Skip to main content

ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Solar Energy
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1500190

Multilevel Stacked Deep Learning Assisted Techno-Economic Assessment of Hybrid Renewable Energy System

Provisionally accepted
Mantosh Kumar Mantosh Kumar 1Kumari Namrata Kumari Namrata 1Akshit Samadhiya Akshit Samadhiya 2*Nishant Kumar Nishant Kumar 3Ahmad Taher Azar Ahmad Taher Azar 4,5,6*Nashwa A. Kamal Nashwa A. Kamal 7Ibrahim A. Hameed Ibrahim A. Hameed 8*
  • 1 National Institute of Technology, Jamshedpur, Jamshedpur, Jharkhand, India
  • 2 School of Engineering and Technology, Sandip University, Nashik, India
  • 3 B.K. Birla Institute of Engineering and Technology, Pilani, India
  • 4 College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
  • 5 Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia
  • 6 Faculty of Computer and Artificial Intelligence, Benha University, Benha, Egypt
  • 7 Faculty of Engineering, Cairo University, Giza, Egypt
  • 8 Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Alesund, Norway

The final, formatted version of the article will be published soon.

    The growing energy demand and target for net zero emission compelling the world to increase the percentage of clean energy sources which are freely available and abundant in nature. To fulfil this, a hyperparametric tuned multilevel deep learning stacked model assisted grid-connected hybrid renewable energy system (HRES) has been developed. The proposed system has been subjected to techno-economic assessment with a novel application of the rime-ice (RIME) optimization algorithm to determine the lowest possible cost of electricity (COE) corresponding to the best HRES system components. The analysis has been carried out for the residents of the eastern part of India. The results show that the prediction accuracy of the solar irradiance and wind speed are 95.92% and 95.80% respectively which have been used as inputs for the HRES. The proposed optimization used has shown the lowest COE of Rs. 4.65 per kWh and total net present cost (TNPC) of 7247 million INR with a renewable factor of 87.88% as compared to other optimizations like GWO, MFO and PSO. Further sensitivity analysis and power flow analysis for three consecutive days carried out have also been done to check the reliability of the HRES and its future perceptiveness.

    Keywords: deep learning, stacking, Forecasting, optimization, Cost of electricity (COE), Total Net Present Cost (TNPC)

    Received: 22 Sep 2024; Accepted: 24 Oct 2024.

    Copyright: © 2024 Kumar, Namrata, Samadhiya, Kumar, Azar, Kamal and Hameed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Akshit Samadhiya, School of Engineering and Technology, Sandip University, Nashik, India
    Ahmad Taher Azar, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
    Ibrahim A. Hameed, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Alesund, Norway

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.