CN106779137A - A kind of method that ship oil consumption is predicted according to sea situation and operating condition - Google Patents
A kind of method that ship oil consumption is predicted according to sea situation and operating condition Download PDFInfo
- Publication number
- CN106779137A CN106779137A CN201611001429.8A CN201611001429A CN106779137A CN 106779137 A CN106779137 A CN 106779137A CN 201611001429 A CN201611001429 A CN 201611001429A CN 106779137 A CN106779137 A CN 106779137A
- Authority
- CN
- China
- Prior art keywords
- oil consumption
- data
- ship
- lasso
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000012417 linear regression Methods 0.000 claims abstract description 6
- 238000012216 screening Methods 0.000 claims abstract description 5
- 238000010845 search algorithm Methods 0.000 claims abstract description 5
- 238000002790 cross-validation Methods 0.000 claims abstract description 4
- 238000012549 training Methods 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 2
- 238000006467 substitution reaction Methods 0.000 claims description 2
- 239000003921 oil Substances 0.000 abstract description 53
- 230000008602 contraction Effects 0.000 abstract 1
- 230000010354 integration Effects 0.000 abstract 1
- 239000010762 marine fuel oil Substances 0.000 abstract 1
- 239000000446 fuel Substances 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000010801 machine learning Methods 0.000 description 4
- 230000001186 cumulative effect Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 239000003651 drinking water Substances 0.000 description 2
- 235000020188 drinking water Nutrition 0.000 description 2
- 238000000611 regression analysis Methods 0.000 description 2
- 238000007634 remodeling Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000012888 cubic function Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013506 data mapping Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000295 fuel oil Substances 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000001568 sexual effect Effects 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of method for predicting ship oil consumption according to sea situation and operating condition, the method of the prediction ship oil consumption obtains sample data set by data screening, data integration and normalized first, then set up Multiple Linear Regression Forecasting Models of Chinese, based on sample data set, one cost function is defined using minimum of contraction and selection opertor (LASSO algorithms), and return (LARS) algorithm to carry out variable and shrink to be selected with parameter with reference to cross validation and minimum angular convolution, last Osborne Conjugate Search Algorithms solve LASSO and estimate, are calculated ship oil consumption.A kind of method that ship oil consumption is predicted according to sea situation and operating condition proposed by the present invention, the functional relation that can be set up between marine fuel oil consumption and each influence factor, solve the Problems of Multiple Synteny during oil consumption is calculated, ship oil consumption computational accuracy is improved, is had great importance to maritime traffic transport energy-saving and emission-reduction.
Description
Technical field
The invention belongs to ship shipping field, and in particular to a kind of side that ship oil consumption is predicted according to sea situation and operating condition
Method.
Background technology
Fuel consumption during ship's navigation is subject to meteorological sea conditions, ship navigation state and operating condition etc. many
The influence of uncertain factor is planted, and oil consumption prediction is manipulated to ship speed, course etc. and has important directive significance.Accurate oil
Consumption calculating can preferably instruct crewman to manipulate ship's navigation, it is ensured that want in seeking time with low oil consumption, low emission, efficient at sailing date
The manipulation schemes navigation of benefit.However, the high correlation between the uncertainty and some factors of influence factor causes oil consumption
Cannot accurately determine with the functional relation between each influence factor, also cannot oil consumption of the Ship ' between the voyage schedule exactly.
Statistical machine learning is the distribution based on a large amount of historical datas, and model hypothesis are set up with reference to statistical theory, is passed through
Algorithm for design process estimates parameter from data and model, and constantly study is deposited so as to improved model performance between Automatic-searching data
Rule, be calculated a functional relation approximate to natural model, and unknown data is entered using gained functional relation
Row prediction, the generalization ability of test model simultaneously verifies a kind of research method of model hypothesis
Regression analysis is one of method for commonly using in data mining, and it reflects the characteristic of data attribute value, and by letter
The relation of number form formula expression data mapping, so that the dependence between finding property value.Regression analysis are widely used
In the prediction and the research of dependency relation to data sequence.
