default author photo

Xiaohan Li

affiliation not provided to SSRN

  • affiliation not provided to SSRN

SCHOLARLY PAPERS

2

DOWNLOADS

89

TOTAL CITATIONS

1

Scholarly Papers (2)

Actions:
Email selected abstracts
1.

Novel Ecological Suitability Evaluation Perspective for Large-Scale Wind Power Construction Zoning and Geographical Potential Prediction:An Explainable Artificial Intelligence-Based Approach

Number of pages: 57 Posted: 01 Jan 2025
Liting Wang, Ruijia Zhang, Bingran Ma, Junwei Ma, Lixiao Zhang, Huaiwu Peng, Weihua Zeng, John Kaiser Calautit, Naijing Liu, Xingyong Li and Xiaohan Li
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, Beijing Normal University (BNU), affiliation not provided to SSRN, affiliation not provided to SSRN, University of Sheffield - Department of Mechanical Engineering, affiliation not provided to SSRN, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 54 (1,020,567)

Abstract:

Loading...

Geographical Wind Potential, Nationally Determined Contributions, Qinghai-Tibet Plateau, Wind Power Generation, Ecological Suitability Zoning

2.

Revealing the Theoretical Wind Potential of the Qinghai-Tibet Plateau: A Novel Bayesian Monte-Carlo Framework for the Weibull Bivariate Distribution

Number of pages: 51 Posted: 27 Jun 2024
Liting Wang, Renzhi Liu, Weihua Zeng, Lixiao Zhang, Huaiwu Peng, John Kaiser Calautit, Bingran Ma, Ruijia Zhang, Xiyao Ma and Xiaohan Li
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, Beijing Normal University (BNU), affiliation not provided to SSRN, University of Sheffield - Department of Mechanical Engineering, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 35 (1,249,711)
Citation 1

Abstract:

Loading...

Theoretical Wind Potential, Hierarchical Bayesian, Qinghai-Tibet Plateau, Wind Power Generation