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.

KISTI was an official partner of EGEE, participating in many grid-related activities ranging from grid operation and maintenance to grid development activities. The EGEE project ended in April 2010 and some new Grid-related projects were launched. In a continuing effort to promote European Grid projects in Korea, KISTI has joined both the European Grid Initiative (EGI) and the European Middleware Initiative (EMI) as an official partner. Within EGI, KISTI is supposed to continue to provide a stable and reliable grid operation and production grid service as a member of Asia-Pacific Grid Initiative (APGI). KISTI has been part of the ALICE Computing Grid as a Tier2 since 2006, providing a stable and reliable node in the ALICE Grid. KISTI serves as one of the official EMI product teams, responsible for the maintenance and evolution of the AMGA software. Under the framework of EMI, in order to improve the usability of AMGA for user community, we have developed the AMGA GUI clients as an Eclipse RCP application released as part of the 2.1 version of AMGA in November 2010. We are now closely working with the other EMI product teams towards the release of EMI-1 scheduled for April 2011. KISTI has been keen on the seamless aggregation and use of heterogeneous distributed computing infrastructures (HDCI) for enabling large-scale science that would otherwise be impossible to address. To this end, we have developed some Ganga plug-ins for having access to different distributed infrastructures including Grids, Clouds, and Supercomputers as one of integral parts of our approach to HTC service.