Spatio-temporal independent applicability of one time trained machine learning wind forecast models: a promising case study from the wind energy perspective-Reference-Cited by-同舟云学术

Spatio-temporal independent applicability of one time trained machine learning wind forecast models: a promising case study from the wind energy perspective

Published:2022-02-02 Issue:9 Volume:41 Page:1164-1182
ISSN:1478-6451
Container-title:International Journal of Sustainable Energy
language:en
Short-container-title:International Journal of Sustainable Energy

Author:

Valsaraj P.¹²^ORCID,Alex Thumba Drisya³^ORCID,Satheesh Kumar K.¹^ORCID

Affiliation:

1. Department of Futures Studies, University of Kerala, Kariavattom, India

2. Agency for New and Renewable Energy Research and Technology (ANERT), Thiruvananthapuram, India

3. Mærsk Mc-Kinney Møller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark

Publisher

Informa UK Limited

Subject

Fluid Flow and Transfer Processes,Process Chemistry and Technology,General Energy,Fuel Technology,Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/14786451.2022.2032060

Reference49 articles.

1. Improved very short-term spatio-temporal wind forecasting using atmospheric regimes

2. Wind speed reconstruction using a novel Multivariate Probabilistic method and Multiple Linear Regression: advantages compared to the single correlation approach

3. Support-vector networks

4. A review on the young history of the wind power short-term prediction

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