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.

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Ultra-Short-Term Wind Power Prediction Method Based on Quadratic Decomposition and Multi-Objective Optimization;EAI Endorsed Transactions on Energy Web;2024-04-15

2. Wind Forecasting based on CEL-LSTM Hybrid Model for German Farm;2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT);2024-03-29

3. Envisioning India's Energy Future: Predictive Models for Power Generation Trends;2024 International Conference on Emerging Smart Computing and Informatics (ESCI);2024-03-05

4. Green energy forecasting using multiheaded convolutional LSTM model for sustainable life;Sustainable Energy Technologies and Assessments;2024-03

5. Wind Power Forecast Based on Multi-Source Data and RNN: A Case Study of Jiangsu;2023 10th International Forum on Electrical Engineering and Automation (IFEEA);2023-11-03