Search: onr:"swepub:oai:DiVA.org:uu-491975" >
Towards carbon Neut...
-
Lv, Zhihan,Dr.1984-Uppsala universitet,Institutionen för speldesign,Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China;State Key Laboratory of Media Convergence Production Technology and Systems, Beijing, 100803, China
(author)
Towards carbon Neutrality : Prediction of wave energy based on improved GRU in Maritime transportation
- Article/chapterEnglish2023
Publisher, publication year, extent ...
-
Elsevier,2023
-
electronicrdacarrier
Numbers
-
LIBRIS-ID:oai:DiVA.org:uu-491975
-
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-491975URI
-
https://doi.org/10.1016/j.apenergy.2022.120394DOI
Supplementary language notes
-
Language:English
-
Summary in:English
Part of subdatabase
Classification
-
Subject category:ref swepub-contenttype
-
Subject category:art swepub-publicationtype
Notes
-
Efficient use of renewable energy is one of the critical measures to achieve carbon neutrality. Countries have introduced policies to put carbon neutrality on the agenda to achieve relatively zero emissions of greenhouse gases and to cope with the crisis brought about by global warming. This work analyzes the wave energy with high energy density and wide distribution based on understanding of various renewable energy sources. This study provides a wave energy prediction model for energy harvesting. At the same time, the Gated Recurrent Unit network (GRU), Bayesian optimization algorithm, and attention mechanism are introduced to improve the model's performance. Bayesian optimization methods are used to optimize hyperparameters throughout the model training, and attention mechanisms are used to assign different weights to features to increase the prediction accuracy. Finally, the 1-hour and 6-hour forecasts are made using the data from China's NJI and BSG observatories, and the system performance is analyzed. The results show that, compared with mainstream prediction algorithms, GRU based on Bayesian optimization and attention mechanism has the highest prediction accuracy, with the lowest MAE of 0.3686 and 0.8204, and the highest R2 of 0.9127 and 0.6436, respectively. Therefore, the prediction model proposed here can provide support and reference for the navigation of ships powered by wave energy.
Subject headings and genre
Added entries (persons, corporate bodies, meetings, titles ...)
-
Wang, Nana
(author)
-
Lou, Ranaran
(author)
-
Tian, Yajun
(author)
-
Guizani, Mohsen
(author)
-
Uppsala universitetInstitutionen för speldesign
(creator_code:org_t)
Related titles
-
In:Applied Energy: Elsevier3310306-26191872-9118
Internet link
Find in a library
To the university's database