CN115115473A - Diesel generating set carbon emission quantitative calculation method based on BP neural network - Google Patents

Diesel generating set carbon emission quantitative calculation method based on BP neural network Download PDF

Info

Publication number
CN115115473A
CN115115473A CN202210846393.2A CN202210846393A CN115115473A CN 115115473 A CN115115473 A CN 115115473A CN 202210846393 A CN202210846393 A CN 202210846393A CN 115115473 A CN115115473 A CN 115115473A
Authority
CN
China
Prior art keywords
diesel generator
carbon emission
generator set
diesel
oil
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.)
Granted
Application number
CN202210846393.2A
Other languages
Chinese (zh)
Other versions
CN115115473B (en
Inventor
刘盼盼
章锐
周吉
钱俊良
邰伟
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Dongbo Intelligent Energy Research Institute Co ltd
Liyang Research Institute of Southeast University
Original Assignee
Nanjing Dongbo Intelligent Energy Research Institute Co ltd
Liyang Research Institute of Southeast University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nanjing Dongbo Intelligent Energy Research Institute Co ltd, Liyang Research Institute of Southeast University filed Critical Nanjing Dongbo Intelligent Energy Research Institute Co ltd
Priority to CN202210846393.2A priority Critical patent/CN115115473B/en
Publication of CN115115473A publication Critical patent/CN115115473A/en
Application granted granted Critical
Publication of CN115115473B publication Critical patent/CN115115473B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • 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
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • 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
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Economics (AREA)
  • Software Systems (AREA)
  • Strategic Management (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • General Business, Economics & Management (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Evolutionary Computation (AREA)
  • Water Supply & Treatment (AREA)
  • Public Health (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Regulation And Control Of Combustion (AREA)
  • Control Of Eletrric Generators (AREA)

Abstract

The invention discloses a quantitative calculation method for carbon emission of a diesel generator set based on a BP (back propagation) neural network, which aims to quantitatively calculate the carbon emission generated by different diesel generators burning different quality oil for power generation based on the BP neural network, and combines an online acquisition technology to realize quantitative dynamic calculation of the carbon emission of the diesel generator set and improve the calculation efficiency and accuracy; the invention provides a quantitative calculation method for carbon emission of a diesel generating set based on a BP neural network, which overcomes the technical problems that the carbon emission of the diesel generating set is difficult to calculate effectively, particularly the on-line dynamic calculation; the method has the advantages of simple flow, reasonable calculation model structure, BP neural network training calculation superposition, comprehensive calculation data quantity, clear intermediate structure meaning in the calculation process, strong practicability, contribution to use and popularization of carbon emission control calculation of the diesel generating set, contribution to mastering carbon emission rules of different types of diesel generating sets and support of carbon emission reduction of the diesel generating set.

