A widely applicable and robust LightGBM - Artificial neural network forecasting model for short-term wind power density-Reference-Cited by-同舟云学术

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A widely applicable and robust LightGBM - Artificial neural network forecasting model for short-term wind power density

Published:2023-12 Issue:12 Volume:9 Page:e23071
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Zeng Xiangrui^ORCID,Abdullah Nibras^ORCID,Liang Baixue

Publisher

Elsevier BV

Subject

Multidisciplinary

Reference58 articles.

1. Carbon Neutrality in the UNECE Region: Integrated Life-Cycle Assessment of Electricity Sources,2022

2. Life cycle assessment of two different 2 MW class wind turbines;Guezuraga;Renew. Energy,2012

3. Life cycle assessment of CO2 emissions from wind power plants: methodology and case studies;Wang;Renew. Energy,2012

4. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market;Fabbri;IEEE Trans. Power Syst.,2005

5. A review of wind power and wind speed forecasting methods with different time horizons;Soman,2010

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1. New Energy Vehicle Customer Mining Model Based on Machine Learning Algorithm;2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE);2024-04-26

2. Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks;Chemosphere;2024-03

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