APPLICATION OF TYPE-2 FUZZY TOPSIS METHOD FOR ESTIMATING RENEWABLE ENERGY SOURCES

As global energy demands rise and the adverse effects of climate change become increasingly evident, the need for clean, sustainable energy sources has never been more critical. Renewable energy options, which harness natural processes like sunlight, wind, water, and geothermal heat, offer promising solutions to reduce dependence on fossil fuels and mitigate environmental damage. Estimating renewable energy options involves evaluating various energy sources to determine their suitability based on several criteria. Renewable energy potential varies significantly across different regions, influenced by factors such as natural resources, technological development, and policy support. Some regions are naturally more suited for certain types of renewable energy due to their climate, geography, and resources. The efficiency and cost-effectiveness of renewable technologies can vary based on how advanced technology is in each region. Government policies and incentives play a huge role in determining how much potential renewable energy can be developed. The key objective is to choose the most viable renewable energy sources for a given region or application by considering factors like efficiency, flexibility, and reliability. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method helps decision-makers identify the preferred alternatives by evaluating the relative proximity of each alternative to an ideal solution. Unlike traditional type-1 fuzzy numbers, type-2 fuzzy numbers provide an additional layer of uncertainty by representing a fuzzy set with both primary and secondary membership functions. This allows capturing more detailed and complex uncertainties. Type-2 fuzzy numbers can be integrated into TOPSIS to handle the inherent uncertainty and imprecision in decision-making, particularly in the context of renewable energy options, where data is often uncertain or vague. This paper offers the integration of spectrum selection optimization algorithms based on the evaluation of the characteristics of various renewable energy options using the TOPSIS fuzzy method.