Description

Diesel generating set carbon emission quantitative calculation method based on BP neural network
Technical Field
The invention relates to the field of power systems, in particular to the technical field of carbon emission quantitative calculation of diesel generating sets.
Background
The diesel oil is an oil mineral substance obtained after refining petroleum, is a mixture of hydrocarbon, can generate carbon dioxide emission when the diesel oil is combusted by the diesel engine to generate power, and is not beneficial to realizing the national carbon emission reduction target due to different carbon emissions generated in the operation process of different types of diesel engine units.
The diesel generator set burns diesel through the engine, converts heat energy into mechanical energy, and then drives the generator to cut a magnetic field through the rotation of the engine, and finally generates electric energy. At present, the diesel generator set mainly has the following application scenes: firstly, self-contained power, when the regional power supply that does not have the electric wire netting, like regions such as marine island, remote mountain area, desert, need diesel generating set to generate electricity, supply with electric power. And secondly, the emergency power supply is mainly configured with a certain number of diesel generator sets as the emergency power supply aiming at the accident conditions of power grid failure, power failure and the like, so that the continuity of power supply is guaranteed. And thirdly, a power supply is replaced, the power supply of the diesel generator set is replaced by the power supply in a short time mainly in order to make up the situation that the power supply capacity of a power grid is insufficient or the power generation of new energy is insufficient and the power supply of the power grid cannot meet the requirements of users. And the diesel generator set is portable, flexible and easy to operate, and the diesel generator set and a vehicle form the mobile power supply, so that the mobile power supply can be used in oil fields, field engineering exploration, steamships and other scenes. And fifthly, the power supply for fire fighting is used as a power supply for the diesel generator set which is commonly used for fire fighting and is equipped for building fire fighting equipment, and when the power supply of a power grid is cut off in case of fire, the diesel generator set generates power to provide power for the fire fighting equipment. In summary, the application scenarios of the diesel generating set are wide, the carbon emission generated by the operation of the diesel generating set is huge, but the carbon emission of the diesel generating set is less concerned by the existing research.
Disclosure of Invention
The invention aims to solve the technical problems that the carbon emission of a diesel generating set is difficult to calculate effectively, and particularly the on-line dynamic calculation is difficult;
aiming at the problems and different scenes, in order to master the carbon emission of the diesel generator set and support the carbon emission reduction of the diesel generator set, the method for quantitatively calculating the carbon emission of the diesel generator set based on the BP neural network is provided, and mainly comprises the following steps: the method comprises the following steps of acquiring the electrical and oil information of the diesel generator set, calculating models of carbon emission of the diesel generator sets of different types, constructing a carbon emission factor model of the diesel generator set based on a BP neural network, constructing a power generation carbon emission online calculating model of the diesel generator set, and quantitatively calculating the carbon emission of the diesel generator sets of different types;
the invention provides the following technical scheme:
the diesel generating set carbon emission quantitative calculation method based on the BP neural network comprises the following steps:
s1, acquiring electric oil information data of all different types of diesel generators in a diesel generator set to form a basic database;
s2, constructing a carbon emission calculation model under active/reactive power of the diesel generator set, calculating carbon emission data of the diesel generator set by using the electric oil information data collected in S1, updating a basic database to form electric oil carbon information data, and dividing a training set and a test set;
s3, constructing a carbon emission factor model under active/reactive power of the diesel generating set based on the BP neural network, training and testing by using a corresponding training set and a corresponding testing set in S2 to obtain the carbon emission factor model under the active/reactive power of the diesel generating set, and giving out the corresponding carbon emission factor under the unit active/reactive power of the diesel generating set when different qualities of oil are used according to the model;
s4, constructing a diesel generating set power carbon emission online calculation model;
s5, collecting the power generation power data of the diesel generator set on line, and distinguishing active power and reactive power; and combining the corresponding carbon emission factor obtained in the step S3, and dynamically calculating the corresponding generated power carbon emission of the diesel generator set on line according to the carbon emission on-line calculation model.
Preferably, for the collection technology based on the sensing device in S1, the current mature sensing device and the internet of things technology are used to collect electric oil information data of the diesel generator set, where the electric oil information data includes active and reactive data of the diesel generator set, different types of quality oil data, and oil consumption data of the diesel generator set;
the active and reactive data of the diesel generating set in the electric oil information data are shown in a formula (1):
Figure 614171DEST_PATH_IMAGE001
（1）
in the formula:
Figure 600582DEST_PATH_IMAGE002
is composed of
Figure 782296DEST_PATH_IMAGE003
The active power vector of the diesel generator set at the moment,
Figure 676302DEST_PATH_IMAGE004
is composed of
Figure 243550DEST_PATH_IMAGE003
The time type is
Figure 655989DEST_PATH_IMAGE005
The active power generated by the diesel generator of (1),
Figure 308687DEST_PATH_IMAGE006
；
Figure 944068DEST_PATH_IMAGE007
，
Figure 178871DEST_PATH_IMAGE008
，
Figure 507084DEST_PATH_IMAGE009
respectively represents three types of diesel generators, namely a high-speed diesel generator, a medium-speed diesel generator and a low-speed diesel generator;
Figure 381499DEST_PATH_IMAGE010
is composed of
Figure 86150DEST_PATH_IMAGE003
At the moment, the reactive power vector of the diesel generator set,
Figure 877258DEST_PATH_IMAGE011
is composed of
Figure 376372DEST_PATH_IMAGE003
The time type is
Figure 800400DEST_PATH_IMAGE012
The reactive power generated by the diesel generating set,
Figure 528316DEST_PATH_IMAGE013
。
preferably, the processing is performed for different types of quality oil data in the electrical oil information data in S1 as shown in formula (2):
Figure 190241DEST_PATH_IMAGE014
（2）
in the formula:
Figure 860257DEST_PATH_IMAGE015
the data are the vector data of the quality oil,
Figure 974844DEST_PATH_IMAGE016
respectively of type 30#, and,20#, 10#, 5#, 0#, -5#, -10#, -21#, and 35 #;
according to the different types of quality oil data, the oil consumption data of the diesel generating set in the electric oil information data is shown as a formula (3):
Figure 4985DEST_PATH_IMAGE017
（3）
in the formula:
Figure 521417DEST_PATH_IMAGE018
is composed of
Figure 96755DEST_PATH_IMAGE019
The different types of diesel generating sets generate oil consumption data of different qualities of oil correspondingly consumed when the diesel generating sets are active,
Figure 698638DEST_PATH_IMAGE020
is composed of
Figure 33935DEST_PATH_IMAGE021
