Document Type : Original Article
Authors
1
Department of Renewable Energy Engineering, Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran
2
Shahid Beheshti University
10.22059/ses.2025.392339.1130
Abstract
In the 21st 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.
The study categorizes energy foresight models based on several key criteria: geographical coverage (global, regional, national, local, and project-specific), time horizon (short-term, medium-term, long-term), sectoral coverage (single-sector vs. multi-sector), and analytical approach (top-down, bottom-up, hybrid). Additionally, models are classified based on their accessibility (commercial, proprietary, open-code, open-access), spatial resolution (single-node vs. multi-node), and data requirements (qualitative, quantitative, hybrid).
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