Probabilistic Wind Power Forecasting Using Optimized Deep Auto-Regressive Recurrent Neural Networks-Reference-Cited by-同舟云学术

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Probabilistic Wind Power Forecasting Using Optimized Deep Auto-Regressive Recurrent Neural Networks

Published:2023-03 Issue:3 Volume:19 Page:2814-2825
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Arora Parul¹^ORCID,Jalali Seyed Mohammad Jafar²^ORCID,Ahmadian Sajad³^ORCID,Panigrahi B. K.¹,Suganthan P. N.⁴^ORCID,Khosravi Abbas²^ORCID

Affiliation:

1. Department of Electrical Engineering, Indian Institute of Technology, Delhi, India

2. Institute for Intelligent Systems Research and Innovation, Deakin University, Highton, VIC, Australia

3. Kermanshah University of Technology, Kermanshah, Iran

4. School of Electrical Electronic Engineering, Nanyang Technological University, Singapore

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10064202/09739990.pdf?arnumber=9739990

Reference43 articles.

1. Toward Nonlinear Local Reinforcement Learning Rules Through Neuroevolution

2. Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

3. Stochastic Gradient Descent for Nonconvex Learning Without Bounded Gradient Assumptions

4. Multi-Source and Temporal Attention Network for Probabilistic Wind Power Prediction

5. From evolutionary computation to the evolution of things

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