Ceylan, OğuzhanBurramukku, BhavanaCeylan, OguzhanNeshat, Mehdi2023-10-192023-10-1920210978-1-6654-4389-0https://doi.org/10.1109/UPEC50034.2021.9548274https://hdl.handle.net/20.500.12469/524356th International Universities Power Engineering Conference (UPEC) - Powering Net Zero Emissions -- AUG 31-SEP 03, 2021 -- Teesside Univ, ELECTR NETWORKOne of the most important factors in the amount of power generated by a wave farm is the Wave Energy Converters (WECs) arrangement along with the usual wave conditions. Therefore, forming an appropriate arrangement of WECs in an array is a significant parameter in maximizing power absorption. This paper focuses on developing a fully connected neural model in order to predict the total power output of a wave farm based on the placement of the converters, derived from the four real wave scenarios on the southern coast of Australia. The applied converter model is a fully submerged three-tether converter called CETO. Data collected from the test sites is used to design a neural model for predicting the wave farm's power output produced. A precise analysis of the WEC placement is investigated to reveal the amount of power generated by the wave farms on the test site. We finally proposed a suitable configuration of a fully connected neural model to forecast the power output with high accuracy.eninfo:eu-repo/semantics/closedAccessNeural-NetworksOcean wave energyAbsorptionwave energy convertersartificial neural networksNeural-Networksfully connected neural networksAbsorptionforecasting modelConference ObjectWOS:00072360840012110.1109/UPEC50034.2021.95482742-s2.0-85116699313N/AN/A