G
Gong Li
Researcher at Yanshan University
Publications - 68
Citations - 2330
Gong Li is an academic researcher from Yanshan University. The author has contributed to research in topics: Amorphous metal & Alloy. The author has an hindex of 15, co-authored 62 publications receiving 1968 citations. Previous affiliations of Gong Li include North Dakota State University.
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On comparing three artificial neural networks for wind speed forecasting
TL;DR: A comprehensive comparison study on the application of different artificial neural networks in 1-h-ahead wind speed forecasting shows that even for the same wind dataset, no single neural network model outperforms others universally in terms of all evaluation metrics.
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Fine tuning support vector machines for short-term wind speed forecasting
Junyi Zhou,Jing Shi,Gong Li +2 more
TL;DR: For the first time, a systematic study on fine tuning of LS-SVM model parameters for one-step ahead wind speed forecasting is presented and it is found that they can outperform the persistence model in the majority of cases.
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Bayesian adaptive combination of short-term wind speed forecasts from neural network models
Gong Li,Jing Shi,Junyi Zhou +2 more
TL;DR: A robust two-step methodology for accurate wind speed forecasting based on Bayesian combination algorithm, and three neural network models, namely, adaptive linear element network (ADALINE), backpropagation (BP) network, and radial basis function (RBF) network is presented.
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Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review
TL;DR: In this paper, the authors present a comprehensive literature analysis on the state-of-the-art research of bidding strategy modeling methods, including game theory, mathematical programming, game theory and agent-based models.
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Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites
TL;DR: In this paper, a comprehensive evaluation on probability density functions for the wind speed data from five representative sites in North Dakota is presented, including gamma, lognormal, inverse Gaussian, and maximum entropy principle (MEP) derived probability density function (PDFs).