| Summary: | The failure probability of the wave energy converters is exceptionally high, which again increases the operation cost of the entities. The cause of this high cost lies in the fact that various factors influence the production efficiency of the converters. To solve this problem, multiple converters are utilized in series and parallel formation to produce energy simultaneously. This multiple converter system, known as wave energy farms, also fails to increase efficiency and decrease the cost of operation sufficiently. The reason for this is that not only technical but socio-economic as well as different environmental factors have a significant role in this aspect, which remains undetected or under- or over-detected while calculating the potential wave energy. The present investigation tries to classify the different factors which are most influential in controlling the transfer efficiency of wave energy farms to solve the problem of erroneously detecting significant factors. The authors offer a new indicator for estimating the failure likelihood of wave energy farms in converting ocean wave energy into electricity by combining Multi-Criteria Decision Making and Polynomial Neural Networks with information collected from an unbiased ranking technique.
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