CN117930687A - Intelligent energy optimization control system for port - Google Patents
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- 238000005457 optimization Methods 0.000 title claims abstract description 43
- 238000005265 energy consumption Methods 0.000 claims abstract description 157
- 230000002159 abnormal effect Effects 0.000 claims abstract description 101
- 238000004458 analytical method Methods 0.000 claims abstract description 32
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 40
- 238000012544 monitoring process Methods 0.000 claims description 30
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 24
- 239000000446 fuel Substances 0.000 claims description 21
- 239000000295 fuel oil Substances 0.000 claims description 21
- 238000000034 method Methods 0.000 claims description 20
- 239000003345 natural gas Substances 0.000 claims description 20
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 19
- 229910052799 carbon Inorganic materials 0.000 claims description 19
- 238000010248 power generation Methods 0.000 claims description 18
- 239000003209 petroleum derivative Substances 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
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- 238000012549 training Methods 0.000 claims description 3
- 230000005856 abnormality Effects 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 10
- 239000001257 hydrogen Substances 0.000 description 10
- 229910052739 hydrogen Inorganic materials 0.000 description 10
- 230000005611 electricity Effects 0.000 description 5
- 239000007789 gas Substances 0.000 description 3
- 239000003921 oil Substances 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
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- 238000012098 association analyses Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- -1 electricity Substances 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
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- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000012545 processing Methods 0.000 description 1
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- 230000000007 visual effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
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Abstract
The application discloses an intelligent energy optimization control system for a port, which relates to the technical field of optimization control and comprises the steps of collecting all energy sources of a target port area and acquiring the energy consumption conditions corresponding to all the energy sources in a preset time interval; comparing the energy consumption condition of each energy source of the target harbor district in the preset time interval with the reference energy consumption corresponding to each energy source in the preset time interval, and further confirming the abnormal energy source energy consumption; the energy type corresponding to the abnormal energy consumption is confirmed, dynamic tracking is carried out based on the energy type corresponding to the abnormal energy consumption, and then abnormal nodes corresponding to the abnormal energy consumption are confirmed; and carrying out anomaly analysis on the abnormal nodes and outputting analysis results, and further carrying out optimization control adjustment on the abnormal nodes based on the analysis results. The application has the effect of improving the accuracy of digital management and control of the energy business in the harbor district.
Description
Technical Field
The application relates to the technical field of optimal control, in particular to an intelligent energy optimal control system for ports.
Background
The intelligent energy is an industry which takes digital and intelligent energy production, storage, supply, consumption, service and the like as main lines, pursues the coordinated supply of various energy sources such as transverse electricity, oil, gas, water, hydrogen and the like, realizes the interactive optimization among links such as longitudinal energy source-network-charge-storage-use links and the like, constructs an energy network with seamless connection between the Internet of things and the Internet, and provides energy integrated service for end users.
In the related art, a port usually adopts manual meter reading to periodically output port energy consumption monitoring data, so that the problems of complex procedures, incapability of confirming the accuracy of the data and the like exist, the digital management and control of the current port energy business cannot be met, and the problem to be improved exists.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides an intelligent energy optimization control system for a port.
In a first aspect, the present application provides an intelligent energy optimization control method for a port, comprising the steps of:
collecting each energy source of a target harbor district, and obtaining the energy consumption condition corresponding to each energy source in a preset time interval, wherein the energy sources comprise water energy, electric energy, petroleum and natural gas;
Comparing the energy consumption condition of each energy source of the target harbor district in the preset time interval with the reference energy consumption corresponding to each energy source in the preset time interval, and further confirming the abnormal energy source energy consumption;
Confirming the energy type corresponding to the abnormal energy consumption, dynamically tracking based on the energy type corresponding to the abnormal energy consumption, and further confirming an abnormal node corresponding to the abnormal energy consumption;
and carrying out anomaly analysis on the anomaly node and outputting an analysis result, and further carrying out optimization control adjustment on the anomaly node based on the analysis result.
Preferably, the obtaining the energy consumption condition corresponding to each energy source in the preset time interval specifically includes:
confirming each water consumption node in the target harbor district, monitoring the water consumption condition of each water consumption node in a preset time interval, and further confirming the total water consumption in the preset time interval in the target harbor district;
Confirming each power utilization node in the target harbor district, monitoring the power utilization condition of each power utilization node in a preset time interval, and further confirming the power load in the preset time interval in the target harbor district;
Extracting each fuel oil device in the target harbor district, and monitoring the fuel oil condition of each fuel oil device in a preset time interval, so as to confirm the fuel oil consumption in the preset time interval in the target harbor district;
And confirming each natural gas node in the target harbor, monitoring the use condition of the natural gas in a preset time interval of each natural gas node, and further confirming the total consumption of the natural gas in the preset time interval of the target harbor.
