Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

Ni, Chenhua; Ma, Xiandong

doi:10.3390/en11082097

Cited by 25 publications

(12 citation statements)

References 39 publications

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“…By using the output gate of each cell as is written in Equation ( 15), the output of the whole network of a long short-term memory has been presented as Equation (16).…”

Section: Machine Learning Lstm Methods Of Predictionmentioning

confidence: 99%

Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

et al. 2021

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Accurate forecasts of ocean waves energy can not only reduce costs for investment, but it is also essential for the management and operation of electrical power. This paper presents an innovative approach based on long short-term memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analysis is provided by collecting the experimental data from another study and the exerted data from a numerical simulation of Searaser. The simulation is performed with Flow-3D software, which has high capability in analyzing fluid–solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study, wind speed and output power are related with an LSTM method. Moreover, it can be inferred that the LSTM network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement, and the root mean square is 0.49 in the mean value related to the accuracy of the LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of the LSTM method.

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“…By using the output gate of each cell as is written in Equation ( 15), the output of the whole network of a long short-term memory has been presented as Equation (16).…”

Section: Machine Learning Lstm Methods Of Predictionmentioning

confidence: 99%

Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

et al. 2021

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“…Ni and Ma 25 researched the applicability of implementing a model based on CNN to predict the generation of power from a marine wave energy converter (WEC) system through the utilization of a double buoy oscillating device (OBD). A multi-input approach was used to train and test the CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Suspended sediment load prediction using long short-term memory neural network

AlDahoul

Essam

Kumar

et al. 2021

Sci Rep

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Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988–1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.

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“…Machine learning is often used to forecast, for example waves [15] and wave power generation [16] or to characterize wave energy resources [17]. For control purposes, there are a few examples where machine learning approaches have been used: in [18], an artificial neural network (ANN) was used for reactive control, in [11] an artificial neural oscillator was used to bring a latching device in resonance with the waves and in [19] an ANN was controlling the damping of a WEC.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays

et al. 2018

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A challenge while applying latching control on a wave energy converter (WEC) is to find a reliable and robust control strategy working in irregular waves and handling the non-ideal behavior of real WECs. In this paper, a robust and model-free collaborative learning approach for latchable WECs in an array is presented. A machine learning algorithm with a shallow artificial neural network (ANN) is used to find optimal latching times. The applied strategy is compared to a latching time that is linearly correlated with the mean wave period: It is remarkable that the ANN-based WEC achieved a similar power absorption as the WEC applying a linear latching time, by applying only two different latching times. The strategy was tested in a numerical simulation, where for some sea states it absorbed more than twice the power compared to the uncontrolled WEC and over 30% more power than a WEC with constant latching. In wave tank tests with a 1:10 physical scale model the advantage decreased to +3% compared to the best tested constant latching WEC, which is explained by the lower advantage of the latching strategy caused by the non-ideal latching of the physical power take-off model.

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Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

Cited by 25 publications

References 39 publications

Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

Suspended sediment load prediction using long short-term memory neural network

Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays

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