LASSO algorithms are that one kind can realize that index set is simplified in machine learning algorithm, and can be processed with multiple common
The Biased estimator method of linear behavio(u)r data.The algorithm in loss function by introducing L1Penalty, in regression coefficient
Under absolute value sum is less than a constraints for constant, minimize residual sum of squares (RSS), so that the recurrence system of some indexs
Number is exactly equal to zero, that is to say, that LASSO algorithms have the sparse characteristic of variable, can eliminate redundancy feature, and pinpointing the problems needs
The variable and only to need, obtains the model for having stronger interpretability to result while realizing that index set is simplified.
LARS algorithms are used to determine which variable is selected into model and provides the parameter Estimation of relevant variable.The algorithm is not
Certain variable is directly added into each step forward stepwire regression, but is first found out and that change of dependent variable degree of correlation highest
Amount, the direction then along least squares error is adjusted to the coefficient of variable, is in course of adjustment, the variable and residual error
Coefficient correlation can be gradually reduced until new variable occur to be more than the now variable with the correlation of residual error related with residual error
Property, then it is adjusted along the direction of least squares error again, repeat the process, until all variables are selected into certainly
In variables collection, required parameter Estimation is finally given.
The content of the invention
The invention provides a kind of method for predicting ship oil consumption according to sea situation and operating condition, ship's navigation is can solve the problem that
During oil consumption computational problem, consider the factors such as the influence meteorology of ship's navigation, sea situation, operational configuration and maneuvering performance,
By building statistical model and carrying out parameter learning and model training to model with reference to machine learning algorithm, final training obtains one
Individual oil consumption forecast model is used to solve quick, the Accurate Prediction of ship oil consumption under the influence of many factors.
A kind of method for predicting ship oil consumption according to sea situation and operating condition that the present invention is provided, primary object is to be based on
The ship oil consumption forecast model framework of LASSO algorithms.
Ship oil consumption forecast model framework based on LASSO algorithms is based on ship history aeronautical data and weather sea situation number
According to initially setting up Linear Regression Forecasting Model and propose it is assumed that then defining a cost function using LASSO algorithms, and turn
It is changed to solution L1The convex quadratic programming problem of the lower square error minimization of loss of constraint, with reference to LARS Algorithm for Solving LASSO problems
Sparse solution, realize coefficients model and variables choice, finally training obtaining ship oil consumption forecast model.
In order to realize the above object the invention mainly includes steps：
Step (1) raw data acquisition.Mainly using different equipment and approach collection weather sea state data, ship's navigation
Status data and Ship Controling data.
Step (2) data prediction.Initial data for collection in (1) carries out data screening, to data de-noising, cuts
With the treatment such as synchronous, then the integrated data from multiple data sources remove redundancy simultaneously, and finally integrated data are united
One normalized, obtains model training data set.
Step (3) sets up oil consumption forecast model.According to the characteristic distributions of training dataset and empirical hypothesis in step (2)
Meet linear relationship between input feature vector variable and corresponding prediction output variable, set up Multiple Linear Regression Forecasting Models of Chinese.
Step (4) parameter learning and model training.It is fixed using LASSO algorithms based on the training data obtained in step (2)
An adopted cost function, and be converted to solution L1The convex quadratic programming problem of the lower square error minimization of loss of constraint, most terminates
Close cross validation to learn model parameter with LARS algorithms, and LASSO is solved using Osborne Conjugate Search Algorithms and estimate.Most
In the oil consumption forecast model that will be set up in LASSO estimates substitution step (3) afterwards, the oil consumption forecast model after being trained.
Step (5) model application.By what is obtained in unknown meteorological sea state data and Ship Controling data input step (5)
In oil consumption forecast model, final oil consumption can be exported and predicted the outcome.
It is of the invention compared with existing artificial neural network (BP-ANN), support vector regression (SVR) forecast model, have
Following characteristics：
1. the present invention can realize that variable is sparse, solve Problems of Multiple Synteny and over-fitting the problems such as.
The present invention mainly defines a cost function using LASSO algorithm combinations least square method, based on training data,
With reference to cross validation and LARS Algorithm for Solving sparse solutions, realize that parameter is shunk and selected, so as to realize being solved while variables choice
Determine Problems of Multiple Synteny.Additionally, LASSO algorithms introduce L in object function1Regularization term, by regularization coefficient
Proportion between balance regularization term and square error term, prevent to some extent the situation of model over-fitting.
2. the present invention has stronger generalization ability and result interpretability.
