Deep Learning Model with Probability Density Function and Feature Engineering for Short Term Wind Speed Prediction-Reference-Cited by-同舟云学术

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Deep Learning Model with Probability Density Function and Feature Engineering for Short Term Wind Speed Prediction

Published:2022-10-09 Issue: Volume: Page:
ISSN:
Container-title:2022 North American Power Symposium (NAPS)
language:
Short-container-title:

Author:

Dhakal Rabin¹,Yosfovand Mohammad¹,Prasai Sudip²,Sedai Ashish³,Pol Suhas³,Parameswaran Siva⁴,Moussa Hanna⁴

Affiliation:

1. Texas Tech University,Department of Mechanical Engineering,Lubbock,TX,USA

2. Logic Information System Minneapolis MN, USA

3. National Wind Institute Texas Tech University. Lubbock TX, USA

4. Department of Mechanical Engineering Texas Tech University Lubbock TX, USA

Funder

National Oceanic and Atmospheric Administration

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10012119/10012131/10012249.pdf?arnumber=10012249

Reference25 articles.

1. Short-Term Wind Speed Forecasting for Power System Operations

2. Feasibility study of distributed wind energy generation in Jumla Nepal;dhakal;International Journal of Renewable Energy Research (IJRER),2020

3. Learning long-term dependencies with gradient descent is difficult

4. Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks

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