Deep learning solar forecasting for green hydrogen production in India: A case study

被引:15
|
作者
Sareen, Karan [1 ]
Panigrahi, Bijaya Ketan [2 ]
Shikhola, Tushar [3 ]
Nagdeve, Rita [1 ]
机构
[1] Cent Elect Author CEA, Delhi 110066, India
[2] Indian Inst Technol Delhi IIT Delhi, Dept Elect Engn, Delhi 110016, India
[3] Delhi Metro Rail Corp Ltd DMRC, Delhi 110001, India
关键词
Solar irradiance; Green hydrogen production; Forecasting; Machine learning; Water electrolysis; Modelling simulation; ENERGY;
D O I
10.1016/j.ijhydene.2023.08.323
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Solar energy based green hydrogen production is dependent on energy produced from Photovoltaic (PV) panels that is in turn dependent on Global Horizontal Irradiance (GHI), which has stochastic and intermittent nature. The intermittent power output from PV panels may interfere with the steady production of electricity required for electrolysis process to synthesis the green hydrogen thereby; reducing the effectiveness and scalability of green hydrogen generation. Ultimate goal of this work will be to effectively forecast and develop the atlas map for solar based green hydrogen production potential using the proposed algorithm i.e. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Bidirectional Long Short-Term Memory (CEEMDAN-BiDLSTM). In order to suggest a reliable and precise forecasting methodology that will support India's low-carbon economy goals, the proposed method is evaluated on GHI datasets acquired via national portal of National Institute of Wind Energy (NIWE) for two Indian sites i.e. Bhadla and Fatehgarh.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:334 / 351
页数:18
相关论文
共 50 条
  • [41] Deep learning assisted solar forecasting for battery swapping stations
    Chawrasia, Sandeep Kumar
    Hembram, Debmalya
    Bose, Dipanjan
    Chanda, Chandan Kumar
    ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2024, 46 (01) : 3381 - 3402
  • [42] Time Series Forecasting on Solar Irradiation using Deep Learning
    Sorkun, Murat Cihan
    Paoli, Christophe
    Incel, Ozlem Durmaz
    2017 10TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ELECO), 2017, : 151 - 155
  • [43] A review of distributed solar forecasting with remote sensing and deep learning
    Chu, Yinghao
    Wang, Yiling
    Yang, Dazhi
    Chen, Shanlin
    Li, Mengying
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2024, 198
  • [44] Deep learning for very short term solar irradiation forecasting
    Bendali, Wadie
    Boussetta, Mohammed
    Bourachdi, Bensalem
    Saber, Ikram
    Mourad, Youssef
    Bossoufi, Bader
    2020 5TH INTERNATIONAL CONFERENCE ON RENEWABLE ENERGIES FOR DEVELOPING COUNTRIES (REDEC), 2020,
  • [45] Deep learning models for solar irradiance forecasting: A comprehensive review
    Kumari, Pratima
    Toshniwal, Durga
    JOURNAL OF CLEANER PRODUCTION, 2021, 318
  • [46] Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting
    Cordeiro-Costas, Moises
    Villanueva, Daniel
    Eguia-Oller, Pablo
    Granada-Alvarez, Enrique
    APPLIED SCIENCES-BASEL, 2022, 12 (17):
  • [47] Ensemble Forecasting Frame Based on Deep Learning and Multi-Objective Optimization for Planning Solar Energy Management: A Case Study
    Liu, Yongjiu
    Li, Li
    Zhou, Shenglin
    FRONTIERS IN ENERGY RESEARCH, 2021, 9
  • [48] Impact of Different Solar Trackers on Hydrogen Production: A Case Study in Iran
    Mostafaeipour, Ali
    Jahangiri, Mehdi
    Saghaei, Hamed
    Goojani, Afsaneh Raiesi
    Chowdhury, Md. Shahariar
    Techato, Kuaanan
    INTERNATIONAL JOURNAL OF PHOTOENERGY, 2022, 2022
  • [49] Impact of Different Solar Trackers on Hydrogen Production: A Case Study in Iran
    Mostafaeipour, Ali
    Jahangiri, Mehdi
    Saghaei, Hamed
    Raiesi Goojani, Afsaneh
    Chowdhury, Md. Shahariar
    Techato, Kuaanan
    International Journal of Photoenergy, 2022, 2022
  • [50] Green hydrogen for ammonia production - A case for the Netherlands
    Pagani, Gianni
    Hajimolana, Yashar
    Acar, Canan
    INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2024, 52 : 418 - 432