Yaoran Chen

TL;DR: Analytical results show that, when compared with the traditional mainstream models, the strategy of using the sequences provided by the signal decomposition technology as the input features can significantly improve the prediction accuracy.

TL;DR: A novel deep learning model was proposed for a 2-D regional wind speed forecast, using the combination of the auto-encoder of convolutional neural network and the long short-term memory unit (LSTM), revealing that the current model can not only offer an accurateWind speed forecast along timeline, but also give a distinct estimation of the spatial wind speed distribution in 2- D wind farm.

TL;DR: In this article, the feasibility of the Reynolds-Averaged Navier-Stokes SST k - ω turbulence model applied on the VAWT was verified against available experiments at first.

TL;DR: In this paper, the authors investigate the wake characteristics of an H-rotor floating vertical axis wind turbine under the platform's pitch and surge motions and compare the wake profiles and structures between pitch and non-pitch conditions.

TL;DR: In this article, a performance optimization of the shape of the Darrieus wind turbine with a given range of inlet wind speed is carried out by involving a heuristic search algorithm, Covariance Matrix Adaptation Evolutionary Strategy (CMAES), into Double Multiple Streamtube model (DMST).