CN115358889A - BP neural network-based industrial device energy consumption control method - Google Patents
BP neural network-based industrial device energy consumption control method Download PDFInfo
- Publication number
- CN115358889A CN115358889A CN202211005965.0A CN202211005965A CN115358889A CN 115358889 A CN115358889 A CN 115358889A CN 202211005965 A CN202211005965 A CN 202211005965A CN 115358889 A CN115358889 A CN 115358889A
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- industrial device
- neural network
- value
- trained
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005265 energy consumption Methods 0.000 title claims abstract description 97
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 20
- 238000003062 neural network model Methods 0.000 claims abstract description 24
- 238000012549 training Methods 0.000 claims abstract description 6
- 230000002159 abnormal effect Effects 0.000 claims abstract description 5
- 238000005516 engineering process Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention provides an energy consumption control method of an industrial device based on a BP (back propagation) neural network, which comprises the following steps: collecting historical energy consumption information and influence factor information of the industrial device and historical data corresponding to the historical energy consumption information and the influence factor information to form an energy consumption characteristic data set; inputting the energy consumption characteristic data set into a BP neural network model to be trained for training until the trained BP neural network model converges to obtain a trained BP neural network model; inputting the collected real-time energy consumption data of the industrial device into a trained BP neural network model for operation to obtain an energy consumption predicted value of the industrial device; and when the absolute value of the deviation between the predicted energy consumption value and the actual energy consumption value is larger than the set deviation fluctuation value, the industrial device gives an abnormal alarm. The industrial device energy consumption control method based on the BP neural network can be suitable for various industrial devices, is simple in control process and accurate in energy consumption control, and can be widely applied to the field of energy consumption control.
Description
Technical Field
The invention relates to a data anomaly detection technology, in particular to an energy consumption control method for an industrial device based on a BP (Back propagation) neural network.
Background
In industrial production, the energy consumption of industrial plants varies depending on the specific conditions of load, ambient temperature, processing scheme, etc. At present, the control of the energy consumption of the industrial device does not take into account the changes caused by the above specific situations, and it is obviously inaccurate and unreasonable to control the energy consumption only in an averaging manner. However, if various management and control methods affecting the energy consumption factors of the industrial devices are considered, the management and control process is too complicated, and therefore an energy consumption management and control technology which is simple in management and control process, accurate in energy consumption management and control and suitable for various industrial devices is needed.
Therefore, in the prior art, an energy consumption control method which is suitable for various industrial devices, simple in control process and accurate in energy consumption control is not available.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide an energy consumption control method for an industrial device based on a BP neural network, which is suitable for various industrial devices, and has a simple control process and relatively accurate energy consumption control.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
an industrial device energy consumption control method based on a BP neural network comprises the following steps:
step 1, collecting historical energy consumption information and influence factor information of an industrial device as characteristic information, and collecting corresponding historical characteristic data to form an energy consumption characteristic data set.
Step 2, inputting the energy consumption characteristic data set into a BP neural network model to be trained for training until the trained BP neural network model converges to obtain a trained BP neural network model, and setting a deviation fluctuation value;
step 3, inputting the acquired real-time energy consumption data of the industrial device into a trained BP neural network model for operation to obtain an energy consumption predicted value of the industrial device; comparing the predicted energy consumption value with the actual energy consumption value: and when the absolute value of the deviation between the predicted energy consumption value and the actual energy consumption value is larger than the set deviation fluctuation value, the industrial device gives an abnormal alarm.
In summary, in the method for managing and controlling energy consumption of an industrial device based on a BP neural network, historical related energy consumption information of the industrial device and a corresponding feature data set thereof are collected first; inputting the characteristic data set into a BP neural network model to be trained for training, and obtaining the trained BP neural network model when the trained BP neural network model is converged. And predicting the energy consumption of the industrial device by utilizing the acquired real-time energy consumption data of the industrial device and the trained BP neural network model, and when the absolute value of the deviation between the predicted energy consumption value and the actual energy consumption value is larger than the set deviation fluctuation value, performing exception alarm by the industrial device. Therefore, the BP neural network-based industrial device energy consumption control method does not need to consider the complex details of the operation of each industrial device, realizes the energy consumption control of the industrial devices with complex and changeable actual conditions on the whole, and has the advantages of simple control process and more accurate energy consumption control.