Oil consumption data of different qualities of oil correspondingly consumed when different types of diesel generator sets send out reactive power at any moment;
Figure 670453DEST_PATH_IMAGE022
is composed of
Figure 416692DEST_PATH_IMAGE023
Instant diesel generator set
Figure 240292DEST_PATH_IMAGE024
When active, quality oil is produced
Figure 877815DEST_PATH_IMAGE025
Oil consumption data of;
Figure 103260DEST_PATH_IMAGE026
is composed of
Figure 285980DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 862455DEST_PATH_IMAGE027
Quality oil when it is idle
Figure 539555DEST_PATH_IMAGE028
The number of fuel consumption.
Preferably, the calculation model of the carbon emission under active/reactive power for the diesel generator set constructed in S2 is as shown in formula (4):
Figure 619506DEST_PATH_IMAGE029
（4）
in the formula:
Figure 238706DEST_PATH_IMAGE030
is composed of
Figure 36898DEST_PATH_IMAGE031
Instant diesel generator set
Figure 768223DEST_PATH_IMAGE032
Burning of quality oil
Figure 764998DEST_PATH_IMAGE033
When the power is generated, the carbon emission generated by the diesel generator set is reduced;
Figure 555100DEST_PATH_IMAGE034
standard quality oil for combustion unit
Figure 325741DEST_PATH_IMAGE033
The amount of carbon emissions generated during the course of treatment,
Figure 859490DEST_PATH_IMAGE035
is a diesel generator set
Figure 914034DEST_PATH_IMAGE036
The active oil consumption factor represents the combustion efficiency of the quality oil of the diesel generating set；
Figure 609457DEST_PATH_IMAGE037
Is composed of
Figure 631509DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 703370DEST_PATH_IMAGE038
Produce active time quality oil
Figure 612420DEST_PATH_IMAGE039
The amount of oil consumption of;
Figure 744324DEST_PATH_IMAGE040
is composed of
Figure 489558DEST_PATH_IMAGE041
Diesel generator set at any moment
Figure 365110DEST_PATH_IMAGE038
Burning of quality oil
Figure 394246DEST_PATH_IMAGE042
Carbon emission generated by the diesel generator set when the engine generates idle work;
Figure 431472DEST_PATH_IMAGE043
is a diesel generator set
Figure 428115DEST_PATH_IMAGE044
The idle oil consumption factor represents the combustion efficiency of the quality oil of the diesel generator set;
Figure 841779DEST_PATH_IMAGE045
is composed of
Figure 725422DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 933549DEST_PATH_IMAGE032
When it is idleQuality oil
Figure 653374DEST_PATH_IMAGE039
The amount of oil consumption.
Preferably, the model for the carbon emission factor under the active/reactive power of the diesel generator set based on the BP neural network in the S3 comprises an input layer, a hidden layer and an output layer; the input layer has 4 inputs; the output layer has 2 outputs;
the 4 inputs are:
Figure 870729DEST_PATH_IMAGE046
、
Figure 608878DEST_PATH_IMAGE047
、
Figure 253486DEST_PATH_IMAGE048
and
Figure 959143DEST_PATH_IMAGE049
(ii) a Wherein
Figure 714609DEST_PATH_IMAGE050
Is composed of
Figure 572844DEST_PATH_IMAGE041
The time type is
Figure 388353DEST_PATH_IMAGE044
The active power generated by the diesel generating set,
Figure 817191DEST_PATH_IMAGE051
；
Figure 376349DEST_PATH_IMAGE047
is composed of
Figure 89090DEST_PATH_IMAGE019
The time type is
Figure 75500DEST_PATH_IMAGE038
The reactive power generated by the diesel generator set，
Figure 761609DEST_PATH_IMAGE052
；
Figure 858878DEST_PATH_IMAGE053
Is composed of
Figure 426125DEST_PATH_IMAGE031
Diesel generator set at any moment
Figure 786699DEST_PATH_IMAGE038
Produce active time quality oil
Figure 986868DEST_PATH_IMAGE039
The amount of oil consumption of the oil pump,
Figure 887828DEST_PATH_IMAGE054
is composed of
Figure 575161DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 903374DEST_PATH_IMAGE044
Quality oil for emitting idle power
Figure 292636DEST_PATH_IMAGE039
The amount of oil consumption of; the 2 outputs are:
Figure 731708DEST_PATH_IMAGE055
and
Figure 273547DEST_PATH_IMAGE056
wherein
Figure 772662DEST_PATH_IMAGE057
Is composed of
Figure 150685DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 393447DEST_PATH_IMAGE044
Burning of quality oil
Figure 55373DEST_PATH_IMAGE058
When the power is generated, the carbon emission generated by the diesel generator set is reduced;
Figure 725388DEST_PATH_IMAGE059
is composed of
Figure 823663DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 870117DEST_PATH_IMAGE038
Burning of quality oil
Figure 386549DEST_PATH_IMAGE058
Carbon emission generated by the diesel generator set when the engine generates idle work;
the hidden layer adopts a tansig function, and after training, the output carbon emission factor model of the BP neural network-based diesel generator set under active/reactive power is shown in a formula (5):
Figure 227466DEST_PATH_IMAGE060
（5）
in the formula:
Figure 314502DEST_PATH_IMAGE061
for the trained carbon emission factor model of the active BP neural network of the diesel generating set,
Figure 899067DEST_PATH_IMAGE062
the model is a carbon emission factor model of the idle BP neural network of the diesel generating set after training.
Preferably, the carbon emission online calculation model for the power generation power of the diesel generating set constructed in the step S4 is shown in the formula (6):
Figure 270005DEST_PATH_IMAGE063
（6）
in the formula:
Figure 281824DEST_PATH_IMAGE064
、
Figure 620270DEST_PATH_IMAGE065
are respectively as
Figure 742947DEST_PATH_IMAGE066
Active and reactive carbon emission of the diesel generator set on line at any moment;
Figure 233971DEST_PATH_IMAGE067
、
Figure 416690DEST_PATH_IMAGE068
are respectively as
Figure 478319DEST_PATH_IMAGE019
The active power and the reactive power of the diesel generator set are on line at any moment;
Figure 404686DEST_PATH_IMAGE069
is composed of
Figure 750217DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 103838DEST_PATH_IMAGE070
Burnup quality oil
Figure 145438DEST_PATH_IMAGE025
Active unit carbon emission factor of Internet;
Figure 875496DEST_PATH_IMAGE071
is composed of
Figure 75534DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 600056DEST_PATH_IMAGE072
Burnup quality oil
Figure 636276DEST_PATH_IMAGE028
And the reactive unit carbon emission factor of the internet.
The invention further provides a carbon emission quantitative calculation system of the diesel generating set based on the BP neural network; comprises a network interface, a memory and a processor; the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements;
the memory for storing a computer program operable on the processor;
the processor is used for executing the carbon emission quantitative calculation method of the diesel generating set based on the BP neural network when the computer program is run.
The invention further provides a computer storage medium, wherein the computer storage medium stores a program for quantitatively calculating the carbon emission of the diesel generating set based on the BP neural network, and the program for quantitatively calculating the carbon emission of the diesel generating set based on the BP neural network is executed by at least one processor to realize the method for quantitatively calculating the carbon emission of the diesel generating set based on the BP neural network;
it should be noted that the electrical and oil information acquisition technology of the present invention adopts the existing sensing device for acquisition, and the present invention is not specifically described nor limited herein;
compared with the prior art, the invention has the following beneficial effects:
