Smart Deep Learning Model to Recognize PCM Optimization Performance on Solar Cooling System-Reference-Cited by-同舟云学术

Smart Deep Learning Model to Recognize PCM Optimization Performance on Solar Cooling System

Published:2023-11-24 Issue: Volume: Page:1-12
ISSN:1532-5008
Container-title:Electric Power Components and Systems
language:en
Short-container-title:Electric Power Components and Systems

Author:

Prasanna K. Mahesh¹,Francis Sheeja V.²,Kumara Varuna³,Sagai Francis Britto A.⁴,Nageswara Rao G.⁵,Ara Tabassum⁶,Ganeshan P.⁷,Ifseisi Ahmad A.⁸,Aftab Sikandar⁹

Affiliation:

1. Department of Electronics and Communication Engineering, Vivekananda College of Engineering and Technology, Puttur, Karnataka, India

2. Department of Electronics & Communication Engineering, Jerusalem College of Engineering, Chennai, Tamil Nadu, India

3. Department of Artificial Intelligence and Machine Learning, Moodlakatte Institute of Technology, Kundapura, Karnataka, India

4. Department Of Mechanical Engineering, Rohini College of Engineering And Technology, Palkulam, Anjugramam, Tamil Nadu, India

5. Department of Electrical and Electronics Engineering, Lakireddy Bali Reddy College of Engineering, JNTUK Kakinada, Kakinada, Andhra Pradesh, India

6. Department of Artificial Intelligence and Machine Learning, HKBK College of Engineering, Bengaluru, Karnataka, India

7. Department of Mechanical Engineering, Sri Eshwar Engineering College, Coimbatore, Tamilnadu, India

8. Department of Chemistry, College of Science, King Saud University, Riyadh, Saudi Arabia

9. Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, South Korea

Funder

King Saud University, Riyadh, Saudi Arabia

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Energy Engineering and Power Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/15325008.2023.2281630

Reference37 articles.

1. Demand response flexibility with synergies on passive PCM walls, BIPVs, and active air-conditioning system in a subtropical climate

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4. Evaluation of machine learning-based applications in forecasting the performance of single effect absorption chiller network

5. Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System