arXiv subjects

Mohamed Abuella

Publications and source records attributed to Mohamed Abuella.

7 recordsLinked to original sources

arXiv2024

Data Analytics for Improving Energy Efficiency in Short Sea Shipping

To meet the urgent requirements for the climate change mitigation, several proactive measures of energy efficiency have been implemented in maritime industry. Many of these practices depend highly on the onboard data of vessel's operation and environmental conditions. In this paper, a high resolution onboard data from passenger vessels in short-sea shipping (SSS) have been collected and preprocessed. We first investigated the available data to deploy it effectively to model the physics of the vessel, and hence the vessel performance. Since in SSS, the weather measurements and forecasts might have not been in temporal and spatial resolutions that accurately representing the actual environmental conditions. Then, We proposed a data-driven modeling approach for vessel energy efficiency. This approach addresses the challenges of data representation and energy modeling by combining and aggregating data from multiple sources and seamlessly integrates explainable artificial intelligence (XAI) to attain clear insights about the energy efficiency for a vessel in SSS. After that, the developed model of energy efficiency has been utilized in developing a framework for optimizing the vessel voyage to minimize the fuel consumption and meeting the constraint of arrival time. Moreover, we developed a spatial clustering approach for labeling the vessel paths to detect the paths for vessels with operating routes of repeatable and semi-repeatable paths.

cs.CE↗

arXiv2024

Spatial Clustering Approach for Vessel Path Identification

This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five classes achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation.

cs.LG↗

arXiv2024

Time-Series Analysis Approach for Improving Energy Efficiency of a Fixed-Route Vessel in Short-Sea Shipping

Several approaches have been developed for improving the ship energy efficiency, thereby reducing operating costs and ensuring compliance with climate change mitigation regulations. Many of these approaches will heavily depend on measured data from onboard IoT devices, including operational and environmental information, as well as external data sources for additional navigational data. In this paper, we develop a framework that implements time-series analysis techniques to optimize the vessel's speed profile for improving the vessel's energy efficiency. We present a case study involving a real-world data from a passenger vessel that was collected over a span of 15 months in the south of Sweden. The results indicate that the implemented models exhibit a range of outcomes and adaptability across different scenarios. The findings highlight the effectiveness of time-series analysis approach for optimizing vessel voyages within the context of constrained landscapes, as often seen in short-sea shipping.

cs.CE↗

arXiv2017

Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting

To mitigate the uncertainty of variable renewable resources, two off-the-shelf machine learning tools are deployed to forecast the solar power output of a solar photovoltaic system. The support vector machines generate the forecasts and the random forest acts as an ensemble learning method to combine the forecasts. The common ensemble technique in wind and solar power forecasting is the blending of meteorological data from several sources. In this study though, the present and the past solar power forecasts from several models, as well as the associated meteorological data, are incorporated into the random forest to combine and improve the accuracy of the day-ahead solar power forecasts. The performance of the combined model is evaluated over the entire year and compared with other combining techniques.

cs.LG↗

arXiv2017

Solar Power Forecasting Using Support Vector Regression

Generation and load balance is required in the economic scheduling of generating units in the smart grid. Variable energy generations, particularly from wind and solar energy resources, are witnessing a rapid boost, and, it is anticipated that with a certain level of their penetration, they can become noteworthy sources of uncertainty. As in the case of load demand, energy forecasting can also be used to mitigate some of the challenges that arise from the uncertainty in the resource. While wind energy forecasting research is considered mature, solar energy forecasting is witnessing a steadily growing attention from the research community. This paper presents a support vector regression model to produce solar power forecasts on a rolling basis for 24 hours ahead over an entire year, to mimic the practical business of energy forecasting. Twelve weather variables are considered from a high-quality benchmark dataset and new variables are extracted. The added value of the heat index and wind speed as additional variables to the model is studied across different seasons. The support vector regression model performance is compared with artificial neural networks and multiple linear regression models for energy forecasting.

cs.LG↗

arXiv2016

Selection of Most Effective Control Variables for Solving Optimal Power Flow Using Sensitivity Analysis in Particle Swarm Algorithm

Solving the optimal power flow problem is one of the main objectives in electrical power systems analysis and design. The modern optimization algorithms such as the evolutionary algorithms are also adopted to solve this problem, especially when the intermittency nature of generation resources are included, as in wind and solar energy resources, where the models are stochastic and non-linear. This paper uses the particle swarm optimization algorithm for solving the optimal power flow for IEEE-30 bus system. In addition to selection of the most effective control variables based on sensitivity analysis to alleviate the violations and return the system back to its normal state. This adopted strategy would decrease the optimal power flow calculation burden by particle swarm optimization algorithm, especially with large systems.

cs.CE↗

arXiv2015

The Economic Dispatch for Integrated Wind Power Systems Using Particle Swarm Optimization

The economic dispatch of wind power units is quite different from that in conventional thermal units, since the adopted model should take into consideration the intermittency nature of wind speed as well. Therefore, this paper uses a model that takes into account the aforementioned consideration in addition to whether the utility owns wind turbines or not. The economic dispatch is solved by using one of the modern optimization algorithms: the particle swarm optimization algorithm. A 6-bus system is used and it includes wind-powered generators besides to thermal generators. The thorough analysis of the results is also provided.

cs.CE↗