The present invention has considered the possible factor of various influence ship oil consumption predictions, can in unknown test data set
Obtain accurate oil consumption to predict the outcome, and preferably the oil consumption that causes of predicted impact factor mutation can be mutated, with compared with
Strong generalization ability and the interpretability to predicting the outcome..
Brief description of the drawings
Fig. 1 is a kind of method composition frame chart that ship oil consumption is predicted according to sea situation and operating condition in the present invention；
Fig. 2 predicts the outcome comparison diagram for LASSO, BP-ANN and SVR forecast model；
Fig. 3 is the cumulative point comparison diagram that LASSO, BP-ANN and SVR forecast model predict the outcome；
Fig. 4 is the mean absolute error value that LASSO, BP-ANN and SVR forecast model predict the outcome.
Specific embodiment
The a kind of of present invention offer is provided in detail below in conjunction with the accompanying drawings ship oil consumption is predicted according to sea situation and operating condition
Method.Fig. 1 is a kind of method composition frame chart that ship oil consumption is predicted according to sea situation and operating condition.
Such as Fig. 1, the structure of model of the present invention is mainly including five steps：Step (1) raw data acquisition.It is main to use not
Same equipment and approach collection weather sea state data, ship navigation state data and Ship Controling data；Step (2) data are located in advance
Reason.Initial data for collection in (1) carries out data screening, to data de-noising, cuts and the treatment such as synchronous, then it is integrated come
Redundancy is removed simultaneously from the data of multiple data sources, unified normalized finally is carried out to integrated data, obtain model
Training dataset；Step (3) sets up oil consumption forecast model.According to the characteristic distributions and experience of training dataset in step (2)
Assuming that meeting linear relationship between input feature vector variable and corresponding prediction output variable, Multiple Linear Regression Forecasting Models of Chinese is set up；
Step (4) parameter learning and model training.Based on the training data obtained in step (2), in a generation, is defined using LASSO algorithms
Valency function, and be converted to solution L1The convex quadratic programming problem of the lower square error minimization of loss of constraint, it is final to be tested with reference to intersection
Card learns with LARS algorithms to model parameter, and solves LASSO estimations using Osborne Conjugate Search Algorithms.Finally by LASSO
Estimate is substituted into the oil consumption forecast model set up in step (3), the oil consumption forecast model after being trained；Step (5) model
Using.In the oil consumption forecast model that will be obtained in unknown meteorological sea state data and Ship Controling data input step (4), can be with
The final oil consumption of output predicts the outcome.
Step one：Raw data acquisition
Initial data mainly includes meteorological sea state data, ship's navigation historical data and Ship Controling data.Meteorological data
Essentially from the Weather Forecast Information of weather bureau's issue, sea state data some sensors are measured indirectly, ship's navigation historical data
Pass through high speed, the acquisition of high-precision sensing device assembly aboard ship of log noon report and installation with data are manipulated, finally will
Data are sent to database server and are stored and analyzed.In order to carry out ship oil consumption prediction, it is necessary to the signal of collection includes
Oil consumption, fuel density, fuel oil temperature, Angle of Trim, Angle of Heel, the side of a ship high, pitch, rudder angle, true course, yaw angle, drinking water, wind angle,
Wind speed, speed through water, ground speed, longitude, latitude, wherein oil consumption as output respond, its dependent variable as model input
Variable.
Step 2：Data prediction
Data prediction is the premise for ensureing high-quality model prediction result.In the magnanimity initial data for gathering in practice
A large amount of imperfect, inconsistent, repetitions, high-dimensional and Noise data are generally there are, machine learning algorithm has been had a strong impact on
Execution efficiency and model complexity.Data screening, data set are passed through to the initial data gathered in step one in the present invention
Sample data set is obtained into normalized.
Step 3：Set up oil consumption forecast model
Because ship oil consumption is that have the coefficient result of many factors, it is assumed that meet line between oil consumption and each influence factor
Sexual intercourse, therefore forecast model is set up using multiple linear regression, oil consumption forecast model may be used to following formula subrepresentation：
yi=βTxi+∈i(1)
In formula, xiRepresent i-th sample, yiIt is the corresponding response variable of i-th sample, i.e. oil consumption.∈iIt is to obey normal state
The random error of distribution, β=(β1,β2,…,βp)TIt is regression coefficient variable, wherein βjRepresent j-th regression coefficient.
