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Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models

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  • Pala, Zeydin

Abstract

Accurate natural gas consumption forecasting plays a significant role in production, supply, and dispatching. Therefore, in this study, a new multi-hybrid model methodology is proposed that combines both statistical and deep learning models to obtain better prediction results beyond individual models or constrained hybrid models in linear and non-linear modeling. Here, long-term natural gas consumption future forecast analyzes were performed for the USA natural gas vehicle fuel (NG-VFC) dataset from January 1997 to October 2021 and for the USA natural gas industrial consumption (NG-IC) dataset between January 2001 and October 2021.

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  • Pala, Zeydin, 2023. "Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models," Energy, Elsevier, vol. 263(PC).
  • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222027128
    DOI: 10.1016/j.energy.2022.125826
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    as
    1. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    2. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
    3. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
    4. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    5. Azam, Anam & Rafiq, Muhammad & Shafique, Muhammad & Zhang, Haonan & Yuan, Jiahai, 2021. "Analyzing the effect of natural gas, nuclear energy and renewable energy on GDP and carbon emissions: A multi-variate panel data analysis," Energy, Elsevier, vol. 219(C).
    6. Rehman, Aniqa & Zhu, Jun-Jie & Segovia, Javier & Anderson, Paul R., 2022. "Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain," Energy, Elsevier, vol. 244(PA).
    7. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    8. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    9. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    10. Li, Nu & Wang, Jianliang & Wu, Lifeng & Bentley, Yongmei, 2021. "Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization," Energy, Elsevier, vol. 215(PA).
    11. Potočnik, Primož & Soldo, Božidar & Šimunović, Goran & Šarić, Tomislav & Jeromen, Andrej & Govekar, Edvard, 2014. "Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia," Applied Energy, Elsevier, vol. 129(C), pages 94-103.
    12. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    13. M. E. Malliaris & S. G. Malliaris, 2008. "Forecasting inter-related energy product prices," The European Journal of Finance, Taylor & Francis Journals, vol. 14(6), pages 453-468.
    14. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
    15. Svoboda, Radek & Kotik, Vojtech & Platos, Jan, 2021. "Short-term natural gas consumption forecasting from long-term data collection," Energy, Elsevier, vol. 218(C).
    16. Li, Jinchao & Wu, Qianqian & Tian, Yu & Fan, Liguo, 2021. "Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network," Energy, Elsevier, vol. 227(C).
    17. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Lu, Xinyi & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Shahzad, Khurram & Rashid, Muhammad Imtiaz & Ali, Arshid Mahmood & Liao, Qi & Wang, Bohong, 2022. "A hybrid deep learning framework for predicting daily natural gas consumption," Energy, Elsevier, vol. 257(C).
    18. Wang, Zheng-Xin & He, Ling-Yang & Zheng, Hong-Hao, 2019. "Forecasting the residential solar energy consumption of the United States," Energy, Elsevier, vol. 178(C), pages 610-623.
    19. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2021. "Forecasting natural gas prices using highly flexible time-varying parameter models," Economic Modelling, Elsevier, vol. 105(C).
    20. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    21. Burns, Kelly, 2021. "An investigation into changes in the elasticity of U.S. residential natural gas consumption: A time-varying approach," Energy Economics, Elsevier, vol. 99(C).
    22. Shaub, David, 2020. "Fast and accurate yearly time series forecasting with forecast combinations," International Journal of Forecasting, Elsevier, vol. 36(1), pages 116-120.
    23. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    24. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
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