the invention provides a quantitative calculation method for carbon emission of a diesel generating set based on a BP neural network, which overcomes the technical problems that the carbon emission of the diesel generating set is difficult to calculate effectively, particularly the on-line dynamic calculation; the method comprises the steps that specifically, through a diesel generator set electric oil information acquisition technology of a sensing device, electric oil information of all different types of diesel generators in a diesel generator set is acquired, and electric oil information data are divided into active power and reactive power; meanwhile, constructing a carbon emission calculation model under the active/reactive power of the diesel generator set; calculating carbon emission data of the diesel generating set by using the collected electric oil information data to form electric oil carbon information data, and dividing a training set and a test set; obtaining a carbon emission factor model under active/reactive power of the diesel generating set through training based on a BP neural network; giving out a carbon emission factor under active/reactive power of the diesel generator set; facilitating subsequent carbon emission calculations;
the method further constructs an on-line calculation model of the carbon emission of the generated power of the diesel generating set; the method has the advantages that the generated power data of the diesel generator set are collected on line, the corresponding carbon emission factors are combined, the carbon emission amount of the generated power corresponding to the diesel generator set is calculated dynamically on line, and the calculation efficiency and accuracy are improved greatly.
The invention distinguishes the generated power into active power and reactive power, avoids the interaction of the active power and the reactive power from influencing the carbon emission calculation, and improves the accuracy; the method has the advantages of simple flow, reasonable calculation model structure, BP neural network training calculation superposition, comprehensive calculation data quantity, clear intermediate structure meaning in the calculation process, strong practicability, contribution to use and popularization of carbon emission control calculation of the diesel generating set, contribution to mastering carbon emission rules of different types of diesel generating sets and support of carbon emission reduction of the diesel generating set.
Drawings
Fig. 1 is a schematic flow chart of a quantitative calculation method for carbon emission of a diesel generator set based on a BP neural network.
Fig. 2 is a schematic diagram of a BP neural network for calculating carbon emission factors of a diesel generating set in a quantitative calculation method for carbon emission of the diesel generating set based on the BP neural network.
Detailed Description
The technical solution of the present patent will be described in further detail with reference to the following embodiments.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention; the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance; furthermore, unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, as they may be fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
As shown in fig. 1, the method for quantitatively calculating carbon emission of a diesel generator set based on a BP neural network comprises the following steps:
s1, collecting electric oil information data of all different types of diesel generators in a diesel generator set to form a basic database;
in the electric oil acquisition technology of the diesel generator set in the embodiment, the currently mature sensing device and the internet of things technology are used for acquiring electric oil information data of the diesel generator set, wherein the electric oil information data comprise active data and reactive data of the diesel generator set, oil data of different types of quality and oil consumption data of the diesel generator set;
the active and reactive data processing of the diesel generating set in the electric oil information data is shown as a formula (1):
Figure 170026DEST_PATH_IMAGE073
（1）
in the formula:
Figure 224569DEST_PATH_IMAGE074
is composed of
Figure 919993DEST_PATH_IMAGE075
The active power vector of the diesel generator set at the moment,
Figure 942044DEST_PATH_IMAGE076
is composed of
Figure 13906DEST_PATH_IMAGE077
The time type is
Figure 922956DEST_PATH_IMAGE078
The active power generated by the diesel generator of (1),
Figure 54860DEST_PATH_IMAGE079
；
Figure 800093DEST_PATH_IMAGE007
，
Figure 675645DEST_PATH_IMAGE080
，
Figure 439202DEST_PATH_IMAGE081
respectively represents three types of diesel generators, namely a high-speed diesel generator, a medium-speed diesel generator and a low-speed diesel generator;
Figure 742007DEST_PATH_IMAGE010
is composed of
Figure 473072DEST_PATH_IMAGE082
At the moment, the reactive power vector of the diesel generator set,
Figure 886735DEST_PATH_IMAGE083
is composed of
Figure 770378DEST_PATH_IMAGE084
The time type is
Figure 244084DEST_PATH_IMAGE085
The reactive power generated by the diesel generating set,
Figure 963910DEST_PATH_IMAGE086
。
the carbon emissions generated when different types of quality oil are combusted are different, so the invention analyzes the carbon emissions of the different types of quality oil; the processing of the different types of quality oil data of the electrical oil information data in the embodiment is shown as formula (2):
Figure 181265DEST_PATH_IMAGE087
（2）
in the formula:
Figure 919414DEST_PATH_IMAGE088
the data are the vector data of the quality oil,
Figure 564021DEST_PATH_IMAGE089
respectively representing quality oils with types of 30#, 20#, 10#, 5#, 0#, -5#, -10#, -21#, and-35 #;
according to the different types of quality oil data, the oil consumption data of the diesel generating set in the electric oil information data is shown as a formula (3):
Figure 269678DEST_PATH_IMAGE090
（3）
in the formula:
Figure 25145DEST_PATH_IMAGE091
is composed of
Figure 883379DEST_PATH_IMAGE019
The different types of diesel generating sets generate oil consumption data of different qualities of oil correspondingly consumed when the diesel generating sets are active,
Figure 433309DEST_PATH_IMAGE092
is composed of
Figure 127727DEST_PATH_IMAGE021
Different types of diesel generators at different momentsThe oil consumption data of the oil with different qualities correspondingly consumed when the group sends out the idle work;
Figure 686884DEST_PATH_IMAGE093
is composed of
Figure 399625DEST_PATH_IMAGE023
Diesel generator set at any moment
Figure 386036DEST_PATH_IMAGE027
When active, quality oil is produced
Figure 806565DEST_PATH_IMAGE025
Oil consumption data of;
Figure 903834DEST_PATH_IMAGE026
is composed of
Figure 736661DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 893973DEST_PATH_IMAGE027
Quality oil when it is idle
Figure 297403DEST_PATH_IMAGE028
Oil consumption data of (c).
S2, constructing a carbon emission calculation model under active/reactive power of the diesel generator set, calculating carbon emission data of the diesel generator set by using the electric oil information data collected in S1, updating a basic database to form electric oil carbon information data, and dividing a training set and a test set;
the model for calculating the carbon emission under the active/reactive power of the diesel generator set constructed in the embodiment is shown as a formula (4):
Figure 198363DEST_PATH_IMAGE094
（4）
in the formula:
Figure 885696DEST_PATH_IMAGE030
is composed of
Figure 213910DEST_PATH_IMAGE031
Instant diesel generator set
Figure 603171DEST_PATH_IMAGE032
Burning of quality oil
Figure 42243DEST_PATH_IMAGE033
When the power is generated, the carbon emission generated by the diesel generator set is reduced;
Figure 584083DEST_PATH_IMAGE034
for combustion of unit standard quality oil
Figure 83197DEST_PATH_IMAGE033
The amount of carbon emissions generated during the course of treatment,
Figure 461220DEST_PATH_IMAGE035
is a diesel generator set
Figure 703983DEST_PATH_IMAGE036
The active oil consumption factor represents the combustion efficiency of the quality oil of the diesel generator set;
Figure 100329DEST_PATH_IMAGE095
is composed of
Figure 770345DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 134199DEST_PATH_IMAGE038
Produce active time quality oil
Figure 180652DEST_PATH_IMAGE039
The amount of oil consumption of;
Figure 431505DEST_PATH_IMAGE096
is composed of
Figure 272422DEST_PATH_IMAGE041
Diesel generator set at any moment
Figure 625037DEST_PATH_IMAGE038
