IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v46y2025i5p215-244.html

Forecasting Energy Commodity Prices Amidst Worldwide Energy Transitions Using Artificial Intelligence Models

Author

Listed:
  • Foued Hamouda
  • Nadia Arfaoui
  • Muhammad Abubakr Naeem

Abstract

This study investigates the effectiveness of forecasting energy commodity prices using Artificial Intelligence-based models that account for the transition to cleaner energy sources during periods of significant market instability, such as the COVID-19 pandemic and the Russia-Ukraine conflict. Employing the Nonlinear Auto-Regressive model with exogenous inputs (NARX) over a comprehensive daily dataset from 2006 to 2023, the results reveal that machine learning models incorporating global energy transition factors perform better than traditional ANN and XGBoost models. The findings also reveal that integrating nonlinear relationships and external factors such as policy changes, technological advancements, and geopolitical events outperform traditional forecasting methods. This approach captures the complex dynamics of energy markets during periods of instability. By providing a reliable forecasting framework, this study enhances the understanding of energy market behaviors amid global transitions and uncertainties, promoting more adaptive and sustainable approaches to energy management. JEL Classification : C53, Q41, Q54

Suggested Citation

  • Foued Hamouda & Nadia Arfaoui & Muhammad Abubakr Naeem, 2025. "Forecasting Energy Commodity Prices Amidst Worldwide Energy Transitions Using Artificial Intelligence Models," The Energy Journal, , vol. 46(5), pages 215-244, September.
    • Handle: RePEc:sae:enejou:v:46:y:2025:i:5:p:215-244
    DOI: 10.1177/01956574251340012
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/01956574251340012
    Download Restriction: no
    File URL: https://libkey.io/10.1177/01956574251340012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Guerra, K. & Welfle, A. & Gutiérrez-Alvarez, R. & Freer, M. & Ma, L. & Haro, P., 2024. "The role of energy storage in Great Britain's future power system: focus on hydrogen and biomass," Applied Energy, Elsevier, vol. 357(C).
    2. Karkowska, Renata & Urjasz, Szczepan, 2023. "How does the Russian-Ukrainian war change connectedness and hedging opportunities? Comparison between dirty and clean energy markets versus global stock indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    3. Zhang, Kefei & Cao, Hua & Thé, Jesse & Yu, Hesheng, 2022. "A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms," Applied Energy, Elsevier, vol. 306(PA).
    4. Zargar, Faisal Nazir & Mohnot, Rajesh & Hamouda, Foued & Arfaoui, Nadia, 2024. "Risk dynamics in energy transition: Evaluating downside risks and interconnectedness in fossil fuel and renewable energy markets," Resources Policy, Elsevier, vol. 92(C).
    5. repec:aen:journl:ej42-3-golombek is not listed on IDEAS
    6. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
    7. 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.
    8. Finn Roar Aune & Rolf Golombek, 2021. "Are Carbon Prices Redundant in the 2030 EU Climate and Energy Policy Package?," The Energy Journal, , vol. 42(3), pages 225-264, May.
    9. Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024. "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, vol. 67(PB).
    10. Li, Yuze & Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach," Energy Economics, Elsevier, vol. 95(C).
    11. Herrera, Gabriel Paes & Constantino, Michel & Tabak, Benjamin Miranda & Pistori, Hemerson & Su, Jen-Je & Naranpanawa, Athula, 2019. "Long-term forecast of energy commodities price using machine learning," Energy, Elsevier, vol. 179(C), pages 214-221.
    12. Michael Plante & Grant Strickler, 2021. "Closer to One Great Pool? Evidence from Structural Breaks inOil Price Differentials," The Energy Journal, , vol. 42(2), pages 1-30, March.
    13. repec:aen:journl:ej42-2-plante is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    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.
    1. Tianxiang Hao & Qingfu Liu & Deyu Miao & Yiuman Tse, 2025. "Predicting Commodity Returns Through Image‐Based Price Patterns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(12), pages 2434-2456, December.
