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, Taesang Huh, Myungil Kim, Jaesoon Hwang and Daeyong Jung
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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

Energies, 2021, vol. 14, issue 11, 1-17

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.

Keywords: oscillating water column; wave energy converter; machine-learning; pressure prediction model; big data platform; HPC cloud (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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