Burning of quality oil
Figure 209602DEST_PATH_IMAGE042
Carbon emission generated by the diesel generator set when the engine generates idle work;
Figure 580541DEST_PATH_IMAGE043
is a diesel generator set
Figure 592359DEST_PATH_IMAGE044
The idle oil consumption factor represents the combustion efficiency of the quality oil of the diesel generator set;
Figure 930805DEST_PATH_IMAGE045
is composed of
Figure 53482DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 278927DEST_PATH_IMAGE032
Quality oil for emitting idle power
Figure 727226DEST_PATH_IMAGE039
The amount of oil consumption.
S3, constructing a carbon emission factor model under active/reactive power of the diesel generator set based on the BP neural network, training and testing by using a corresponding training and testing set in S2 to obtain the carbon emission factor model under the active/reactive power of the diesel generator set, and giving out corresponding carbon emission factors under unit active/reactive power of the diesel generator set under the condition of using different qualities of oil according to the model;
the application of the BP neural network has 3 main steps, which are respectively as follows: training data, a training network and a testing network are collected. The method adopts an off-line training neural network, namely, the neural network is trained according to active data and reactive data of different types of diesel generator sets and corresponding oil consumption data of different qualities of oil, connection weight values, threshold values, training functions and the like are obtained, and the active power, the reactive power and fuel carbon emission are associated;
as shown in fig. 2, the carbon emission factor model in the embodiment of the invention based on the BP neural network under the active/reactive power of the diesel generator set has an input layer, a hidden layer and an output layer; the input layer has 4 inputs; the output layer has 2 outputs;
the 4 inputs are:
Figure 788854DEST_PATH_IMAGE097
、
Figure 715222DEST_PATH_IMAGE098
、
Figure 60752DEST_PATH_IMAGE099
and
Figure 414373DEST_PATH_IMAGE100
(ii) a Wherein
Figure 479411DEST_PATH_IMAGE101
Is composed of
Figure 209469DEST_PATH_IMAGE041
The time type is
Figure 409506DEST_PATH_IMAGE044
The active power generated by the diesel generating set,
Figure 934029DEST_PATH_IMAGE051
；
Figure 970249DEST_PATH_IMAGE102
is composed of
Figure 238419DEST_PATH_IMAGE019
The time type is
Figure 292963DEST_PATH_IMAGE038
Diesel generatorThe reactive power emitted by the group is,
Figure 253966DEST_PATH_IMAGE052
；
Figure 276017DEST_PATH_IMAGE103
is composed of
Figure 82299DEST_PATH_IMAGE031
Diesel generator set at any moment
Figure 256929DEST_PATH_IMAGE038
Produce active time quality oil
Figure 123253DEST_PATH_IMAGE104
The amount of oil consumption of the oil pump,
Figure 134066DEST_PATH_IMAGE105
is composed of
Figure 744039DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 773175DEST_PATH_IMAGE044
Quality oil for emitting idle power
Figure 75980DEST_PATH_IMAGE058
The amount of oil consumption of; the 2 outputs are:
Figure 807045DEST_PATH_IMAGE106
and
Figure 220708DEST_PATH_IMAGE107
wherein
Figure 104351DEST_PATH_IMAGE106
Is composed of
Figure 578057DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 297883DEST_PATH_IMAGE044
Burning of quality oil
Figure 249658DEST_PATH_IMAGE058
When the power is generated, the carbon emission generated by the diesel generator set is reduced;
Figure 253386DEST_PATH_IMAGE108
is composed of
Figure 897994DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 603651DEST_PATH_IMAGE038
Burning of quality oil
Figure 359118DEST_PATH_IMAGE058
Carbon emission generated by the diesel generator set when the engine generates idle work;
the hidden layer adopts a tansig function, and after training, the output carbon emission factor model of the BP neural network-based diesel generator set under active/reactive power is shown as a formula (5):
Figure 951773DEST_PATH_IMAGE109
（5）
in the formula:
Figure 767282DEST_PATH_IMAGE110
for the trained carbon emission factor model of the active BP neural network of the diesel generating set,
Figure 461700DEST_PATH_IMAGE111
the model is a carbon emission factor model of the idle BP neural network of the diesel generating set after training. The factor model outputs the carbon emission corresponding to the unit internet power, which is a unit value; the carbon emission on the internet at the back is calculated according to the power amount on the internet.
S4, constructing a diesel generating set power carbon emission online calculation model;
the diesel generator set power generation carbon emission online calculation model constructed in the S4 is shown in the formula (6):
Figure 20857DEST_PATH_IMAGE112
（6）
in the formula:
Figure 733598DEST_PATH_IMAGE113
、
Figure 454430DEST_PATH_IMAGE114
respectively measuring the active and reactive carbon emissions of the diesel generator set on line at the moment t;
Figure 140538DEST_PATH_IMAGE115
、
Figure 237807DEST_PATH_IMAGE116
are respectively as
Figure 70634DEST_PATH_IMAGE019
The active power and the reactive power of the diesel generator set are on line at any moment;
Figure 962366DEST_PATH_IMAGE117
is composed of
Figure 631376DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 532336DEST_PATH_IMAGE118
Burnup quality oil
Figure 954090DEST_PATH_IMAGE025
Active unit carbon emission factor of Internet;
Figure 282303DEST_PATH_IMAGE119
is composed of
Figure 671565DEST_PATH_IMAGE019
Diesel generator set at any moment
Figure 376216DEST_PATH_IMAGE120
Burnup quality oil
Figure 918056DEST_PATH_IMAGE025
And the reactive unit carbon emission factor of the internet.
S5, collecting the power generation power data of the diesel generator set on line, wherein the electricity and oil information data of the diesel generator set are collected by the current mature sensing device and the Internet of things technology through the electricity and oil collection technology of the diesel generator set; distinguishing active power and reactive power by utilizing power generation power data in the electric oil information data; and combining the corresponding carbon emission factor obtained in the step S3, and dynamically calculating the corresponding generated power carbon emission of the diesel generator set on line according to the carbon emission on-line calculation model.
The working principle of the invention is as follows: the invention collects the electric oil information of all different types of diesel generators in the diesel generator set by the electric oil information collection technology of the diesel generator set of the sensing device, and divides the electric oil information data into active power and reactive power; avoiding the interaction of active power and reactive power from influencing subsequent calculation, and constructing a calculation model of carbon emission under the active/reactive power of the diesel generator set; calculating carbon emission data of the diesel generating set by using the collected electric oil information data to form electric oil carbon information data, and dividing a training set and a test set; obtaining a carbon emission factor model under active/reactive power of the diesel generator set through training based on a BP neural network; giving out a carbon emission factor under active/reactive power of the diesel generator set; further, constructing a carbon emission online calculation model of the power generation power of the diesel generator set; the method has the advantages that the generated power data of the diesel generator set are collected on line, the corresponding carbon emission factors are combined, the carbon emission amount of the generated power corresponding to the diesel generator set is calculated dynamically on line, and the calculation efficiency and accuracy are improved.
Although the preferred embodiments of the present patent have been described in detail, the present patent is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present patent within the knowledge of those skilled in the art.

