Using an artificial neural network model for natural gas compositions forecasting
Author
Abstract
Suggested Citation
DOI: 10.1016/j.energy.2022.126001
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
- Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
- Rodríguez, Fermín & Florez-Tapia, Ane M. & Fontán, Luis & Galarza, Ainhoa, 2020. "Very short-term wind power density forecasting through artificial neural networks for microgrid control," Renewable Energy, Elsevier, vol. 145(C), pages 1517-1527.
- Szoplik, Jolanta, 2016. "Improving the natural gas transporting based on the steady state simulation results," Energy, Elsevier, vol. 109(C), pages 105-116.
- Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
- Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
- Szoplik, Jolanta & Stelmasińska, Paulina, 2019. "Analysis of gas network storage capacity for alternative fuels in Poland," Energy, Elsevier, vol. 172(C), pages 343-353.
- Ali S. Alghamdi, 2021. "Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio," Energies, MDPI, vol. 14(6), pages 1-18, March.
- Jiwon Park & Jungkeun Cho & Heewon Choi & Jungsoo Park, 2020. "Prediction of Reformed Gas Composition for Diesel Engines with a Reformed EGR System Using an Artificial Neural Network," Energies, MDPI, vol. 13(22), pages 1-17, November.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Huanying Liu & Yulin Liu & Changhao Wang & Yanling Song & Wei Jiang & Cuicui Li & Shouxin Zhang & Bingyuan Hong, 2023. "Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China," Energies, MDPI, vol. 16(11), pages 1-15, May.
- Bartłomiej Gaweł & Andrzej Paliński, 2024. "Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series," Energies, MDPI, vol. 17(2), pages 1-25, January.
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.- Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
- Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
- Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
- Mustafa Akpinar & M. Fatih Adak & Nejat Yumusak, 2017. "Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey," Energies, MDPI, vol. 10(6), pages 1-20, June.
- Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
- Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A D2C algorithm on the natural gas consumption and economic growth: Challenges faced by Germany and Japan," Energy, Elsevier, vol. 219(C).
- Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
- Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
- Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
- Mohsen Beigi & Hossein Beigi Harchegani & Mehdi Torki & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Jacek Dziwulski, 2022. "Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches," Sustainability, MDPI, vol. 14(5), pages 1-12, March.
- Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
- Zhu, Yongbin & Shi, Yajuan & Wang, Zheng, 2014. "How much CO2 emissions will be reduced through industrial structure change if China focuses on domestic rather than international welfare?," Energy, Elsevier, vol. 72(C), pages 168-179.
- Khaled, Mohamed & Ibrahim, Mostafa M. & Abdel Hamed, Hesham E. & AbdelGwad, Ahmed F., 2019. "Investigation of a small Horizontal–Axis wind turbine performance with and without winglet," Energy, Elsevier, vol. 187(C).
- Li, Wei & Lu, Can, 2019. "The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China," Applied Energy, Elsevier, vol. 235(C), pages 685-698.
- Satrio Mukti Wibowo & Dedi Budiman Hakim & Baba Barus & Akhmad Fauzi, 2022. "Estimation of Energy Demand in Indonesia using Artificial Neural Network," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 261-271, November.
- Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
- Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
- Soltanisarvestani, A. & Safavi, A.A., 2021. "Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map," Utilities Policy, Elsevier, vol. 72(C).
- Andrés Ruiz & Florin Onea & Eugen Rusu, 2020. "Study Concerning the Expected Dynamics of the Wind Energy Resources in the Iberian Nearshore," Energies, MDPI, vol. 13(18), pages 1-25, September.
- Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).
More about this item
Keywords
Natural gas composition variability; Natural gas compositions forecasting; Artificial neural network model; Risk analysis;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:eee:energy:v:263:y:2023:i:pd:s0360544222028870. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.