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

Abstract

Abstract 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.