Online Training for Fuel Oil Consumption Estimation: A Data Driven Approach

Kaklis, Dimitrios; Varlamis, Iraklis; Giannakopoulos, George; Spyropoulos, Constantine D.; Varelas, Takis J.

doi:10.1109/mdm55031.2022.00088

Cited by 5 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They employed input variables such as the rotation speed of the combustion engine, a controllable pitch propeller, marine conditions, and time since the last docking from the onboard sensors and decision models of the Barquentine. Kaklis, Dimitrios et al [33,34] estimated fuel consumption using Automatic Identification System (AIS) data and onboard sensor data from a 3000 TEU container ship. Tran, Tien Anh [35] developed a fuel consumption estimation model with an ANN algorithm considering the factors of cargo load, diesel engine load, and engine operating status obtained from a Data Acquisition System (DAS) and validated it on a bulk carrier.…”

Section: Literature Reviewmentioning

confidence: 99%

The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data

Lee,

Eom,

Park

et al. 2024

Sustainability

View full text Add to dashboard Cite

Zero-carbon shipping is the prime goal of the seaborne trade industry at this moment. The utilization of ammonia and liquid hydrogen propulsion in a carbon-free propulsion system is a promising option to achieve net-zero emission in the maritime supply chain. Meanwhile, optimal ship voyage planning is a candidate to reduce carbon emissions immediately without new buildings and retrofits of the alternative fuel-based propulsion system. Due to the voyage options, the precise prediction of fuel consumption and carbon emission via voyage operation profile optimization is a prerequisite for carbon emission reduction. This paper proposes a novel fuel consumption and carbon emission quantity prediction method which is based on the onboard measurement data of a smart ship. The prediction performance of the proposed method was investigated and compared to machine learning and LSTM-model-based fuel consumption and gas emission prediction methods. The results had an accuracy of 81.5% in diesel mode and 91.2% in gas mode. The SHAP (Shapley additive explanations) model, an XAI (Explainable Artificial Intelligence), and a CO2 consumption model were employed to identify the major factors used in the predictions. The accuracy of the fuel consumption calculated using flow meter data, as opposed to power load data, improved by approximately 21.0%. The operational and flow meter data collected by smart ships significantly contribute to predicting the fuel consumption and carbon emissions of vessels.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data

Lee,

Eom,

Park

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Kaklis D presents a holistic approach to continuously monitor and estimate the emissions of a vessel as well as to assess and improve the efficiency of scrubbers [34]. Kaklis D presents an online learning framework that employs a custom encoding-decoding Neural Network scheme and real-time data from various on-board sensors to appropriately update FOC estimation models [35].…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method of Ship Motion Based on LSTM Neural Network with Variable Step-Variable Sampling Frequency Characteristics

Han

Xiong

2023

JMSE

View full text Add to dashboard Cite

In active heave compensation, in order to realize the smooth control of the heave compensation platform, it is necessary to use the ship motion measurement system to accurately obtain the ship displacement signal, invert the ship displacement signal, and then control the expansion and contraction of the electric cylinder so that the compensation platform remains horizontal. The ship displacement measurement system generally adopts the second integral of the acceleration sensor to obtain the ship displacement signal. During the acquisition process of the ship displacement signal, the quadratic integration process of the acceleration, and the communication process of the output control command, there is a processing lag which makes the error accumulate, resulting in a delay in the measurement of the ship motion. In order to collect the ship motion more accurately and control the heave compensation platform more precisely, this paper proposes a ship motion prediction method based on a variable step-variable sampling frequency characteristic LSTM (Long Short-Term Memory) neural network. First, we use the autocorrelation function algorithm to calculate the inherent delay of the lag in the process of signal acquisition by the measurement system. Secondly, the LSTM neural network is used to predict the inherent delay step of the lagging ship displacement signal. During the prediction process, it is found that the difference in the sampling frequency of the displacement signal will lead to a change in the step of the inherent delay—experiment in the laboratory to verify. By controlling the motion platform to simulate the motion of the ship and using the ship motion measurement system and the laser sensor system to measure the displacement signal of the motion platform synchronously, it is verified that the ship motion measurement system does have an inherent delay. Thirdly, on a sailing ship, ship displacement signals are collected by setting multiple sets of ship motion measurement systems. Finally, multiple sets of sampling frequency and multiple steps are set, and the ship motion is predicted based on the variable step-variable sampling frequency LSTM neural network. It is verified that the prediction accuracy is related to the sampling frequency of the signal collector and the prediction step of the LSTM neural network, which improves the prediction accuracy of the model and the timeliness of ship motion acquisition.

show abstract

Data-driven prediction of ship fuel oil consumption based on machine learning models considering meteorological factors

Yang,

Sun,

Han

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

To improve the energy efficiency of ships and reduce greenhouse gas (GHG) emissions, the implementation of energy-efficient operation measures is particularly important. Driven by this, this study was dedicated to improving the accuracy of ship fuel oil consumption (FOC) prediction and laying the foundation for optimizing energy-efficient operations. Firstly, we combined voyage reports and meteorological data and constructed six datasets containing different features. These features comprise navigation-related features encompassing sailing speed, displacement and trim, as well as meteorological features encompassing wind, wave, sea current, sea water salinity and sea water temperature. Secondly, we conducted experiments with 14 popular ML models on the datasets and compared the prediction performance of different models by a new scoring system. Finally, we explored the advantages and disadvantages of each dataset based on the model performance scoring results and analyzed the effects of related meteorological factors on FOC during navigation. The key findings of the proposed work were that extra trees (ET), random forest (RF), XGBoost, and LightGBM had good fitting and generalization performance. Set5, the dataset containing the most complete meteorological data, achieved the best prediction results. In particular, it had an R2 (test) of 0.9317 on the ET model, which was 1.97% higher than the R2 (test) of the dataset using only voyage reports. The conclusions can assist shipping companies in constructing a ship FOC prediction framework and developing ship fuel-saving strategies.

show abstract

Online Training for Fuel Oil Consumption Estimation: A Data Driven Approach

Cited by 5 publications

References 16 publications

The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data

The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data

A Prediction Method of Ship Motion Based on LSTM Neural Network with Variable Step-Variable Sampling Frequency Characteristics

Data-driven prediction of ship fuel oil consumption based on machine learning models considering meteorological factors

Contact Info

Product

Resources

About