Preferably, the reference energy consumption corresponding to each energy source in the preset time interval specifically includes:
extracting historical energy consumption corresponding to each energy source in a historical preset time interval from a cloud database, and carrying out correlation analysis on the historical energy consumption corresponding to each energy source and the historical preset time interval to acquire target correlation information corresponding to the historical energy consumption corresponding to each energy source and the historical preset time interval;
And inputting the target correlation information corresponding to the historical energy consumption and the historical preset time interval into the big data model for training, confirming the energy consumption prediction model corresponding to each energy source, and further extracting the reference energy consumption corresponding to each energy source in the preset time interval based on the energy consumption prediction model corresponding to each energy source.
Preferably, the reference energy consumption corresponding to each energy source in the preset time interval specifically further includes:
When the energy source is electric energy, a wind power generation model and a photovoltaic power generation model corresponding to the target harbor area are built;
The wind power generation model comprises:
Pt is the actual output power of the fan at the t moment, pr is the rated output power of the fan, vt is the wind speed of the altitude section where the impeller at the t moment is located, v0 is the wind cut-in speed of the fan, v1 is the rated wind speed of the fan, and v'0 is the wind cut-out speed of the fan;
The photovoltaic power generation model comprises: pi=min (ii×p, P), where Pi is the actual output power of the photovoltaic system corresponding to the i-th moment, ii is the horizontal surface radiance of the photovoltaic panel corresponding to the i-th moment, and P is the rated power of the photovoltaic system;
and confirming the actual generated energy corresponding to the preset time interval according to the wind power generation model and the photovoltaic power generation model corresponding to the target harbor district, and setting the actual generated energy corresponding to the preset time interval as the reference energy consumption corresponding to the electric energy in the preset time interval.
Preferably, the dynamic tracking is performed on the basis of the energy type corresponding to the abnormal energy consumption, so as to confirm the abnormal node corresponding to the abnormal energy consumption, and the method specifically includes:
dividing a target harbor area into target subareas based on energy types corresponding to abnormal energy consumption, and confirming energy unit types corresponding to the target subareas;
Matching the energy unit type corresponding to each target sub-region with an energy consumption typical unit library, extracting key nodes of the energy unit type corresponding to each target sub-region, and further obtaining energy consumption monitoring values and energy consumption prediction intervals of each key node of each target sub-region in a preset time interval;
When the energy consumption monitoring value of the key node in the target subarea in the preset time interval is not in the energy consumption prediction interval corresponding to the key node in the target subarea, setting the key node as an abnormal node corresponding to abnormal energy consumption.
Preferably, the performing an anomaly analysis on the anomaly node and outputting an analysis result, and further performing an optimization control adjustment on the anomaly node based on the analysis result, specifically includes:
Confirming an energy consumption monitoring value and an energy consumption prediction interval corresponding to the abnormal node, and further based on a calculation formula Calculating an energy consumption optimization coefficient beta corresponding to the abnormal node, wherein G' is expressed as an energy consumption monitoring value corresponding to the abnormal node, and Gmin and Gmax are respectively expressed as a minimum value and a maximum value of an energy consumption prediction interval corresponding to the abnormal node;
And controlling and adjusting the energy consumption value corresponding to the abnormal node based on the energy consumption optimization coefficient corresponding to the abnormal node.
Preferably, the extracting each fuel device in the target port area, and monitoring the fuel condition of each fuel device in a preset time interval, specifically further includes:
Acquiring a first carbon emission amount emitted by each fuel oil device in a target harbor district when the fuel oil device operates within a preset time interval;
confirming the fuel consumption of each fuel device in a target harbor district within a preset time interval, and further confirming the second carbon emission corresponding to the fuel device based on the fuel consumption;
And based on the comparison of the first carbon emission amount and the second carbon emission amount with the carbon emission standard amount corresponding to the preset time interval, confirming the abnormal carbon emission condition corresponding to the target harbor district within the preset time interval.
