system |
| CN116647819A (en) * | 2023-07-27 | 2023-08-25 | 深圳市中科智联有限公司 | Instrument energy consumption monitoring method and system based on sensor network |
| CN116647819B (en) * | 2023-07-27 | 2023-11-07 | 深圳市中科智联有限公司 | Instrument energy consumption monitoring method and system based on sensor network |
| CN117151701A (en) * | 2023-10-31 | 2023-12-01 | 山东欣历能源有限公司 | Industrial waste heat recycling system for cogeneration |
| CN117151701B (en) * | 2023-10-31 | 2024-02-09 | 山东欣历能源有限公司 | Industrial waste heat recycling system for cogeneration |
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