Claims (8)

1. A diesel generating set carbon emission quantitative calculation method based on a BP neural network is characterized by comprising the following steps:
s1, acquiring electric oil information data of all different types of diesel generators in a diesel generator set to form a basic database;
s2, constructing a carbon emission calculation model under active/reactive power of the diesel generator set, calculating carbon emission data of the diesel generator set by using the electric oil information data collected in S1, updating a basic database to form electric oil carbon information data, and dividing a training set and a test set;
s3, constructing a carbon emission factor model under active/reactive power of the diesel generating set based on the BP neural network, training and testing by using a corresponding training set and a corresponding testing set in S2 to obtain the carbon emission factor model under the active/reactive power of the diesel generating set, and giving out the corresponding carbon emission factor under the unit active/reactive power of the diesel generating set when different qualities of oil are used according to the model;
s4, constructing a diesel generating set generating power carbon emission online calculation model;
s5, collecting the power generation power data of the diesel generator set on line, and distinguishing active power and reactive power; and combining the corresponding carbon emission factor obtained in the step S3, and dynamically calculating the corresponding generated power carbon emission of the diesel generator set on line according to the carbon emission on-line calculation model.
2. The BP neural network-based quantitative carbon emission calculation method for a diesel generator set according to claim 1, wherein in step S1, a collection technology based on a sensing device is used to collect electrical and oil information data of all different types of diesel generators in the diesel generator set;
the active and reactive data of the diesel generator set in the electric oil information data are shown as a formula (1):
Figure 562786DEST_PATH_IMAGE001
（1）
in the formula:
Figure 57090DEST_PATH_IMAGE002
is composed of
Figure 367986DEST_PATH_IMAGE003
The active power vector of the diesel generator set at the moment,
Figure 294354DEST_PATH_IMAGE004
is composed of
Figure 639884DEST_PATH_IMAGE003
The time type is
Figure 744238DEST_PATH_IMAGE005
The active power generated by the diesel generator of (1),
Figure 542430DEST_PATH_IMAGE006
；
Figure 272488DEST_PATH_IMAGE007
，
Figure 472525DEST_PATH_IMAGE008
，
Figure 246315DEST_PATH_IMAGE009
respectively represents three types of diesel generators, namely a high-speed diesel generator, a medium-speed diesel generator and a low-speed diesel generator;
Figure 266224DEST_PATH_IMAGE010
is composed of
Figure 534394DEST_PATH_IMAGE003
At the moment, the reactive power vector of the diesel generator set,
Figure 588938DEST_PATH_IMAGE011
is composed of
Figure 300673DEST_PATH_IMAGE003
The time type is
Figure 73457DEST_PATH_IMAGE012
The diesel generator set generates reactive power.
3. The BP neural network-based diesel generator set carbon emission quantitative calculation method according to claim 2,
the different types of quality oil data in the electric oil information data are shown in formula (2):
Figure 879739DEST_PATH_IMAGE013
（2）
in the formula:
Figure 54368DEST_PATH_IMAGE014
the data are the vector data of the quality oil,
Figure 966698DEST_PATH_IMAGE015
respectively representing quality oils with types of 30#, 20#, 10#, 5#, 0#, -5#, -10#, -21#, and-35 #;
according to the different types of quality oil data, the oil consumption data of the diesel generating set in the electric oil information data is shown as a formula (3):
Figure 430041DEST_PATH_IMAGE016
（3）
in the formula:
Figure 836751DEST_PATH_IMAGE017
is composed of
Figure 616620DEST_PATH_IMAGE003
At no timeThe diesel generating sets of the same type correspondingly consume oil consumption data of different qualities of oil when generating active power,
Figure 919425DEST_PATH_IMAGE018
is composed of
Figure 401222DEST_PATH_IMAGE003
Oil consumption data of different qualities of oil correspondingly consumed when different types of diesel generator sets send out reactive power at any moment;
Figure 814886DEST_PATH_IMAGE019
is composed of
Figure 965374DEST_PATH_IMAGE003
Diesel generator set at any moment
Figure 439080DEST_PATH_IMAGE020
When active, quality oil is produced
Figure 408173DEST_PATH_IMAGE021
Oil consumption data of;
Figure 359949DEST_PATH_IMAGE022
is composed of
Figure 114409DEST_PATH_IMAGE003
Diesel generator set at any moment
Figure 759017DEST_PATH_IMAGE020
Quality oil when it is idle
Figure 215406DEST_PATH_IMAGE023
The number of fuel consumption.
4. The method for quantitatively calculating the carbon emission of the diesel generator set based on the BP neural network according to claim 3, wherein in the step S2, the model for calculating the carbon emission of the diesel generator set under the active/reactive power is constructed as shown in formula (4):
Figure 970873DEST_PATH_IMAGE024
（4）
in the formula:
Figure 812796DEST_PATH_IMAGE025
is composed of
Figure 628305DEST_PATH_IMAGE026
Diesel generator set at any moment
Figure 306411DEST_PATH_IMAGE027
Burning of quality oil
Figure 865568DEST_PATH_IMAGE028
When the power is generated, the carbon emission generated by the diesel generator set is reduced;
Figure 329042DEST_PATH_IMAGE029
for combustion of unit standard quality oil
Figure 49873DEST_PATH_IMAGE028
The amount of carbon emissions generated during the course of treatment,
Figure 480855DEST_PATH_IMAGE030
is a diesel generator set
Figure 578124DEST_PATH_IMAGE027
The active oil consumption factor represents the combustion efficiency of the quality oil of the diesel generating set;
Figure 660218DEST_PATH_IMAGE031
is composed of
Figure 551951DEST_PATH_IMAGE026
Diesel generator set at any moment
Figure 470228DEST_PATH_IMAGE032
Produce active time quality oil
Figure 371188DEST_PATH_IMAGE033
The amount of oil consumption of;