Step 4：Parameter learning and model training
Due to there may be high correlation between the factor for influenceing oil consumption, such as wind speed and wave are high, air pressure and wind it is strong
Degree, goods weight and drinking water etc. are separate between each input variable in step 4 in order to ensure, LASSO is used in the present invention
Algorithm enters line parameter and shrinks and variables choice.LASSO algorithms may be expressed as solving L as follows1The loss of constraint lower square error is most
Small problem：
In formula, N represents sample size, and p represents the input variable number that each sample is included, xijRepresent i-th sample pair
J-th input variable answered, βjThe corresponding regression coefficient of j-th input variable is represented, t represents decision regularization set in advance
The free parameter of degree, t >=0.
(2) formula is expressed as matrix form：
s.t.||βj||1≤t (3)
BecauseSo,
Formula (3) can be re-written as：
s.t.||βj||1≤t (5)
Method of Lagrange multipliers is expressed as：
In formula, λ >=0 is regulation parameter, weighs squared and L1The proportion of regularization term.Using intersection in the present invention
Proof method is combined with LARS algorithms, calculating parameter regularization path, while the parameter lambda in the formula of solution (6), all friendships of λ correspondences
Pitch the corresponding λ value of that mean square error of minimum in the average value of the result.Then solved using Osborne Conjugate Search Algorithms
LASSO estimates.Oil consumption forecast model is tried to achieve during LASSO estimates finally are substituted into formula (1).
Step 5：Model application
In training the oil consumption forecast model for obtaining in meteorological sea conditions and Ship Controling condition data input step four,
The fuel consumption for obtaining ship under the meteorological sea conditions and operating condition can be predicted.By predict fuel consumption values according to
Cubic function relation between oil consumption and the speed of a ship or plane extrapolates the speed of following ship's navigation, so as to instruct crewman ensureing navigation peace
On the premise of complete, within regulation sailing date, navigated by water with the manipulation schemes of low oil consumption, low emission, high benefit.
Such as Fig. 2 for LASSO, BP-ANN and SVR forecast model predict the outcome comparison diagram.Shown in figure unknown by 20 groups
In three kinds of oil consumption forecast models of meteorological sea state data and Ship Controling data input, the final oil consumption of output predicts the outcome and truly
Fuel consumption values.It can be seen that under identical navigation condition, prediction of the LASSO forecast models to oil consumption is more accurate, to boat
The change of row environment is more sensitive, and it is more stable to predict the outcome.
If Fig. 3 is the cumulative point comparison diagram that LASSO, BP-ANN and SVR forecast model predict the outcome.Mean absolute error
(MAE) refer to predicted value and actual value difference means absolute value result.Cumulative point is defined as：
Wherein, M is the size of unknown data collection, ME ＜ δData set of the absolute error that expression predicts the outcome less than δ ton days
Size.As can be seen from the figure predict the outcome BP-ANN models and the SVR moulds compared with the error between actual value of LASSO models
Type is small, and precision of prediction is higher.
If Fig. 4 is the mean absolute error value comparative result that LASSO, BP-ANN and SVR forecast model predict the outcome, the knot
Fruit is obtained on the basis of 5000 test sets.There it can be seen that the mean absolute error for predicting the outcome of LASSO models is most
Small, the excursion of mean absolute error is also minimum.
Those skilled in the art can carry out various remodeling and change to the present invention.Therefore, present invention covers falling into
Various remodeling and change in the range of appending claims and its equivalent.
Claims (1)
1. a kind of method that ship oil consumption is predicted according to sea situation and operating condition, it is characterised in that it is described according to sea situation with manipulate
The method of conditional forecasting ship oil consumption includes following five steps：
Raw data acquisition step：The Weather Forecast Information from weather bureau's issue is collected as meteorological data；By sensor
Sea state data is measured indirectly；Ship's navigation historical data is obtained by log noon report；By installing biography aboard ship
Sensor component obtains Ship Controling data；Data above is finally sent to database server storage；
Data prediction step：Data screening is carried out for the initial data gathered in raw data acquisition step, data are gone
Make an uproar, cut and synchronization process, then the integrated data from multiple data sources remove redundancy simultaneously, and finally integrated data are entered
The unified normalized of row, obtains model training data set；
Oil consumption forecast model establishment step：According to the characteristic distributions of training dataset and empirical hypothesis in data prediction step
Meet linear relationship between input feature vector variable and corresponding prediction output variable, set up Multiple Linear Regression Forecasting Models of Chinese；
Parameter learning and model training step：It is fixed using LASSO algorithms based on the training data obtained in data prediction step
An adopted cost function, and be converted to solution L1The convex quadratic programming problem of the lower square error minimization of loss of constraint, most terminates
Close cross validation to learn model parameter with LARS algorithms, and LASSO is solved using Osborne Conjugate Search Algorithms and estimate；Most
In LASSO estimates substitution oil consumption forecast model establishment step is set up oil consumption forecast model afterwards, the oil consumption after being trained
Forecast model；
Model applying step：Unknown meteorological sea state data and Ship Controling data input oil consumption forecast model establishment step are obtained
To oil consumption forecast model in, export final oil consumption and predict the outcome.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611001429.8A CN106779137A (en) | 2016-11-14 | 2016-11-14 | A kind of method that ship oil consumption is predicted according to sea situation and operating condition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611001429.8A CN106779137A (en) | 2016-11-14 | 2016-11-14 | A kind of method that ship oil consumption is predicted according to sea situation and operating condition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106779137A true CN106779137A (en) | 2017-05-31 |
Family
ID=58968058
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611001429.8A Pending CN106779137A (en) | 2016-11-14 | 2016-11-14 | A kind of method that ship oil consumption is predicted according to sea situation and operating condition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106779137A (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107292451A (en) * | 2017-07-18 | 2017-10-24 | 上海海阳气象导航技术有限公司 | A kind of ship speed optimization method and equipment |
| CN107545785A (en) * | 2017-07-21 | 2018-01-05 | 华南理工大学 | A kind of river channel running method based on big data |
| CN107944648A (en) * | 2018-01-08 | 2018-04-20 | 中国船舶工业系统工程研究院 | A kind of accurate Forecasting Methodology of large ship speed of a ship or plane rate of fuel consumption |
| CN108564237A (en) * | 2017-12-13 | 2018-09-21 | 中国银联股份有限公司 | A kind of Capacity Evaluation Model method for building up, capacity evaluating method and device |
| CN109710879A (en) * | 2017-08-17 | 2019-05-03 | 中国水利水电科学研究院 | A kind of optimized treatment method and device of forecast system of controlling flood |
| CN109743019A (en) * | 2018-12-21 | 2019-05-10 | 中国计量大学 | The system and method for hot spot temperature prediction and hot spot positioning based on meteorologic factor |
| CN110046735A (en) * | 2018-12-10 | 2019-07-23 | 南京航空航天大学 | Aircraft based on flying quality analysis is left the theatre fuel consumption appraisal procedure |
| CN110826790A (en) * | 2019-10-31 | 2020-02-21 | 天津大学 | Intelligent prediction method for construction productivity of cutter suction dredger |
| CN110849411A (en) * | 2018-08-21 | 2020-02-28 | 上海博泰悦臻网络技术服务有限公司 | Internet of vehicles server, vehicle and vehicle condition evaluation method based on driving oil consumption data |
| CN111476484A (en) * | 2020-04-07 | 2020-07-31 | 上海船舶研究设计院（中国船舶工业集团公司第六0四研究院） | Ship fuel consumption reason analysis method and device |
| CN112149909A (en) * | 2020-09-28 | 2020-12-29 | 神华中海航运有限公司 | Ship oil consumption prediction method and device, computer equipment and storage medium |
| CN112836893A (en) * | 2021-02-26 | 2021-05-25 | 上海海事大学 | Method for predicting ship oil consumption under severe sea conditions based on sea condition and ship navigation conditions |
| CN112925201A (en) * | 2021-01-20 | 2021-06-08 | 北京大学 | Model prediction control-based fuel control method for ship propulsion system |
| CN112977754A (en) * | 2021-04-07 | 2021-06-18 | 梧州欣欣科技发展有限公司 | Oil consumption analysis method suitable for running of inland ship |
| US20220194533A1 (en) * | 2019-02-07 | 2022-06-23 | Shell Oil Company | Method and system for reducing vessel fuel consumption |
| CN114780644A (en) * | 2022-06-17 | 2022-07-22 | 鹏城实验室 | Ship navigation data processing method, device, equipment and storage medium |
| CN115456451A (en) * | 2022-09-28 | 2022-12-09 | 中远海运科技股份有限公司 | Ship oil consumption estimation method and system based on AIS |
| CN115660137A (en) * | 2022-09-07 | 2023-01-31 | 中远海运科技股份有限公司 | Method for accurately estimating wind wave navigation energy consumption of ship |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110208377A1 (en) * | 2007-08-14 | 2011-08-25 | Propeller Control Aps | Efficiency optimizing propeller speed control for ships |
| CN104463381A (en) * | 2015-01-04 | 2015-03-25 | 福州大学 | Building energy consumption predication method based on KPCA and WLSSVM |
-
2016
- 2016-11-14 CN CN201611001429.8A patent/CN106779137A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110208377A1 (en) * | 2007-08-14 | 2011-08-25 | Propeller Control Aps | Efficiency optimizing propeller speed control for ships |
| CN104463381A (en) * | 2015-01-04 | 2015-03-25 | 福州大学 | Building energy consumption predication method based on KPCA and WLSSVM |
Non-Patent Citations (2)
| Title |
|---|
| 叶睿 等: "基于人工神经网络的船舶油耗模型", 《船舶工程》 * |
| 周雁舟 等: "基于LASSO-LARS的软件复杂性度量属性特征选择研究", 《计算机科学》 * |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107292451B (en) * | 2017-07-18 | 2020-11-03 | 上海海阳气象导航技术有限公司 | Ship speed optimization method and equipment |
| CN107292451A (en) * | 2017-07-18 | 2017-10-24 | 上海海阳气象导航技术有限公司 | A kind of ship speed optimization method and equipment |
| CN107545785A (en) * | 2017-07-21 | 2018-01-05 | 华南理工大学 | A kind of river channel running method based on big data |
| CN109710879A (en) * | 2017-08-17 | 2019-05-03 | 中国水利水电科学研究院 | A kind of optimized treatment method and device of forecast system of controlling flood |
| CN108564237A (en) * | 2017-12-13 | 2018-09-21 | 中国银联股份有限公司 | A kind of Capacity Evaluation Model method for building up, capacity evaluating method and device |
| CN107944648A (en) * | 2018-01-08 | 2018-04-20 | 中国船舶工业系统工程研究院 | A kind of accurate Forecasting Methodology of large ship speed of a ship or plane rate of fuel consumption |
| CN107944648B (en) * | 2018-01-08 | 2021-06-18 | 中国船舶工业系统工程研究院 | Large ship speed and oil consumption rate prediction method |
| CN110849411A (en) * | 2018-08-21 | 2020-02-28 | 上海博泰悦臻网络技术服务有限公司 | Internet of vehicles server, vehicle and vehicle condition evaluation method based on driving oil consumption data |
| CN110046735B (en) * | 2018-12-10 | 2021-02-26 | 南京航空航天大学 | Aircraft departure fuel oil consumption evaluation method based on flight data analysis |
| CN110046735A (en) * | 2018-12-10 | 2019-07-23 | 南京航空航天大学 | Aircraft based on flying quality analysis is left the theatre fuel consumption appraisal procedure |
| CN109743019B (en) * | 2018-12-21 | 2023-09-19 | 中国计量大学 | System and method for hot spot temperature prediction and hot spot positioning based on meteorological factors |
| CN109743019A (en) * | 2018-12-21 | 2019-05-10 | 中国计量大学 | The system and method for hot spot temperature prediction and hot spot positioning based on meteorologic factor |
| US20220194533A1 (en) * | 2019-02-07 | 2022-06-23 | Shell Oil Company | Method and system for reducing vessel fuel consumption |
| CN110826790A (en) * | 2019-10-31 | 2020-02-21 | 天津大学 | Intelligent prediction method for construction productivity of cutter suction dredger |
| CN111476484A (en) * | 2020-04-07 | 2020-07-31 | 上海船舶研究设计院（中国船舶工业集团公司第六0四研究院） | Ship fuel consumption reason analysis method and device |
| CN111476484B (en) * | 2020-04-07 | 2024-03-12 | 上海船舶研究设计院（中国船舶工业集团公司第六0四研究院） | Ship fuel consumption high cause analysis method and device |
| CN112149909A (en) * | 2020-09-28 | 2020-12-29 | 神华中海航运有限公司 | Ship oil consumption prediction method and device, computer equipment and storage medium |
| CN112925201A (en) * | 2021-01-20 | 2021-06-08 | 北京大学 | Model prediction control-based fuel control method for ship propulsion system |
| CN112925201B (en) * | 2021-01-20 | 2022-01-28 | 北京大学 | Model prediction control-based fuel control method for ship propulsion system |
| CN112836893A (en) * | 2021-02-26 | 2021-05-25 | 上海海事大学 | Method for predicting ship oil consumption under severe sea conditions based on sea condition and ship navigation conditions |
| CN112836893B (en) * | 2021-02-26 | 2024-05-14 | 上海海事大学 | Method for predicting ship oil consumption under severe sea conditions based on sea condition and ship navigation condition |
| CN112977754A (en) * | 2021-04-07 | 2021-06-18 | 梧州欣欣科技发展有限公司 | Oil consumption analysis method suitable for running of inland ship |
| CN114780644A (en) * | 2022-06-17 | 2022-07-22 | 鹏城实验室 | Ship navigation data processing method, device, equipment and storage medium |
| CN114780644B (en) * | 2022-06-17 | 2022-09-13 | 鹏城实验室 | Ship navigation data processing method, device, equipment and storage medium |
| CN115660137A (en) * | 2022-09-07 | 2023-01-31 | 中远海运科技股份有限公司 | Method for accurately estimating wind wave navigation energy consumption of ship |
| CN115660137B (en) * | 2022-09-07 | 2023-08-11 | 中远海运科技股份有限公司 | Accurate estimation method for wind wave navigation energy consumption of ship |
| CN115456451A (en) * | 2022-09-28 | 2022-12-09 | 中远海运科技股份有限公司 | Ship oil consumption estimation method and system based on AIS |
| CN115456451B (en) * | 2022-09-28 | 2023-10-31 | 中远海运科技股份有限公司 | AIS-based ship oil consumption estimation method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106779137A (en) | A kind of method that ship oil consumption is predicted according to sea situation and operating condition | |
| Fan et al. | A novel model to predict significant wave height based on long short-term memory network | |
| CN107944648B (en) | Large ship speed and oil consumption rate prediction method | |
| CN109214107A (en) | A kind of ship's navigation behavior on-line prediction method | |
| CN108960421B (en) | Improved online forecasting method for speed of unmanned surface vehicle based on BP neural network | |
| CN109143408B (en) | Dynamic region combined short-time rainfall forecasting method based on MLP | |
| CN107092987B (en) | Method for predicting autonomous landing wind speed of small and medium-sized unmanned aerial vehicles | |
| CN114444378A (en) | Short-term power prediction method for regional wind power cluster | |
| JPWO2006120724A1 (en) | Geographic information system using neural network | |
| Zhao et al. | An evaporation duct height prediction model based on a long short-term memory neural network | |
| CN112380781B (en) | Satellite observation completion method based on reanalysis data and unbalanced learning | |
| CN115081307A (en) | Weather numerical mode assimilation method and device based on neural network | |
| CN111144666A (en) | Ocean thermocline prediction method based on deep space-time residual error network | |
| Castelão | A machine learning approach to quality control oceanographic data | |
| CN115545159A (en) | Average sea surface temperature forecasting method of deep neural network | |
| CN116021981A (en) | Method, device, equipment and storage medium for predicting ice coating faults of power distribution network line | |
| CN118312792A (en) | Artificial intelligent detection method and system for ocean buoy data quality | |
| Liang et al. | A multivariate empirical orthogonal function method to construct nitrate maps in the Southern Ocean | |
| Niu et al. | Precipitation forecast based on multi-channel ConvLSTM and 3d-CNN | |
| Menaka et al. | Prediction of dominant ocean parameters for sustainable marine environment | |
| CN117665974A (en) | Sea fog and low cloud coverage two-dimensional distribution field forecasting method and system | |
| Domala et al. | A Univariate and multivariate machine learning approach for prediction of significant wave height | |
| Eleftheriou et al. | Micro climate prediction utilising machine learning approaches | |
| Trifunov et al. | A data-driven approach to partitioning net ecosystem exchange using a deep state space model | |
| KR102411074B1 (en) | Devices and Methods for Detection and Prediction of Cold Water Mass Using Artificial Intelligence and Satellite Imaging |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170531 |