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Author

Biyi Cheng

Bio: Biyi Cheng is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Wind power & Support vector machine. The author has co-authored 1 publications.

Papers
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Journal ArticleDOI
16 Nov 2021-Energy
TL;DR: In this paper, the authors developed a bi-level structure design and optimization model based on the algorithms of machine learning for wind turbines, which can reach to the optimal combination of chord ratio, radius difference and offset angle, with an identical accuracy compared with the outcome of OT-CFD model.

8 citations

Journal ArticleDOI
TL;DR: In this paper , a power prediction formula CP=f(Δ,β,θ), containing radius difference Δ, chord ratio β, and offset angle θ, is proposed.

4 citations

Journal ArticleDOI
TL;DR: In this article , a novel U-type Darrieus wind turbine (UDWT) is proposed by using machine learning (ML) method based on BPNN and three optimization algorithms, GA, PSO and SA, are adopted.

4 citations

Journal ArticleDOI
TL;DR: In this paper , the authors divide the amorphization process into three parts: peel, disorder and steady state based on the simulation results of molecular dynamics (MD) to obtain a deep understanding of the transitions under different shear conditions.
Journal ArticleDOI
01 May 2023-Energy
TL;DR: In this article , a data-driven surrogated model based on Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gaussian process regression (GPR), and decision tree regression (DTR) is proposed to regress the experimental data of aerodynamic forces.

Cited by
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Journal ArticleDOI
23 Mar 2022-Energies
TL;DR: In this article , the performance of enhanced machine learning models to forecast univariate wind power time-series data is investigated, and the results reveal the benefit of considering lagged data and input variables to better forecast wind power.
Abstract: Wind power represents a promising source of renewable energies. Precise forecasting of wind power generation is crucial to mitigate the challenges of balancing supply and demand in the smart grid. Nevertheless, the major difficulty in wind power is its high fluctuation and intermittent nature, making it challenging to forecast. This study aims to develop efficient data-driven models to accurately forecast wind power generation. Crucially, the main contributions of this work are listed in the following major elements. Firstly, we investigate the performance of enhanced machine learning models to forecast univariate wind power time-series data. Specifically, we employed Bayesian optimization (BO) to optimally tune hyperparameters of the Gaussian process regression (GPR), Support Vector Regression (SVR) with different kernels, and ensemble learning (ES) models (i.e., Boosted trees and Bagged trees) and investigated their forecasting performance. Secondly, dynamic information has been incorporated in their construction to further enhance the forecasting performance of the investigated models. Specifically, we introduce lagged measurements to enable capturing time evolution into the design of the considered models. Furthermore, more input variables (e.g., wind speed and wind direction) are used to further improve wind prediction performance. Actual measurements from three wind turbines in France, Turkey, and Kaggle are used to verify the efficiency of the considered models. The results reveal the benefit of considering lagged data and input variables to better forecast wind power. The results also showed that the optimized GPR and ensemble models outperformed the other machine learning models.

22 citations

Journal ArticleDOI
01 May 2022-Energy
TL;DR: In this article , a back-propagation artificial neural network model for salt cavern construction prediction (BPANN-SCCP) is trained on the dataset, and a design parameter optimization method is devised to optimize 3 sets of design parameters from a million random ones.

5 citations

Journal ArticleDOI
TL;DR: In this article , a novel U-type Darrieus wind turbine (UDWT) is proposed by using machine learning (ML) method based on BPNN and three optimization algorithms, GA, PSO and SA, are adopted.

4 citations

Journal ArticleDOI
TL;DR: In this article , an inverse design framework using statistical tools and machine learning models is developed to design thin-walled cellular structures with the desired properties, which exhibited excellent structural properties with record high specific recovery stress.

3 citations

Journal ArticleDOI
TL;DR: In this paper , a novel optimization technique called the Artificial Bee Colony Algorithm based on Blade Element Momentum Theory (ABC-BEM) was developed and applied for the first time to design a small-scale wind turbine blade.

2 citations