Classification of Energy Foresight Methods: A Framework for Renewable Energy Transitions

Document Type : Original Article

Authors

1 Phd. Student, Department of Renewable Energies Engineering, Faculty of Mechanical and Energy Engineering, University of Shahid Beheshti, Tehran, Iran

2 Associate Professor, Department of Renewable Energies Engineering, Faculty of Mechanical and Energy Engineering, University of Shahid Beheshti, Tehran, Iran

10.22059/ses.2025.392339.1130

Abstract

In the twenty first century, energy systems are undergoing a profound transformation as renewable energy sources increasingly replace fossil fuels. Effective energy foresight methods are essential for managing this transition, addressing challenges such as supply-demand dynamics, investment planning, and policy formulation. This study reviews and categorizes various energy foresight models based on multiple criteria, including geographical scope, time horizon, sectoral coverage, analytical approach, accessibility, and data requirements. By analyzing previous studies, we identify key methodologies such as predictive modeling, scenario analysis, back casting, and hybrid approaches, highlighting their applications in energy transition planning. Special attention is given to the role of foresight models in accelerating renewable energy adoption and optimizing its integration into future energy systems. This study emphasizes that a comprehensive understanding of different foresight models is crucial for shaping sustainable and resilient energy policies in the face of growing environmental and economic challenges.

Keywords

Main Subjects

References

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Journal of sustainable Energy Systems
Volume 4, Issue 4
October 2025
Pages 445-461

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