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Wave energy is a kind of clean energy that is rich in reserves and has not been exploited on a large scale. The slope-pendulum wave energy conversion (S-PWEC) device has been optimized in structure and its capture efficiency has been increased. Taking the selection of the Zhejiang sea area as the research background, this paper performs numerical simulation and array WEC experimental testing of S-PWEC under 66 major sea conditions. The experimental results show that S-PWEC adds a slope structure to the bottom, which can effectively improve the motion response ability and resistance to extreme sea conditions. In the regular wave and irregular wave tests, the electron power output efficiency can be increased by 13.24% and 10.06%, respectively; in the array WEC experiment, the diffraction effect and radiation effect will affect the work of the array WEC, and the optimal arrangement distance can be selected to maximize the power output of the WEC system. [ABSTRACT FROM AUTHOR]

Accurate and reliable tidal current forecasting plays a key role in application of grid-connected tidal current power generation systems. Hence, a novel tidal current prediction method based on a hybrid machine learning method is proposed in this paper. Compared with previous tidal current prediction method, the proposed tidal current prediction method considers the influence of turbulence on multi-layer tidal velocity difference. A hybrid machine learning method based on hierarchical extreme learning machine(H-ELM) and long short-term memory (LSTM) is adopted to reduce effect of the turbulent flow, which enables to increase the accuracy of the prediction result. The tidal data that collected by using acoustic doppler current profiler (ADCP) in Zhejiang province China is used to validate the effectiveness of the proposed method. The results of the case study show that the proposed method has higher accuracy than results from standard harmonic analysis using UTide (a freely available academic research product). • A novel tidal current prediction model considering the multilayer difference caused by the turbulent flow is proposed. • A hybrid machine learning method is adopted to reduce effect of the turbulent flow and increase the prediction accuracy. • Both H-ELM and LSTM methods are applied in the short-term tidal current velocity prediction. [ABSTRACT FROM AUTHOR]