In a second aspect, the present application provides an intelligent energy optimization control system for a port, comprising:
The energy consumption acquisition module is used for acquiring each energy source of the target harbor district and acquiring the energy consumption condition corresponding to each energy source in a preset time interval, wherein the energy sources comprise water energy, electric energy, petroleum and natural gas;
the energy consumption comparison module is used for comparing the energy consumption condition corresponding to each energy source in the preset time interval of the target harbor district with the reference energy consumption corresponding to each energy source in the preset time interval, so as to confirm the abnormal energy source energy consumption;
The abnormal node confirming module is used for confirming the energy type corresponding to the abnormal energy consumption, dynamically tracking the abnormal energy consumption based on the energy type corresponding to the abnormal energy consumption and further confirming the abnormal node corresponding to the abnormal energy consumption;
And the optimization control module is used for carrying out abnormal analysis on the abnormal nodes and outputting analysis results, so as to carry out optimization control and adjustment on the abnormal nodes based on the analysis results.
In a third aspect, the present application provides a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform an intelligent energy optimization control method for a port as set forth in any one of the above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. The invention provides an intelligent energy optimization control method for a port, which is characterized in that the energy consumption condition corresponding to each energy source in a preset time interval is obtained, and the energy consumption condition corresponding to each energy source in the preset time interval of a target port area is compared with the reference energy consumption corresponding to each energy source in the preset time interval to confirm abnormal energy consumption; the abnormal energy consumption is dynamically tracked and confirmed to obtain abnormal nodes, so that the abnormal nodes are optimally controlled and regulated, the integrated comprehensive monitoring and intelligent analysis of the water, electricity and oil gas in the target harbor are realized, and the accuracy of digital management and control of the harbor energy business is effectively improved;
2. The energy consumption data of the target harbor is obtained in real time by carrying out association analysis on the generated energy and the electric load of the target harbor, and prediction and early warning are carried out based on abnormal conditions, so that the daily supervision capability of the target harbor is effectively improved.
Drawings
FIG. 1 is a flow chart of a method for intelligent energy optimization control for ports according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a system for intelligent energy optimization control for ports according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
Example 1
The embodiment of the application discloses an intelligent energy optimization control method for a port.
Referring to fig. 1, an intelligent energy optimization control method for a port includes the steps of:
s1, collecting energy sources of a target harbor area, and obtaining energy consumption conditions corresponding to the energy sources in a preset time interval, wherein the energy sources comprise water energy, electric energy, petroleum and natural gas;
s2, comparing the energy consumption condition of each energy source of the target harbor district in a preset time interval with the reference energy consumption corresponding to each energy source in the preset time interval, and further confirming the abnormal energy source energy consumption;
S3, confirming the energy type corresponding to the abnormal energy consumption, dynamically tracking based on the energy type corresponding to the abnormal energy consumption, and further confirming the abnormal node corresponding to the abnormal energy consumption;
s4, carrying out anomaly analysis on the abnormal node, outputting an analysis result, and further carrying out optimization control adjustment on the abnormal node based on the analysis result.
It should be further described that, in step S1, obtaining the energy consumption situation corresponding to each energy source in the preset time interval specifically includes:
confirming each water consumption node in the target harbor district, monitoring the water consumption condition of each water consumption node in a preset time interval, and further confirming the total water consumption in the preset time interval in the target harbor district;
Confirming each power utilization node in the target harbor district, monitoring the power utilization condition of each power utilization node in a preset time interval, and further confirming the power load in the preset time interval in the target harbor district;
Extracting each fuel oil device in the target harbor district, and monitoring the fuel oil condition of each fuel oil device in a preset time interval, so as to confirm the fuel oil consumption in the preset time interval in the target harbor district;
And confirming each natural gas node in the target harbor, monitoring the use condition of the natural gas in a preset time interval of each natural gas node, and further confirming the total consumption of the natural gas in the preset time interval of the target harbor.
Specifically, the total water consumption, the electric load, the fuel consumption and the natural gas consumption in the target harbor district are subjected to visual processing in a preset time interval, and consumption trends of the total water consumption, the electric load, the fuel consumption and the natural gas consumption are predicted in a plurality of continuous preset time intervals.
It should be noted that, in the step S2, the reference energy consumption corresponding to each energy source in the preset time interval specifically includes:
extracting historical energy consumption corresponding to each energy source in a historical preset time interval from a cloud database, and carrying out correlation analysis on the historical energy consumption corresponding to each energy source and the historical preset time interval to acquire target correlation information corresponding to the historical energy consumption corresponding to each energy source and the historical preset time interval;
And inputting the target correlation information corresponding to the historical energy consumption and the historical preset time interval into the big data model for training, confirming the energy consumption prediction model corresponding to each energy source, and further extracting the reference energy consumption corresponding to each energy source in the preset time interval based on the energy consumption prediction model corresponding to each energy source.
