A feature transformation and extraction approach-based artificial neural network for an improved production prediction of grid-connected solar photovoltaic systems

Al‐Dahidi, Sameer; Adeeb, Jehad; Ayadi, Osama; Alrbai, Mohammad; Al‐Ghussain, Loiy

doi:10.1080/15567036.2022.2128475

Cited by 3 publications

(1 citation statement)

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“…After inputs are received from one end and processed/ summed, the outputs are generated on the other end of the artificial neuron. Within this process, the weight factor for each input is calculated according to the strength of the input signal [100]. The weighted sum of all inputs is processed via a nonlinear function, known as the activation linear function [101].…”

Section: Ann Structurementioning

confidence: 99%

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Nguyen

Sakthivel

Rudzki

et al. 2023

Polish Maritime Research

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In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.

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Section: Ann Structurementioning

confidence: 99%