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

Author

Listed:
  • Dongwoo Seo

    (Korea Institute of Science and Technology Information (KISTI), Yuseong-gu, Daejeon 34141, Korea)

  • Taesang Huh

    (Korea Institute of Science and Technology Information (KISTI), Yuseong-gu, Daejeon 34141, Korea)

  • Myungil Kim

    (Korea Institute of Science and Technology Information (KISTI), Yuseong-gu, Daejeon 34141, Korea)

  • Jaesoon Hwang

    (Korea Institute of Science and Technology Information (KISTI), Yuseong-gu, Daejeon 34141, Korea)

  • Daeyong Jung

    (Korea Institute of Science and Technology Information (KISTI), Yuseong-gu, Daejeon 34141, Korea)

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.

Suggested Citation

  • Dongwoo Seo & Taesang Huh & Myungil Kim & Jaesoon Hwang & Daeyong Jung, 2021. "Prediction of Air Pressure Change Inside the Chamber of an Oscillating Water Column–Wave Energy Converter Using Machine-Learning in Big Data Platform," Energies, MDPI, vol. 14(11), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:2982-:d:559240
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    References listed on IDEAS

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    1. Raúl Cascajo & Emilio García & Eduardo Quiles & Antonio Correcher & Francisco Morant, 2019. "Integration of Marine Wave Energy Converters into Seaports: A Case Study in the Port of Valencia," Energies, MDPI, vol. 12(5), pages 1-24, February.
    2. Eunjeong Choi & Soohwan Cho & Dong Keun Kim, 2020. "Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability," Sustainability, MDPI, vol. 12(3), pages 1-14, February.
    3. Chenhua Ni & Xiandong Ma, 2018. "Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs," Energies, MDPI, vol. 11(8), pages 1-18, August.
    4. Kharati-Koopaee, Masoud & Fathi-Kelestani, Arman, 2020. "Assessment of oscillating water column performance: Influence of wave steepness at various chamber lengths and bottom slopes," Renewable Energy, Elsevier, vol. 147(P1), pages 1595-1608.
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