Performance of artificial neural network in prediction of heave displacement for non‐buoyant type wave energy converter

Santhosh, Nagulan; Jegadheeswaran, S.; Amarkarthik, Arunachalam; Sivanandam, Kaushik

doi:10.1049/iet-rpg.2015.0416

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…The MLP-ANN will be train using Levenberg-Marquadt learning algorithm for its fast-computational time [27,28]. In this work, the energy consumption that was measured and the independent variables data collected will be divided into 70% training 15% validation and 15% testing distribution [29] by interleaved method for modelling purpose. The number of neurons and tapped delay lines of the NARX-ANN model will be determined by trial and error method.…”

Section: ( ) = 1 1 + 2 2 + + 3 3 +mentioning

confidence: 99%

Energy consumption prediction through linear and non-linear baseline energy model

Mustapa

Dahlan

Yassin

et al. 2020

IJEECS

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<span>Accurate baseline energy models demand increase significantly as it lower the risk of energy savings quantification. It is achieved by performing energy consumption prediction with its respective independent variables through linear or non-linear modelling technique. Developing such model through linear modelling technique provide certain disadvantages due to the fact that the behavior of certain independent variables with respect to the energy consumption is non-linear in nature. Furthermore, linear modelling technique requires prior studies upon modelling to achieve accurate energy consumption prediction. Thus, to apprehend this situation, this paper main intention is to perform energy consumption prediction through a non-linear modelling technique to provide alternative option for developing a good and accurate baseline energy models. This study proposes energy consumption prediction based on Non-linear Auto Regressive with Exogenous Input – Artificial Neural Network (NARX-ANN) as a non-linear modelling technique that will be compared with Multiple Linear Regression Model (MLR) as linear modelling technique. A case study in Malaysian educational buildings during lecture week will be used for this purpose. The results demonstrate that NARX-ANN shows a higher accuracy through statistical error measurement.</span>

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Section: ( ) = 1 1 + 2 2 + + 3 3 +mentioning

confidence: 99%

Energy consumption prediction through linear and non-linear baseline energy model

Mustapa

Dahlan

Yassin

et al. 2020

IJEECS

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“…Gan et al [22] used a fuzzy c-means algorithm to group the historical tracks and then employed artificial neural network to predict ships' track length and it effectively improved the system's efficiency. Nagulan et al [23] applied artificial neural network to the prediction of heave displacement of non-buoyant-type wave energy converters. However, such methods are prone to be lost in the local optimum and have a low convergence speed.…”

Section: Literature Reviewmentioning

confidence: 99%

Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model

Dong

Wang²,

Tseng

et al. 2020

Symmetry

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This study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probability curve from where the gyro motor state evaluation and prediction were realized. A core component of gyroscopes is linked to the reliability of the inertia system to conduct gyro motor state evaluation and prediction. This study features the vectors’ extraction from full life cycle gyro motor data and completes the training model to feature the vectors according to the time sequence and extraction to full life cycle data undergoing hidden Markov model training. This proposed model applies to full life cycle gyro motor data for validation, compared with traditional hidden Markov model predictive methods and health condition-trained data. The results suggest precise evaluation and prediction and provide an important basis for gyro motor repair and replacement strategies.

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“…Fuzzy artificial neural network (ANN) using generalized ellipsoidal basis function [32] are proposed for black-box modeling of ship motion. The assessment of heave displacement for non-buoyant type WEC is investigated by means of ANN [33]. ANN is utilized to predict the wave surface elevation at the WEC location using measurements of wave elevation at ahead located sensor [34].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process

Liu

Xue

Huang

et al. 2021

JMSE

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Reducing the carbon emissions of ships and increasing the utilization of marine renewable energy are the important ways to achieve the goal of carbon neutrality in ocean engineering. Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of operation energy efficiency optimization and prediction of power generation. System identification from observed input–output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KRISO very large crude oil carrier (KVLCC2) ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics, and prediction accuracy.

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Performance of artificial neural network in prediction of heave displacement for non‐buoyant type wave energy converter

Cited by 5 publications

References 9 publications

Energy consumption prediction through linear and non-linear baseline energy model

Energy consumption prediction through linear and non-linear baseline energy model

Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model

Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process

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