Molecular fuzzy least squares optimization-based decision making with entropy expert weighting for transparent solar panel installation investments

The performance of transparent solar panel projects is affected by both technological and environmental and economicfactors. However, the most important items should be identified for the efficient use of limited resources and effectiverisk management. There are few studies in the literature determining these factors. Because of this situation, businessescannot direct their resources correctly and cannot create an effective strategy. A molecular fuzzy decision-making systemis created to determine the most successful alternative investment policies for these projects to satisfy this gap in theliterature. Various techniques are integrated in this model to reach the most effective solutions, such as molecularfuzzy sets to handle uncertainties, q-learning algorithm to weight experts, least square optimization (LSO) to calculatecriteria weights and multi-objective particle swarm optimization (MOPSO) to rank investment strategies. The maincontribution of this study is proposing an innovative molecular fuzzy decision-making model that manages uncertaintiesmore effectively to select the most appropriate investment strategies for transparent solar panel investments. Consideringmolecular fuzzy sets allows for more accurate modelling of uncertainties and subjective evaluations. The results showthat the most critical investment criterion for transparent solar panel investments is the regional solar radiation level.

Keywords:

Molecular fuzzy sets , least square optimization , q-learning , Solar Panels , energy economics