Liquefied Natural Gas and Hydrogen Regasification Terminal Design through Neural Network Estimated Demand for the Canary Islands
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Alejandro J. del Real & Fernando Dorado & Jaime Durán, 2020. "Energy Demand Forecasting Using Deep Learning: Applications for the French Grid," Energies, MDPI, vol. 13(9), pages 1-15, May.
- Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
- Geng, Zhiqiang & Zhang, Yanhui & Li, Chengfei & Han, Yongming & Cui, Yunfei & Yu, Bin, 2020. "Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature," Energy, Elsevier, vol. 194(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Han, Yongming & Liu, Shuang & Cong, Di & Geng, Zhiqiang & Fan, Jinzhen & Gao, Jingyang & Pan, Tingrui, 2021. "Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes," Energy, Elsevier, vol. 225(C).
- Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
- Wu, Yixi & Wang, Ziqi & Shi, Chenli & Jin, Xiaohang & Xu, Zhengguo, 2024. "A novel data-driven approach for coal-fired boiler under deep peak shaving to predict and optimize NOx emission and heat exchange performance," Energy, Elsevier, vol. 304(C).
- Bhandari, Ramchandra & Subedi, Subodh, 2023. "Evaluation of surplus hydroelectricity potential in Nepal until 2040 and its use for hydrogen production via electrolysis," Renewable Energy, Elsevier, vol. 212(C), pages 403-414.
- Zou, Songchun & Zhao, Wanzhong, 2020. "Energy optimization strategy of vehicle DCS system based on APSO algorithm," Energy, Elsevier, vol. 208(C).
- Manuel Jaramillo & Diego Carrión, 2022. "An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19," Energies, MDPI, vol. 15(22), pages 1-19, November.
- Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
- Han, Yongming & Wu, Hao & Geng, Zhiqiang & Zhu, Qunxiong & Gu, Xiangbai & Yu, Bin, 2020. "Review: Energy efficiency evaluation of complex petrochemical industries," Energy, Elsevier, vol. 203(C).
- Mafakheri, Aso & Sulaimany, Sadegh & Mohammadi, Sara, 2023. "Predicting the establishment and removal of global trade relations for import and export of petrochemical products," Energy, Elsevier, vol. 269(C).
- Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
- Bibi Ibrahim & Luis Rabelo & Edgar Gutierrez-Franco & Nicolas Clavijo-Buritica, 2022. "Machine Learning for Short-Term Load Forecasting in Smart Grids," Energies, MDPI, vol. 15(21), pages 1-19, October.
- Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
- Gwanggil Jeon, 2022. "Artificial Intelligence Approaches for Energies," Energies, MDPI, vol. 15(18), pages 1-3, September.
- Duong Trung Kien & Phan Dieu Huong & Nguyen Dat Minh, 2023. "Application of Sarima Model in Load Forecasting in Hanoi City," International Journal of Energy Economics and Policy, Econjournals, vol. 13(3), pages 164-170, May.
- Anna Manowska & Anna Bluszcz, 2022. "Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network," Energies, MDPI, vol. 15(13), pages 1-23, July.
- Khondaker Golam Moazzem & Helen Mashiyat Preoty, 2021. "Proposed Power and Energy System Master Plan (PESMP): Perspective on Analytical Frame, Methodology and Influencing Factors on Demand Forecasting," CPD Working Paper 139, Centre for Policy Dialogue (CPD).
- Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
- Dana-Mihaela Petroșanu & Alexandru Pîrjan, 2020. "Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network," Sustainability, MDPI, vol. 13(1), pages 1-31, December.
- Miroslav Variny & Kristián Hanus & Marek Blahušiak & Patrik Furda & Peter Illés & Ján Janošovský, 2021. "Energy and Environmental Assessment of Steam Management Optimization in an Ethylene Plant," IJERPH, MDPI, vol. 18(22), pages 1-17, November.
- do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
More about this item
Keywords
LNG regasification terminal; hydrogen; LOHC; neural network; modeling; energy demand forecast;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8682-:d:977554. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.