Optimal Configuration Planning of Multi-Energy Systems using Optimization-based Deep Learning Technique-Reference-Cited by-同舟云学术

Optimal Configuration Planning of Multi-Energy Systems using Optimization-based Deep Learning Technique

Published:2023-04-20 Issue:14 Volume:51 Page:1506-1521
ISSN:1532-5008
Container-title:Electric Power Components and Systems
language:en
Short-container-title:Electric Power Components and Systems

Author:

Dimlo U. M. Fernandes¹,Umanesan R.²,Narasimharao Jonnadula³,Senthamilarasi N.⁴,Ranjit P. S.⁵,Balaji B.⁶,Thamarai I.⁷,Dwivedi Vijay Kumar⁸

Affiliation:

1. Department of Computer Science and Engineering, Sreyas Institute of Engineering and Technology, Nagole, Hyderabad, Telangana, India

2. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology, Chennai, Tamilnadu, India

3. Department of Computer Science and Engineering, CMR Technical Campus, Medchal, Hyderabad, Telangana, India

4. Department of Information Technology, Panimalar Engineering College, Chennai, Tamilnadu, India

5. Department of Mechanical Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh, India

6. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India

7. Department of Computer Science and Engineering, Panimalar Engineering College Chennai City Campus, Chennai, Tamilnadu, India

8. Department of Mathematics, VEC Ambikapur, Surguja, Chhattisgarh, India

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/15325008.2023.2199750

Reference29 articles.

1. Energy Pattern Classification and Prediction in an Educational Institution using Deep Learning Framework

2. Real Time Demand Response Modeling for Residential Consumers in Smart Grid Considering Renewable Energy With Deep Learning Approach

3. Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning

4. Short-Term forecasting of photovoltaic power in an isolated area of Ecuador using deep learning techniques

5. Day-ahead Multiple Households Load Forecasting using Deep Learning and Unsupervised Clustering