Investigating the Potential of Evaluation Based on Distance from Average Solution (EDAS) Method in Crisp and Fuzzy Environments for Solving Building Energy Consumption Optimisation Multiple Attribute Decision-Making (MADM) Problems

Sustainable selection of the best energy consumption optimisation strategies for buildings, whether active or passive, is a complex task due to the existence of several criteria. Distance from Average Solution (EDAS) Method is one of the most recent Multiple Attribute Decision-Making (MADM) techniques which has been widely used in many disciplines. However, it has been rarely used for solving MADM problems within the area of building energy optimisation, especially selecting suitable energy consumption optimisation strategies. In order to fulfil the mentioned gap within the body of knowledge, this study aimed to assess the strength of EDAS method in comparison to other MADM techniques in both crisp and fuzzy environments, especially for selecting suitable energy consumption optimisation strategies, using secondary and primary data. Based on the obtained results, EDAS achieved the score of 4.46/5 which clearly illustrated its suitability for solving MADM problems within the studied field. Also, the most popular types of fuzzy EDAS, namely Type-1 Fuzzy EDAS and Interval Type-2 Fuzzy EDAS, were investigated and their step-by-step application procedures for solving an MADM problem were explained in detail. The outcome of this study is valuable for the decision makers within the area of building energy consumption optimisation. Moreover, other scholars investigating MADM problems in other disciplines can also apply the proposed methodology of this study to investigate the suitability of other MADM techniques for solving MADM problems within their field of study.