Hybrid molecular fuzzy recommender systems for impact of climate change on renewable energy performance

Abstract

The purpose of this study is to identify the most convenient strategies to manage the negative impacts of climate change on renewable energy projects effectively. Missing expert assessments for these criteria and alternatives are completed via collaborative filtering. The sample for this study consists of evaluations provided by six experts in the fields of renewable energy and climate science. The data is collected through structured interviews and questionnaires, focusing on the criteria and alternatives relevant to climate change mitigation in renewable energy projects. Criteria are weighted with the help of molecular fuzzy cognitive maps. Strategies are ranked by using fuzzy molecular ranking (MORAN). The main contribution of this study is that a comprehensive evaluation can be conducted to generate the strategies to minimize the negative impacts of climate change on these projects by creating a new model. Weather changes and ecosystem disruptions are the climate change factor that has the most negative impact on the performance of renewable energy projects. Some standards should be determined, or necessary arrangements should be made to comply with existing international standards. The integration of different renewable energy sources is an important strategy to eliminate the negative effects of climate change on renewable energy projects.