A cubical fuzzy Dubois-Prade aggregation framework for renewable and sustainable green energy decision-making.

Choosing the best green‑energy option means weighing many competing environmental, economic, and technical factors, often with a lot of uncertainty. This project helps you set up a decision‑making framework that uses cubical fuzzy sets together with Dubois‑Prade aggregation operators. The framework captures uncertain, vague, or contradictory data with interval‑valued and fuzzy membership functions. That makes it handle complex decisions more reliably than older fuzzy methods. It compares renewable sources—solar, wind, biomass, and small‑scale hydro—against sustainability measures such as carbon‑reduction potential, capital cost, land use, and social acceptance. Step by step, the Dubois‑Prade operators combine expert judgments while keeping the decision space’s limits intact. The project’s main task is to build

a software tool that runs these calculations and ranks the green‑energy options systematically. The guidance provided helps you organize the decision process and gives environmental engineers a solid way to prioritize sustainable energy projects when many attributes are uncertain. In the end, the framework becomes a dependable tool for policy planning and efficient resource allocation.

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