Figure 543674DEST_PATH_IMAGE034
is composed of
Figure 871888DEST_PATH_IMAGE026
Instant diesel generator set
Figure 11882DEST_PATH_IMAGE035
Burning of quality oil
Figure 716533DEST_PATH_IMAGE036
Carbon emission generated by the diesel generator set when the engine generates idle work;
Figure 304378DEST_PATH_IMAGE037
is a diesel generator set
Figure 803492DEST_PATH_IMAGE035
The idle oil consumption factor represents the combustion efficiency of the quality oil of the diesel generator set;
Figure 430783DEST_PATH_IMAGE038
is composed of
Figure 424278DEST_PATH_IMAGE003
Diesel generator set at any moment
Figure 820624DEST_PATH_IMAGE039
Quality oil for emitting idle power
Figure 490640DEST_PATH_IMAGE040
The amount of oil consumption.
5. The BP neural network-based quantitative calculation method for carbon emission of a diesel generator set according to claim 4, wherein in step S3, the BP neural network-based model for carbon emission factor under active/reactive power of the diesel generator set has an input layer, a hidden layer and an output layer; the input layer has 4 inputs; the output layer has 2 outputs;
the 4 inputs are:
Figure 605226DEST_PATH_IMAGE041
、
Figure 641227DEST_PATH_IMAGE042
、
Figure 157659DEST_PATH_IMAGE043
and
Figure 998576DEST_PATH_IMAGE044
(ii) a Wherein
Figure 600459DEST_PATH_IMAGE045
Is composed of
Figure 935756DEST_PATH_IMAGE003
The time type is
Figure 306695DEST_PATH_IMAGE039
The active power generated by the diesel generator set;
Figure 52934DEST_PATH_IMAGE046
is composed of
Figure 876534DEST_PATH_IMAGE003
The time type is
Figure 514057DEST_PATH_IMAGE039
The diesel generating set generates reactive power;
Figure 739502DEST_PATH_IMAGE047
is composed of
Figure 922222DEST_PATH_IMAGE003
Diesel generator set at any moment
Figure 498697DEST_PATH_IMAGE039
Produce active time quality oil
Figure 175797DEST_PATH_IMAGE040
The amount of oil consumption of the oil pump,
Figure 255748DEST_PATH_IMAGE048
is composed of
Figure 874948DEST_PATH_IMAGE003
Diesel generator set at any moment
Figure 673140DEST_PATH_IMAGE049
Quality oil for emitting idle power
Figure 449204DEST_PATH_IMAGE040
The amount of oil consumption of; the 2 outputs are:
Figure 383662DEST_PATH_IMAGE050
and
Figure 173763DEST_PATH_IMAGE051
wherein
Figure 209984DEST_PATH_IMAGE052
Is composed of
Figure 478154DEST_PATH_IMAGE003
Diesel generator set at any moment
Figure 532698DEST_PATH_IMAGE039
Burning of quality oil
Figure 493700DEST_PATH_IMAGE040
When the power is generated, the carbon emission generated by the diesel generator set is reduced;
Figure 250173DEST_PATH_IMAGE053
is composed of
Figure 322034DEST_PATH_IMAGE003
Diesel generator set at any moment
Figure 496663DEST_PATH_IMAGE054
Burning of quality oil
Figure 362988DEST_PATH_IMAGE040
Carbon emission generated by the diesel generator set when the engine generates idle work;
the hidden layer adopts a tansig function, and after training, the output carbon emission factor model of the BP neural network-based diesel generator set under active/reactive power is shown in a formula (5):
Figure 373801DEST_PATH_IMAGE055
（5）
in the formula:
Figure 983773DEST_PATH_IMAGE056
for the trained carbon emission factor model of the active BP neural network of the diesel generating set,
Figure 12909DEST_PATH_IMAGE057
the model is a trained carbon emission factor model of the idle BP neural network of the diesel generating set.
6. The method for quantitatively calculating the carbon emission of the diesel generator set based on the BP neural network as claimed in claim 5, wherein in the step S4, an on-line calculation model of the carbon emission of the generated power of the diesel generator set is constructed as shown in formula (6):
Figure 784556DEST_PATH_IMAGE058
（6）
in the formula:
Figure 775341DEST_PATH_IMAGE059
、
Figure 189004DEST_PATH_IMAGE060
are respectively as
Figure 72647DEST_PATH_IMAGE003
Active and reactive carbon emission of the diesel generator set on line at any moment;
Figure 546353DEST_PATH_IMAGE061
、
Figure 266179DEST_PATH_IMAGE062
are respectively as
Figure 217954DEST_PATH_IMAGE003
The active power and the reactive power of the diesel generator set are on line at any moment;
Figure 221682DEST_PATH_IMAGE063
is composed of
Figure 600711DEST_PATH_IMAGE003
Diesel generator set at any moment
Figure 306368DEST_PATH_IMAGE035
Combustion quality oil
Figure 61834DEST_PATH_IMAGE036
Active unit carbon emission factor of Internet;
Figure 716807DEST_PATH_IMAGE064
is composed of
Figure 735578DEST_PATH_IMAGE003
Diesel generator set at any moment
Figure 226734DEST_PATH_IMAGE035
Combustion quality oil
Figure 520312DEST_PATH_IMAGE036
And the reactive unit carbon emission factor of the internet.
7. The carbon emission quantitative calculation system of the diesel generating set based on the BP neural network is characterized by comprising a network interface, a memory and a processor; wherein the content of the first and second substances,
the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements;
the memory for storing a computer program operable on the processor;
the processor is used for executing the method for quantitatively calculating the carbon emission of the diesel generator set based on the BP neural network according to any one of claims 2-6 when the computer program is run.
8. A computer storage medium, wherein the computer storage medium stores a program for BP neural network-based quantitative calculation of carbon emission of a diesel genset, and the program for BP neural network-based quantitative calculation of carbon emission of a diesel genset is executed by at least one processor to implement the BP neural network-based quantitative calculation method of carbon emission of a diesel genset according to any one of claims 2 to 6.
CN202210846393.2A 2022-07-19 2022-07-19 BP neural network-based carbon emission quantitative calculation method for diesel generator set Active CN115115473B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210846393.2A CN115115473B (en) 2022-07-19 2022-07-19 BP neural network-based carbon emission quantitative calculation method for diesel generator set