Specifically, the correlation analysis is performed on the historical energy consumption corresponding to each energy source and the historical preset time interval, namely the energy consumption condition of the same preset time interval of the history is obtained.
Further, the reference energy consumption corresponding to each energy source in the preset time interval specifically further includes:
When the energy source is electric energy, a wind power generation model and a photovoltaic power generation model corresponding to the target harbor area are built;
The wind power generation model comprises:
Pt is the actual output power of the fan at the t moment, pr is the rated output power of the fan, vt is the wind speed of the altitude section where the impeller at the t moment is located, v0 is the wind cut-in speed of the fan, v1 is the rated wind speed of the fan, and v'0 is the wind cut-out speed of the fan;
The photovoltaic power generation model comprises: pi=min (ii×p, P), where Pi is the actual output power of the photovoltaic system corresponding to the i-th moment, ii is the horizontal surface radiance of the photovoltaic panel corresponding to the i-th moment, and P is the rated power of the photovoltaic system;
and confirming the actual generated energy corresponding to the preset time interval according to the wind power generation model and the photovoltaic power generation model corresponding to the target harbor district, and setting the actual generated energy corresponding to the preset time interval as the reference energy consumption corresponding to the electric energy in the preset time interval.
Specifically, the reference energy consumption corresponding to the electric energy in the preset time interval further comprises the electric power consumption of the hydrogen storage device, namely the electric power consumption of the electrolytic tank and the electric power consumption of the compressor, wherein the electric power consumption of the electrolytic tank is the electric power consumption generated in the process of generating hydrogen through the electric power consumption of the electrolytic reaction, and the electric power consumption of the compressor is the electric power consumption generated in the process of absorbing low-pressure hydrogen from the electrolytic tank and injecting high-pressure hydrogen into the hydrogen storage tank for standby.
The hydrogen storage equipment can also be used for producing hydrogen by electrolyzing water by utilizing surplus electric energy in the low-valley period when electricity is used in the low-valley period, and can be stored or used by downstream industries; during peak electricity consumption, stored hydrogen energy can be discharged by the fuel cell.
The system also comprises a photovoltaic system, wind power, hydrogen energy and other generator sets, wherein the system monitors the operation conditions of the generator sets and gives early warning according to the operation conditions in time.
In the step S3, dynamic tracking is performed based on the energy type corresponding to the abnormal energy consumption, so as to confirm the abnormal node corresponding to the abnormal energy consumption, which specifically includes:
dividing a target harbor area into target subareas based on energy types corresponding to abnormal energy consumption, and confirming energy unit types corresponding to the target subareas;
Matching the energy unit type corresponding to each target sub-region with an energy consumption typical unit library, extracting key nodes of the energy unit type corresponding to each target sub-region, and further obtaining energy consumption monitoring values and energy consumption prediction intervals of each key node of each target sub-region in a preset time interval;
When the energy consumption monitoring value of the key node in the target subarea in the preset time interval is not in the energy consumption prediction interval corresponding to the key node in the target subarea, setting the key node as an abnormal node corresponding to abnormal energy consumption.
Specifically, the energy consumption prediction interval is obtained by correcting according to the corresponding energy consumption monitoring value.
In the step S4, performing an anomaly analysis on the anomaly node and outputting an analysis result, and performing an optimization control adjustment on the anomaly node based on the analysis result, which specifically includes:
Confirming an energy consumption monitoring value and an energy consumption prediction interval corresponding to the abnormal node, and further based on a calculation formula Calculating an energy consumption optimization coefficient beta corresponding to the abnormal node, wherein G' is expressed as an energy consumption monitoring value corresponding to the abnormal node, and Gmin and Gmax are respectively expressed as a minimum value and a maximum value of an energy consumption prediction interval corresponding to the abnormal node;
And controlling and adjusting the energy consumption value corresponding to the abnormal node based on the energy consumption optimization coefficient corresponding to the abnormal node.
Further, extracting each fuel device in the target harbor district, and monitoring the fuel condition of each fuel device in a preset time interval, and specifically further comprising:
Acquiring a first carbon emission amount emitted by each fuel oil device in a target harbor district when the fuel oil device operates within a preset time interval;
confirming the fuel consumption of each fuel device in a target harbor district within a preset time interval, and further confirming the second carbon emission corresponding to the fuel device based on the fuel consumption;
And based on the comparison of the first carbon emission amount and the second carbon emission amount with the carbon emission standard amount corresponding to the preset time interval, confirming the abnormal carbon emission condition corresponding to the target harbor district within the preset time interval.
