Prediction of Air Pressure Change Inside the Chamber of an Oscillating Water Column–Wave Energy Converter Using Machine-Learning in Big Data Platform

Dongwoo Seo,Myungil Kim,Daeyong Jung,Taesang Huh,Jaesoon Hwang

doi:10.3390/en14112982

Dongwoo Seo, Myungil Kim + Show 3 more

Open Access

https://doi.org/10.3390/en14112982

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Journal: Energies	Publication Date: May 21, 2021
Citations: 6	License type: CC BY 4.0

Affiliation: Korea Institute of Science & Technology Information

Abstract

Wave power is an eco-friendly power generation method. Owing to the highly volatile nature of wave energy, the application of prediction techniques for power generation, failure diagnosis, and operational efficiency plays a key role in the successful operation of wave power plants (WPPs). To this end, we propose the following approaches: (i) deriving the correlation between highly volatile data such as wave height data and sensor data in an oscillating water column (OWC) chamber; (ii) development of an optimal training model capable of accurate prediction of the state of the wave energy converter (WEC) based on the collected sensor data. In this study, we developed a big data analysis system that can utilize the machine learning framework in KNIME (an open analysis platform), and to enable smart operation, we designed a training model using a digital twin of an OWC–WEC that is currently in operation. Using various machine learning models, the pressure of the OWC chamber was predicted, and the results obtained were tested and evaluated to confirm its validity. Furthermore, the prediction performance was comparatively analyzed, demonstrating the excellent performance of the proposed CNN-LSTM-based prediction model.

Highlights

Mohamed BenbouzidIn recent years, the problems of global warming, environmental pollution, and depletion of natural resources caused by the use of fossil fuel-based energy, and the safety problem of nuclear energy have triggered the need for an alternative and permanent energy source, drawing attention toward renewable energy sources such as ocean energy, solar energy, and wind power
The results showed that a time delay and deviation occurred because of the distance of 1 km between the Yongsu wave power plants (WPPs) and wave height metermeter (WHM), making it difficult to utilize the wave height data for the target WPP in this study
To verify the utility of the proposed pressure prediction model based on convolutional neural networks (CNNs)-long shortterm memory (LSTM), tests were performed using linear regression (LR) and a machine learning algorithm

Summary

Introduction

The problems of global warming, environmental pollution, and depletion of natural resources caused by the use of fossil fuel-based energy, and the safety problem of nuclear energy have triggered the need for an alternative and permanent energy source, drawing attention toward renewable energy sources such as ocean energy, solar energy, and wind power Against this backdrop, the South Korean government, in line with its incentives and planned electric power target, has announced its long-term plan to increase the proportion of renewable energy, which is currently 6% of the total power generation portfolio, to at least 20% by 2030 and 30% by 2040 [1]. Structures for wave energy converters (WECs) can be classified into three categories according to the conversion method of kinetic energy from ocean waves: (i) overtopping devices, (ii) wave-activated bodies, and (iii) oscillating water columns (OWC).

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