Forecasting high penetration of solar and wind power in the smart grid environment using robust ensemble learning approach for large-dimensional data

Ahmad, Tanveer; Manzoor, Sohaib; Zhang, Dongdong

doi:10.1016/j.scs.2021.103269

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Forecasting high penetration of solar and wind power in the smart grid environment using robust ensemble learning approach for large-dimensional data

Author(s): Tanveer Ahmad , Sohaib Manzoor , Dongdong Zhang

Publication date Created: December 2021

Publication date (Print): December 2021

Journal: Sustainable Cities and Society

Publisher: Elsevier BV

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R. Ahmed, V. Sreeram, Y Mishra … (2020)

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Journal

Title: Sustainable Cities and Society

Abbreviated Title: Sustainable Cities and Society

Publisher: Elsevier BV

ISSN (Print): 22106707

Publication date Created: December 2021

Publication date (Print): December 2021

Volume: 75

Page: 103269

Article

DOI: 10.1016/j.scs.2021.103269

SO-VID: 65babcfb-1499-4cb7-83bd-c76d6d0d5dd1

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https://www.elsevier.com/tdm/userlicense/1.0/

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Forecasting high penetration of solar and wind power in the smart grid environment using robust ensemble learning approach for large-dimensional data

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Most cited references 54

A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer

A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization

Modeling and forecasting building energy consumption: A review of data-driven techniques

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