Specifically, the system is in communication connection with the mobile terminal, so that real-time energy consumption data, data query, abnormal alarm and the like can be checked at the mobile terminal, and a user can conveniently manage energy consumption at any time and any place.
Example 2
The embodiment of the application also discloses an intelligent energy optimization control system for the port.
Referring to fig. 2, an intelligent energy optimization control system for a port, comprising:
The energy consumption acquisition module is used for acquiring each energy source of the target harbor district and acquiring the energy consumption condition corresponding to each energy source in a preset time interval, wherein the energy sources comprise water energy, electric energy, petroleum and natural gas;
the energy consumption comparison module is used for comparing the energy consumption condition corresponding to each energy source in the preset time interval of the target harbor district with the reference energy consumption corresponding to each energy source in the preset time interval, so as to confirm the abnormal energy source energy consumption;
The abnormal node confirming module is used for confirming the energy type corresponding to the abnormal energy consumption, dynamically tracking the abnormal energy consumption based on the energy type corresponding to the abnormal energy consumption and further confirming the abnormal node corresponding to the abnormal energy consumption;
And the optimization control module is used for carrying out abnormal analysis on the abnormal nodes and outputting analysis results, so as to carry out optimization control and adjustment on the abnormal nodes based on the analysis results.
Specifically, the system also comprises a panoramic visualization display module for displaying the information of the fields such as water, electricity, oil, gas, wind energy, photovoltaic, energy storage and the like in regions, and mainly comprises real-time energy consumption, energy consumption prediction, comprehensive energy consumption statistics, intelligent analysis, event early warning, carbon emission and the like, and is used for overview of the energy consumption conditions of ports.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar thereto, by those skilled in the art, without departing from the principles of the invention or beyond the scope of the appended claims.
Claims (9)
1. An intelligent energy optimization control method for a port is characterized by comprising the following steps:
collecting each energy source of a target harbor district, and obtaining the energy consumption condition corresponding to each energy source in a preset time interval, wherein the energy sources comprise water energy, electric energy, petroleum and natural gas;
Comparing the energy consumption condition of each energy source of the target harbor district in the preset time interval with the reference energy consumption corresponding to each energy source in the preset time interval, and further confirming the abnormal energy source energy consumption;
Confirming the energy type corresponding to the abnormal energy consumption, dynamically tracking based on the energy type corresponding to the abnormal energy consumption, and further confirming an abnormal node corresponding to the abnormal energy consumption;
and carrying out anomaly analysis on the anomaly node and outputting an analysis result, and further carrying out optimization control adjustment on the anomaly node based on the analysis result.
2. The intelligent energy optimization control method for ports according to claim 1, wherein: the obtaining the energy consumption condition corresponding to each energy source in the preset time interval specifically comprises the following steps:
confirming each water consumption node in the target harbor district, monitoring the water consumption condition of each water consumption node in a preset time interval, and further confirming the total water consumption in the preset time interval in the target harbor district;
Confirming each power utilization node in the target harbor district, monitoring the power utilization condition of each power utilization node in a preset time interval, and further confirming the power load in the preset time interval in the target harbor district;
Extracting each fuel oil device in the target harbor district, and monitoring the fuel oil condition of each fuel oil device in a preset time interval, so as to confirm the fuel oil consumption in the preset time interval in the target harbor district;
And confirming each natural gas node in the target harbor, monitoring the use condition of the natural gas in a preset time interval of each natural gas node, and further confirming the total consumption of the natural gas in the preset time interval of the target harbor.
3. The intelligent energy optimization control method for ports according to claim 2, wherein: the reference energy consumption corresponding to each energy source in the preset time interval specifically comprises the following steps:
extracting historical energy consumption corresponding to each energy source in a historical preset time interval from a cloud database, and carrying out correlation analysis on the historical energy consumption corresponding to each energy source and the historical preset time interval to acquire target correlation information corresponding to the historical energy consumption corresponding to each energy source and the historical preset time interval;
And inputting the target correlation information corresponding to the historical energy consumption and the historical preset time interval into the big data model for training, confirming the energy consumption prediction model corresponding to each energy source, and further extracting the reference energy consumption corresponding to each energy source in the preset time interval based on the energy consumption prediction model corresponding to each energy source.
