Development of deep learning method for predicting DC power based on renewable solar energy and multi-parameters function-Reference-Cited by-同舟云学术

Development of deep learning method for predicting DC power based on renewable solar energy and multi-parameters function

Published:2023-04-08 Issue:21 Volume:35 Page:15273-15294
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Al-Janabi Samaher^ORCID,Al-Janabi Zainab

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-08480-6.pdf

Reference65 articles.

1. Al-Janabi S, Alkaim AF, Adel Z (2020) An Innovative synthesis of deep learning techniques (DCapsNet and DCOM) for generation electrical renewable energy from wind energy. Soft Comput 24:10943–10962. https://doi.org/10.1007/s00500-020-04905-9

2. Diezmartínez CV (2021) Clean energy transition in Mexico: policy recommendations for the deployment of energy storage technologies. Renew Sustain Energy Rev 135:87. https://doi.org/10.1016/j.rser.2020.110407

3. Al-Janabi S, Alkaim A, Al-Janabi E, Aljeboree A, Mustafa M (2021) Intelligent forecaster of concentrations (PM2.5, PM10, NO2, CO, O3, SO2) caused air pollution (IFCsAP). Neural Comput Appl. https://doi.org/10.1007/s00521-021-06067-7

4. Mahdi MA, Al-Janabi S (2020) A novel software to improve healthcare base on predictive analytics and mobile services for cloud data centers in lecture notes in networks and systems. Springer

5. Migdadi YKAA (2022) Identifying the best practices in hotel green supply chain management strategy: a global study. J Qual Assur Hosp Tour. https://doi.org/10.1080/1528008X.2022.2065657

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