Sustainable renewable energy source selection with novel fuzzy entropy based extended TOPSIS method

As the world transition towards a low-carbon economy, the selection of sustainable renewable energy has become a critical component of mitigating climate change, ensuring energy security, and promoting economic growth. With the increasing demand to reduce greenhouse gas emission and reliance on fossil fuels, it is essential to evaluate and compare different renewable energy sources such as solar, wind, hydro, and biomass power to determine the most suitable options relative to various conflicting parameters such as cost, efficiency, scalability, energy density and environmental impact. The selection of sustainable renewable energy source is a complex process. For this, opinions of experts regarding the performance of different sources relative to different criteria are taken into consideration. In real-world decision making processes, decision makers often rely on linguistic assessment, which cannot be precisely quantified using classical (crisp) approaches. This kind of qualitative information introduces epistemic uncertainty, which fuzzy set theory is specifically designed to handle. This paper presents a modified TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) method in fuzzy environment for the selection of suitable renewable energy source. Fuzzy entropy quantify the fuzziness or vagueness within a fuzzy set. This study presents a new fuzzy entropy to overcome the limitations of several existing fuzzy entropies. A novel fuzzy distance measure is proposed to more accurately quantify the dissimilarity between fuzzy sets, enhancing the precision of comparative analysis. The effectiveness of the proposed distance measure in pattern recognition is demonstrated through comprehensive numerical illustrations. This research aims to enhance the stability of the TOPSIS method by integrating the proposed entropy and distance measures into its framework.