With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms, which are usually more accurate and less error, the use of these algorithms increases the ability of the model to solve complex problems in this field. In this article, we have tried to examine DL algorithms that are very powerful in problem solving but have received less attention in other studies, such as RNN, ANFIS, RBN, DBN, WNN, and so on. This research uses knowledge discovery in research databases to understand ML and DL applications in energy systems’ current status and future. Subsequently, the critical areas and research gaps are identified. In addition, this study covers the most common and efficient applications used in this field; optimization, forecasting, fault detection, and other applications of energy systems are investigated. Attempts have also been made to cover most of the algorithms and their evaluation metrics, including not only algorithms that are more important, but also newer ones that have received less attention.

/pdf/machine-learning-and-deep-learning-in-energy-systems-a-2deen8do.pdf

Machine Learning and Deep Learning in Energy Systems: A Review

A review of ship fuel consumption models

The shipping industry is associated with approximately three quarters of all world trade. In recent years, the sustainability of shipping has become a public concern, and various emissions control regulations to reduce pollutants and greenhouse gas (GHG) emissions from ships have been proposed and implemented globally. These regulations aim to drive the shipping industry in a low-carbon and low-pollutant direction by motivating it to switch to more efficient fuel types and reduce energy consumption. At the same time, the cyclical downturn of the world economy and high bunker prices make it necessary and urgent for the shipping industry to operate in a more cost-effective way while still satisfying global trade demand. As bunker fuel bunker (e.g., heavy fuel oil [HFO], liquified natural gas [LNG]) consumption is the main source of emissions and bunker fuel costs account for a large proportion of operating costs, shipping companies are making unprecedented efforts to optimize ship energy efficiency. It is widely accepted that the key to improving the energy efficiency of ships is the development of accurate models to predict ship fuel consumption rates under different scenarios. In this study, ship fuel consumption prediction models presented in the literature (including the academic literature and technical reports as a typical type of “grey literature”) are reviewed and compared, and models that optimize ship operations based on fuel consumption prediction results are also presented and discussed. Current research challenges and promising research questions on ship performance monitoring and operational optimization are identified.

Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Design of a solar photovoltaic system for a Ro-Ro ship and estimation of performance analysis: A case study

Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge, while few of them are based on historical data accumulated from practice. In recent years, emerging approaches, which we refer to as machine learning and deep learning techniques in this essay, have been receiving a wider attention to solve practical problems. As a relatively conservative industry, there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector. The objective of this essay is to review the application of emerging approaches to maritime transport research. The main research topics in maritime transport and classic methods developed to solve them are first presented. The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed. Related existing studies are then reviewed according to problem settings, main data sources, and emerging approaches adopted. Challenges and solutions in the process are also discussed from the perspectives of data, model, users, and targets. Finally, promising future research directions are identified. This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.

Emerging approaches applied to maritime transport research: Past and future

An improvement of the marine vessel's fuel consumption will provide efficiency and profitability in ship management since fuel cost is one of the biggest operating cost. However, estimation of the fuel consumption of marine vessels is a difficult issue, because the fuel consumption rate of the vessel is directly dependent on multiple external factors such as the condition of the main engine, cargo weight, ship draft, sea condition, weather condition, etc. Nowadays, statistical models have been established based on actual ship data, and the fuel consumption of the vessel has been tried to be estimated as best as possible. In this study, various prediction models such as Multiple Linear Regression, Ridge and LASSO Regression, Support Vector Regression, Tree-Based Algorithms, Boosting Algorithms have been established for a container ship. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, coefficient of determination are employed in order to evaluate the correctness of estimation models and correlation analysis between variables is accomplished. Parameters such as main engine rpm, main engine cylinder values, scavenge air, shaft indicators are found highly correlated with fuel consumption. Under the influence of various external factors on fuel consumption, the nearest estimation of the actual fuel consumption data is made by multiple linear regression and ridge regression with 0.0001 root mean square error, 0.002 mean absolute error and %99.9 coefficient of determination score.

Machine learning approach to ship fuel consumption: A case of container vessel

Data-driven ship berthing forecasting for cold ironing in maritime transportation

In recent years, the usage potential of alternative energy sources has been gaining importance to increase the efficiency of ships within the scope of the obligations brought by international maritime regulations. The possibility of using alternative energy sources such as solar energy, wind energy, fuel cells, and waste heat recovery technologies on ships has been evaluated in the literature. Today, ships also have waste heat recovery systems as standard equipment for this purpose, and this method is suitable for thermoelectric generators that generate electricity from temperature differences on shipboards. This article aims to review the thermal technologies for the power generation of shipboards. By conducting a case study, an energy efficiency increase was obtained when functional areas were selected on a practical ship, and the effect of this efficiency increase on emissions was examined. As a result of the research, it was discovered that thermoelectric generators increased onboard energy efficiency and have significant potential for sustainability in the maritime sector.

/pdf/thermoelectric-generators-as-an-alternative-energy-source-in-10v1s32z.pdf

Thermoelectric Generators as an Alternative Energy Source in Shipboard Microgrids

Risk assessment of sea chest fouling on the ship machinery systems by using both FMEA method and ERS process

In recent years, shipborne emissions have become a growing environmental threat. The International Maritime Organization has implemented various rules and regulations to resolve this concern. The Ship Energy Efficiency Management Plan, Energy Efficiency Design Index, and Energy Efficiency Operational Indicator are examples of guidelines that increase energy efficiency and reduce shipborne emissions. The main engine shaft power (MESP) and fuel consumption (FC) are the critical components used in ship energy efficiency calculations. Errors in ship energy efficiency calculation methodologies are also caused by misinterpretation of these values. This study aims to predict the MESP and FC of a container ship with the help of data-driven methodologies utilizing actual voyage data to assist in the calculation process of the ship’s energy efficiency indexes appropriately. The algorithms’ prediction success was measured using the RMSE, MAE, and R2 error metrics. When the simulation results were analyzed, the Deep Neural Network and Bayes algorithms predicted MESP best with 0.000001 and 0.000002 RMSE, 0.000987 and 0.000991 MAE, and 0.999999 R2, respectively, while the Multiple-Linear Regression and Kernel Ridge algorithms estimated FC best with 0.000208 and 0.000216 RMSE, 0.001375 and 0.001471 MAE, and 0.999999 R2, respectively.

/pdf/data-driven-approach-for-estimating-power-and-fuel-3p7lt1vy.pdf

Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

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