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
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