Drawings
Fig. 1 is a general flow diagram of the BP neural network-based industrial device energy consumption control method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a schematic general flow chart of the BP neural network-based industrial device energy consumption control method according to the present invention. As shown in fig. 1, the method for controlling energy consumption of an industrial device based on a Back Propagation (BP) neural network according to the present invention includes the following steps:
step 1, collecting historical energy consumption information and influence factor information of an industrial device as characteristic information, and meanwhile collecting corresponding historical characteristic data to form an energy consumption characteristic data set.
And 2, inputting the energy consumption characteristic data set into a BP neural network model to be trained for training until the trained BP neural network model is converged to obtain the trained BP neural network model, and setting a deviation fluctuation value.
In the present invention, the BP neural network used is the prior art, and is not described herein again.
Step 3, inputting the acquired real-time energy consumption data of the industrial device into the trained BP neural network model for operation to obtain an energy consumption predicted value of the industrial device; comparing the predicted energy consumption value with the actual energy consumption value: and when the absolute value of the deviation between the predicted energy consumption value and the actual energy consumption value is larger than the set deviation fluctuation value, the industrial device gives an abnormal alarm.
In practical application, when the absolute value of the deviation between the predicted energy consumption value and the actual energy consumption value is smaller than or equal to the set deviation fluctuation value, the industrial device is indicated to normally operate. And when the absolute value of the deviation between the predicted energy consumption value and the actual energy consumption value is larger than the set deviation fluctuation value, indicating that the industrial device is abnormal in operation, and alarming by the industrial device at the moment.
In summary, in the method for managing and controlling energy consumption of an industrial device based on a BP neural network, historical related energy consumption information of the industrial device and a corresponding characteristic data set thereof are collected first; inputting the characteristic data set into a BP neural network model to be trained for training, and obtaining the trained BP neural network model when the trained BP neural network model is converged. And predicting the energy consumption of the industrial device by utilizing the acquired real-time energy consumption data of the industrial device and the trained BP neural network model, and when the absolute value of the deviation between the predicted energy consumption value and the actual energy consumption value is larger than the set deviation fluctuation value, performing abnormity alarm by the industrial device. Therefore, the BP neural network-based industrial device energy consumption control method does not need to consider the complex details of the operation of each industrial device, realizes the energy consumption control of the industrial devices with complex and changeable actual conditions on the whole, and has the advantages of simple control process and more accurate energy consumption control.
In step 1 of the method, the energy consumption information comprises electric energy information and gas energy information of the industrial device, and the influence factor information comprises the operation temperature, the operation time, the maximum energy consumption, the minimum energy consumption and the operation period of the industrial device.
In step 2 of the invention, the deviation fluctuation value is 5% of the energy consumption predicted value.
In step 3 of the invention, the real-time energy consumption data of the industrial device is not less than 10 times of energy consumption data of the industrial device acquired in the same day.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. An energy consumption control method for an industrial device based on a BP neural network is characterized by comprising the following steps:
step 1, collecting historical energy consumption information and influence factor information of an industrial device as characteristic information, and collecting corresponding historical characteristic data to form an energy consumption characteristic data set;
step 2, inputting the energy consumption characteristic data set into a BP neural network model to be trained for training until the trained BP neural network model converges to obtain a trained BP neural network model, and setting a deviation fluctuation value;
step 3, inputting the acquired real-time energy consumption data of the industrial device into a trained BP neural network model for operation to obtain an energy consumption predicted value of the industrial device; comparing the predicted energy consumption value with the actual energy consumption value: and when the absolute value of the deviation between the predicted energy consumption value and the actual energy consumption value is larger than the set deviation fluctuation value, the industrial device gives an abnormal alarm.