4. A smart energy optimal control method for a port according to claim 3, wherein: the reference energy consumption corresponding to each energy source in the preset time interval specifically further comprises:
When the energy source is electric energy, a wind power generation model and a photovoltaic power generation model corresponding to the target harbor area are built;
The wind power generation model comprises:
Pt is the actual output power of the fan at the t moment, pr is the rated output power of the fan, vt is the wind speed of the altitude section where the impeller at the t moment is located, v0 is the wind cut-in speed of the fan, v1 is the rated wind speed of the fan, and v'0 is the wind cut-out speed of the fan;
The photovoltaic power generation model comprises: pi=min (ii×p, P), where Pi is the actual output power of the photovoltaic system corresponding to the i-th moment, ii is the horizontal surface radiance of the photovoltaic panel corresponding to the i-th moment, and P is the rated power of the photovoltaic system;
and confirming the actual generated energy corresponding to the preset time interval according to the wind power generation model and the photovoltaic power generation model corresponding to the target harbor district, and setting the actual generated energy corresponding to the preset time interval as the reference energy consumption corresponding to the electric energy in the preset time interval.
5. The intelligent energy optimization control method for ports according to claim 4, wherein: the method for dynamically tracking the energy type based on the abnormal energy consumption further confirms the abnormal node corresponding to the abnormal energy consumption, and specifically comprises the following steps:
dividing a target harbor area into target subareas based on energy types corresponding to abnormal energy consumption, and confirming energy unit types corresponding to the target subareas;
Matching the energy unit type corresponding to each target sub-region with an energy consumption typical unit library, extracting key nodes of the energy unit type corresponding to each target sub-region, and further obtaining energy consumption monitoring values and energy consumption prediction intervals of each key node of each target sub-region in a preset time interval;
When the energy consumption monitoring value of the key node in the target subarea in the preset time interval is not in the energy consumption prediction interval corresponding to the key node in the target subarea, setting the key node as an abnormal node corresponding to abnormal energy consumption.
6. The intelligent energy optimization control method for ports according to claim 5, wherein: the method for carrying out the abnormality analysis on the abnormal node and outputting the analysis result, and further carrying out the optimization control adjustment on the abnormal node based on the analysis result comprises the following steps:
Confirming an energy consumption monitoring value and an energy consumption prediction interval corresponding to the abnormal node, and further based on a calculation formula Calculating an energy consumption optimization coefficient beta corresponding to the abnormal node, wherein G' is expressed as an energy consumption monitoring value corresponding to the abnormal node, and Gmin and Gmax are respectively expressed as a minimum value and a maximum value of an energy consumption prediction interval corresponding to the abnormal node;
And controlling and adjusting the energy consumption value corresponding to the abnormal node based on the energy consumption optimization coefficient corresponding to the abnormal node.
7. The intelligent energy optimization control method for ports according to claim 6, wherein: extracting each fuel oil device in the target harbor area, monitoring the fuel oil condition of each fuel oil device in a preset time interval, and specifically further comprising:
Acquiring a first carbon emission amount emitted by each fuel oil device in a target harbor district when the fuel oil device operates within a preset time interval;
confirming the fuel consumption of each fuel device in a target harbor district within a preset time interval, and further confirming the second carbon emission corresponding to the fuel device based on the fuel consumption;
And based on the comparison of the first carbon emission amount and the second carbon emission amount with the carbon emission standard amount corresponding to the preset time interval, confirming the abnormal carbon emission condition corresponding to the target harbor district within the preset time interval.
8. An intelligent energy optimization control system for a port, comprising:
The energy consumption acquisition module is used for acquiring each energy source of the target harbor district and acquiring the energy consumption condition corresponding to each energy source in a preset time interval, wherein the energy sources comprise water energy, electric energy, petroleum and natural gas;
the energy consumption comparison module is used for comparing the energy consumption condition corresponding to each energy source in the preset time interval of the target harbor district with the reference energy consumption corresponding to each energy source in the preset time interval, so as to confirm the abnormal energy source energy consumption;
The abnormal node confirming module is used for confirming the energy type corresponding to the abnormal energy consumption, dynamically tracking the abnormal energy consumption based on the energy type corresponding to the abnormal energy consumption and further confirming the abnormal node corresponding to the abnormal energy consumption;
And the optimization control module is used for carrying out abnormal analysis on the abnormal nodes and outputting analysis results, so as to carry out optimization control and adjustment on the abnormal nodes based on the analysis results.
9. A computer-readable storage medium, characterized by: instructions stored which, when executed on a computer, cause the computer to perform an intelligent energy optimization control method for ports according to any one of claims 1 to 7.
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