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210846393.2A CN115115473B (en) 2022-07-19 2022-07-19 BP neural network-based carbon emission quantitative calculation method for diesel generator set

Publications (2)

Publication Number Publication Date
CN115115473A true CN115115473A (en) 2022-09-27
CN115115473B CN115115473B (en) 2023-05-12

Family

ID=83331742

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210846393.2A Active CN115115473B (en) 2022-07-19 2022-07-19 BP neural network-based carbon emission quantitative calculation method for diesel generator set

Country Status (1)

Country Link
CN (1) CN115115473B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104362681A (en) * 2014-11-18 2015-02-18 湖北省电力勘测设计院 Island micro-grid capacity optimal-configuration method considering randomness
CN105975799A (en) * 2016-06-01 2016-09-28 广东电网有限责任公司电力科学研究院 Method and system for calculating carbon emissions
CN113887827A (en) * 2021-10-25 2022-01-04 国网安徽省电力有限公司电力科学研究院 Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit
CN113886752A (en) * 2021-09-10 2022-01-04 远景智能国际私人投资有限公司 Method, device, terminal and storage medium for calculating carbon emission intensity
CN113935125A (en) * 2021-09-09 2022-01-14 西华大学 BP neural network prediction model optimization method for diesel engine emission performance
CN114202127A (en) * 2021-12-18 2022-03-18 东南大学 Electric vehicle load optimization method based on graph volume and deep confidence network
CN114417571A (en) * 2021-12-24 2022-04-29 国网湖北省电力有限公司电力科学研究院 Method and system for realizing on-line monitoring of carbon emission of coal-fired generator set and storage medium
CN114742294A (en) * 2022-04-09 2022-07-12 广州网文三维数字技术有限公司 Neural network algorithm for carbon emission prediction

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104362681A (en) * 2014-11-18 2015-02-18 湖北省电力勘测设计院 Island micro-grid capacity optimal-configuration method considering randomness
CN105975799A (en) * 2016-06-01 2016-09-28 广东电网有限责任公司电力科学研究院 Method and system for calculating carbon emissions
CN113935125A (en) * 2021-09-09 2022-01-14 西华大学 BP neural network prediction model optimization method for diesel engine emission performance
CN113886752A (en) * 2021-09-10 2022-01-04 远景智能国际私人投资有限公司 Method, device, terminal and storage medium for calculating carbon emission intensity
CN113887827A (en) * 2021-10-25 2022-01-04 国网安徽省电力有限公司电力科学研究院 Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit
CN114202127A (en) * 2021-12-18 2022-03-18 东南大学 Electric vehicle load optimization method based on graph volume and deep confidence network
CN114417571A (en) * 2021-12-24 2022-04-29 国网湖北省电力有限公司电力科学研究院 Method and system for realizing on-line monitoring of carbon emission of coal-fired generator set and storage medium
CN114742294A (en) * 2022-04-09 2022-07-12 广州网文三维数字技术有限公司 Neural network algorithm for carbon emission prediction

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
郜新军: "《城市轨道交通***碳排放评估及集成优化控制方法研究》", 《工程科技Ⅰ辑》 *

Also Published As

Publication number Publication date
CN115115473B (en) 2023-05-12

Similar Documents

Publication Publication Date Title
Kenanoğlu et al. Performance & emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches
Yang et al. Machine learning prediction of specific capacitance in biomass derived carbon materials: Effects of activation and biochar characteristics
Laudan Cognitive change in technology and science
TW200925015A (en) Mixed type vehicle power system and method of forming multidimentional data of fuel consumption
CN105071389A (en) Hybrid AC/DC microgrid optimization operation method and device considering source-grid-load interaction
Solmaz et al. Comparison of artificial neural network and fuzzy logic approaches for the prediction of in-cylinder pressure in a spark ignition engine
CN112347694A (en) Sea island micro-grid power supply planning method containing ocean current-offshore wind power-tidal current power generation
CN116482975A (en) Virtual-real fusion ship energy management strategy verification method and platform
CN115115473A (en) Diesel generating set carbon emission quantitative calculation method based on BP neural network
Gu et al. Energy management strategy considering fuel economy and life of fuel cell for fuel cell electric vehicles
CN106451570A (en) Method and device for dynamically distributing load of diesel power generation units of ships
CN106056168B (en) The determination method of gas-steam combined circulating generation unit operating condition optimal value
CN112072651A (en) Transient voltage instability and power angle instability identification method based on transient energy function
CN115099522B (en) Active and reactive carbon emission prediction method for special transformer user based on BP neural network
CN116050858A (en) Comprehensive energy service success evaluation method based on normal cloud model and application thereof
Horcasitas-Verdiguel et al. Design and manufacture of ice test module to reduce gasoline consumption using oxyhydrogen gas from an alkaline electrolyzer
Guo et al. Compressed air energy storage and future development
Prakash et al. Multi-objective optimal economic dispatch of a fuel cell and combined heat and power based renewable integrated grid tied micro-grid using whale optimization algorithm
CN113949098A (en) Island type hybrid micro-grid system reliability evaluation method considering FCV
CN112677752A (en) New energy automobile electricity molecule fission driving system based on artificial intelligence
Tian et al. Comprehensive cost-benefit assessment of offshore wind power based on improved VIKOR method
Wang et al. Operation effect evaluation of grid side energy storage power station based on combined weight TOPSIS model
CN117312293B (en) Electric power multisource heterogeneous data management and intelligent analysis method and system
Yongqi et al. Comprehensive benefit analysis for integrated energy systems projects based on ahp-fuzzy
Khan et al. Facts and popular perceptions on saving energy and the environment

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
GR01 Patent grant
GR01 Patent grant