    2. Lin Wang & Lean Yu & Wuyue An, 2025. "Two‐Stream Reinforcement Ensemble Framework for Agricultural Commodity Prices Forecasting Using Textual Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(8), pages 2386-2404, December.
    3. Ewald, Christian Oliver & Li, Yaoyu, 2024. "The role of news sentiment in salmon price prediction using deep learning," Journal of Commodity Markets, Elsevier, vol. 36(C).
    4. Li, Jieyi & Qian, Shuangyue & Li, Ling & Guo, Yuanxuan & Wu, Jun & Tang, Ling, 2024. "A novel secondary decomposition method for forecasting crude oil price with twitter sentiment," Energy, Elsevier, vol. 290(C).
    5. Pan, Zhiyuan & Fu, Ziqian & Wang, Yudong & Dong, Qingma, 2024. "Exploiting the sentiments: A simple approach for improving cross hedging effectiveness," Energy Economics, Elsevier, vol. 134(C).
    6. Liu, Jinpei & Qiu, Biyue & Du, Pengcheng & Zhao, Xiaoman & Zhu, Jiaming, 2025. "A novel probabilistic connectivity network link prediction model for natural gas price based on an improved K-shell algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 671(C).
    7. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
    8. Hua, Xia & Dong, Dairui & Xu, Zhiwei & Huang, Wentao, 2025. "Official media sentiments toward energy and equity returns: Evidence from China," Energy, Elsevier, vol. 340(C).
    9. Wu, Siping & Xia, Guilin & Liu, Lang, 2023. "A novel decomposition integration model for power coal price forecasting," Resources Policy, Elsevier, vol. 80(C).
    10. Xu, Zhiwei & Gan, Shiqi & Hua, Xia & Xiong, Yujie, 2024. "Can the sentiment of the official media predict the return volatility of the Chinese crude oil futures?," Energy Economics, Elsevier, vol. 140(C).
    11. Nguyen, Bich Ngoc, 2025. "A sentiment-based approach to predict energy price volatility using distilRoBERTa and GARCH models," Energy Economics, Elsevier, vol. 149(C).
    12. Wu, Siping & Liu, Junjie & Liu, Lang, 2024. "Interval price predictions for coal using a new multi-scale ensemble model," Energy, Elsevier, vol. 313(C).
    13. Shao, Qihui & Du, Yongqiang & Xue, Wenxuan & Yang, Zhiyuan & Jia, Zhenxin & Shao, Xianzhu & Xu, Xue & Duan, Hongbo & Zhu, Zhipeng, 2024. "Predicting China's thermal coal price: Does multivariate decomposition-integrated forecasting model with window rolling work?," Resources Policy, Elsevier, vol. 99(C).
    14. Mohammed-Khalil Ghali & Cecil Pang & Oscar Molina & Carlos Gershenson-Garcia & Daehan Won, 2025. "Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News," Papers 2508.06497, arXiv.org.
    15. Feng, Zongbao & Wu, Xianguo & Chen, Hongyu & Qin, Yawei & Zhang, Limao & Skibniewski, Miroslaw J., 2022. "An energy performance contracting parameter optimization method based on the response surface method: A case study of a metro in China," Energy, Elsevier, vol. 248(C).
    16. Rao, Amar & Sharma, Gagan Deep & Tiwari, Aviral Kumar & Hossain, Mohammad Razib & Dev, Dhairya, 2025. "Crude oil Price forecasting: Leveraging machine learning for global economic stability," Technological Forecasting and Social Change, Elsevier, vol. 216(C).
    17. Hongjun Zeng & Abdullahi D. Ahmed, 2026. "Dependency structure and volatility connectedness among China-ASEAN stock market, cryptocurrencies, and crude oil," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-39, December.
    18. Shen, Yiran & Liu, Chang & Sun, Xiaolei & Guo, Kun, 2023. "Investor sentiment and the Chinese new energy stock market: A risk–return perspective," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 395-408.
    19. Alexander Vallejo Díaz & Idalberto Herrera Moya & Edwin Garabitos Lara & Cándida K. Casilla Victorino, 2024. "Assessment of Urban Wind Potential and the Stakeholders Involved in Energy Decision-Making," Sustainability, MDPI, vol. 16(4), pages 1-20, February.
    20. Yue Ma & Ling Miao & Lianyong Feng & Ruirui Fang, 2026. "The ripple effects of international energy prices on domestic products in China under external shocks," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 28(3), pages 7129-7145, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    Statistics

    Access and download statistics

    Corrections

    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:sae:enejou:v:46:y:2025:i:5:p:215-244. 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: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.