Optimizing the deep learning parameters using metaheuristic algorithms for PV-battery-based DC nanogrids-Reference-Cited by-同舟云学术

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Optimizing the deep learning parameters using metaheuristic algorithms for PV-battery-based DC nanogrids

Published:2024-06-03 Issue:1 Volume:45 Page:
ISSN:0143-0750
Container-title:International Journal of Ambient Energy
language:en
Short-container-title:International Journal of Ambient Energy

Author:

Sulaiman Mohd Herwan¹^ORCID,Mustaffa Zuriani²^ORCID,Seyyedabbasi Amir³^ORCID,Irawan Addie¹^ORCID

Affiliation:

1. Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan Pahang, Malaysia

2. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan Pahang, Malaysia

3. Software Engineering Department, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey

Funder

Ministry of Higher Education, Malaysia

Universiti Malaysia Pahang

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01430750.2024.2358068

Reference37 articles.

1. Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review

2. Deep Learning-Aided Sensorless Control Approach for PV Converters in DC Nanogrids

3. Sensorless Control of DC Microgrid Based on Artificial Intelligence

4. Modelling of an off-grid roof-top residential photovoltaic nano grid system for an urban locality in India

5. Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

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