Predictive analysis of RNN, GBM and LSTM network for short-term wind power forecasting-Reference-Cited by-同舟云学术

Predictive analysis of RNN, GBM and LSTM network for short-term wind power forecasting

Published:2020-01-02 Issue:1 Volume:23 Page:33-47
ISSN:0972-0510
Container-title:Journal of Statistics and Management Systems
language:en
Short-container-title:Journal of Statistics and Management Systems

Author:

Srivastava Tushar¹,Vedanshu ²,Tripathi M. M.²

Affiliation:

1. Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India

2. Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India,

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09720510.2020.1723224

Reference16 articles.

1. G. Batres-Estrada, “Deep learning for multivariate financial time series,” ser. Technical Report, Stockholm, May 2015.

2. Paolo Burlando aRenzoRosso aLuis G. CadavidbJose D. Salasb: Forecasting of short-term rainfall using ARMA models, vol. 144, issue 1-4, April 1993, pages 193-211

3. Adebile, Olukayode, Shangodoyin, Kehinde, Arnab Raghunath: Forecasting performance of logistic STAR model: an alternative version to the original LSTAR models, vol. 3, MASA. Model Assisted Statistics and Applications,2018/12/16.

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