2. The method for managing and controlling the energy consumption of the industrial device based on the BP neural network as claimed in claim 1, wherein in step 1, the energy consumption information includes information of electric energy source and gas energy source of the industrial device, and the influence factor information includes operation temperature, operation time, maximum energy consumption, minimum energy consumption and operation period of the industrial device.
3. The BP neural network based industrial device energy consumption control method according to claim 1 or 2, wherein in the step 2, the deviation fluctuation value is 5% of the energy consumption prediction value.
4. The method for managing and controlling the energy consumption of the industrial device based on the BP neural network as claimed in claim 1 or 2, wherein in step 3, the real-time energy consumption data of the industrial device is not less than 10 times of energy consumption data of the industrial device collected in the same day.
5. The method for managing and controlling the energy consumption of the industrial devices based on the BP neural network as claimed in claim 3, wherein in step 3, the real-time energy consumption data of the industrial devices is not less than 10 times of the energy consumption data of the industrial devices acquired in the same day.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211005965.0A CN115358889A (en) | 2022-08-22 | 2022-08-22 | BP neural network-based industrial device energy consumption control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211005965.0A CN115358889A (en) | 2022-08-22 | 2022-08-22 | BP neural network-based industrial device energy consumption control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115358889A true CN115358889A (en) | 2022-11-18 |
Family
ID=84001742
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211005965.0A Pending CN115358889A (en) | 2022-08-22 | 2022-08-22 | BP neural network-based industrial device energy consumption control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115358889A (en) |
-
2022
- 2022-08-22 CN CN202211005965.0A patent/CN115358889A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lindemann et al. | Anomaly detection and prediction in discrete manufacturing based on cooperative LSTM networks | |
| JP2019527413A (en) | Computer system and method for performing root cause analysis to build a predictive model of rare event occurrences in plant-wide operations | |
| WO2024065988A1 (en) | Device monitoring method and system, electronic device, and storage medium | |
| US20220121164A1 (en) | Control apparatus, controller, control system, control method, and computer-readable medium having recorded thereon control program | |
| CN106199174A (en) | Extruder energy consumption predicting abnormality method based on transfer learning | |
| CN117273402B (en) | Energy-saving management system and method for glass deep processing production line based on Internet of Things technology | |
| US20240223661A1 (en) | Methods and smart gas internet of things (iot) systems for smart control of smart gas pipeline network data collection terminals | |
| CN113687609A (en) | Intelligent monitoring system and monitoring method for Internet of things applied to abnormal environment | |
| CN114881167B (en) | Abnormality detection method, abnormality detection device, electronic device, and medium | |
| CN118089287B (en) | Water chiller energy efficiency optimizing system based on intelligent algorithm | |
| CN110426996B (en) | Environmental pollution monitoring method based on big data and artificial intelligence | |
| CN115962551A (en) | Intelligent air conditioner control system and method for building automatic control | |
| CN117111446B (en) | Fuzzy PID control optimization method for magnetic suspension flywheel motor | |
| CN115358889A (en) | BP neural network-based industrial device energy consumption control method | |
| US11687772B2 (en) | Optimization of cyber-physical systems | |
| Lee et al. | Autoencoder-based detector for distinguishing process anomaly and sensor failure | |
| Mesa-Jiménez et al. | Early warning signals of failures in building management systems | |
| CN103472721B (en) | The pesticide waste liquid incinerator furnace temperature optimization system of self-adaptation machine learning and method | |
| CN117648312A (en) | Super-parameter automatic adjustment method and system suitable for QG-SVM algorithm model | |
| Patil et al. | Economic design of moving average control chart for non-normal data using variable sampling intervals | |
| CN117871771B (en) | Big data-based gas energy monitoring method | |
| CN116760033B (en) | Real-time power demand prediction system based on artificial intelligence | |
| CN117076260B (en) | Parameter and equipment abnormality detection method and device | |
| CN117113886B (en) | Pressure prediction method and device | |
| EP4160344A1 (en) | Monitoring apparatus, monitoring method, and monitoring program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |