CN106779137A - A kind of method that ship oil consumption is predicted according to sea situation and operating condition - Google Patents
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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.
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Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
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| 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 |
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