Stacked LSTM for Solar Irradiance Forecasting Using Minimum Redundancy Maximum Relevance (MRMR) Feature Selection-Reference-Cited by-同舟云学术

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Stacked LSTM for Solar Irradiance Forecasting Using Minimum Redundancy Maximum Relevance (MRMR) Feature Selection

Published:2024-04-11 Issue: Volume:15 Page:1-6
ISSN:
Container-title:2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST)
language:
Short-container-title:

Author:

Hari Ninu G¹^ORCID,G Jisha²^ORCID

Affiliation:

1. Rajagiri School of Engineering and Technology, APJ Abdul Kalam Technological University,Department of Computer Science and Engineering,Kerala,India

2. Rajagiri School of Engineering and Technology,Department of Computer Science and Engineering,Kochi,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10576062/10576051/10576087.pdf?arnumber=10576087

Reference24 articles.

1. Global Greenhouse Gas Emissions Data | US EPA,2023

2. A review on global solar energy policy

3. Analysis of ANN-based daily global horizontal irradiance prediction models with different meteorological parameters: a case study of mountainous region of India

4. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization

5. Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms

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