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

Foued Hamouda et al.

Energy Journal2025https://doi.org/10.1177/01956574251340012article
AJG 3ABDC A
Weight
0.46

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

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https://doi.org/https://doi.org/10.1177/01956574251340012

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@article{foued2025,
  title        = {{Forecasting Energy Commodity Prices Amidst Worldwide Energy Transitions Using Artificial Intelligence Models}},
  author       = {Foued Hamouda et al.},
  journal      = {Energy Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/01956574251340012},
}

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

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.