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<p>Sustainable economies require effective energy planning that goes beyond relying on functioning forecasting models to comprehend energy dynamics, and also provides well-defined decision-making (DM) models that can address risk, ambiguity, and conflicting eco-economic objectives. This type of strategic planning requires an integrated assessment approach that can evaluate forecasting choices in an uncertain and dynamic environment. This paper presents a new and modified methodology for ranking energy forecasting models within a Pythagorean Fuzzy Set (PFS) system by integrating the CRITIC (Criteria Importance Through Inter-Criteria Correlation) weighting framework and the MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis) ranking scheme. In the suggested framework, expert uncertainty and vagueness are represented by the PFS environment. In contrast, some of the leading eco-economic indicators are objectively weighted using CRITIC, and forecasting model alternatives are prioritized based on MAIRCA. A comparative study is conducted on a hypothetical data set that represents realistic energy system capabilities, including adaptability, carbon policy integration, and computing efficiency. The findings suggest that the framework contributes to consistent, interpretable, and uncertainty-aware rankings, and the Deep Q-Network (DQN) model was ranked to be the most effective alternative. The study contributes to the development of more sophisticated decision-support mechanisms to facilitate sustainable energy planning, enabling informed and balanced decisions as the eco-economic climate evolves rapidly.</p>

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Pythagorean fuzzy MAIRCA CRITIC for energy price and demand forecasting involving eco economic factors for sustainable economy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月2日

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  • Xiaoyu Zhang

摘要

Sustainable economies require effective energy planning that goes beyond relying on functioning forecasting models to comprehend energy dynamics, and also provides well-defined decision-making (DM) models that can address risk, ambiguity, and conflicting eco-economic objectives. This type of strategic planning requires an integrated assessment approach that can evaluate forecasting choices in an uncertain and dynamic environment. This paper presents a new and modified methodology for ranking energy forecasting models within a Pythagorean Fuzzy Set (PFS) system by integrating the CRITIC (Criteria Importance Through Inter-Criteria Correlation) weighting framework and the MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis) ranking scheme. In the suggested framework, expert uncertainty and vagueness are represented by the PFS environment. In contrast, some of the leading eco-economic indicators are objectively weighted using CRITIC, and forecasting model alternatives are prioritized based on MAIRCA. A comparative study is conducted on a hypothetical data set that represents realistic energy system capabilities, including adaptability, carbon policy integration, and computing efficiency. The findings suggest that the framework contributes to consistent, interpretable, and uncertainty-aware rankings, and the Deep Q-Network (DQN) model was ranked to be the most effective alternative. The study contributes to the development of more sophisticated decision-support mechanisms to facilitate sustainable energy planning, enabling informed and balanced decisions as the eco-economic climate evolves rapidly.