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A deep learning and GIS approach for the optimal positioning of wave energy converters

Batsis Georgios, Partsinevelos Panagiotis, Stavrakakis Georgios

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URIhttp://purl.tuc.gr/dl/dias/60E7B35B-D0EB-4711-96CE-7165AF2F3776-
Αναγνωριστικόhttps://doi.org/10.3390/en14206773-
Αναγνωριστικόhttps://www.mdpi.com/1996-1073/14/20/6773/htm-
Γλώσσαen-
Μέγεθος21 pagesen
ΤίτλοςA deep learning and GIS approach for the optimal positioning of wave energy convertersen
ΔημιουργόςBatsis Georgiosen
ΔημιουργόςΜπατσης Γεωργιοςel
ΔημιουργόςPartsinevelos Panagiotisen
ΔημιουργόςΠαρτσινεβελος Παναγιωτηςel
ΔημιουργόςStavrakakis Georgiosen
ΔημιουργόςΣταυρακακης Γεωργιοςel
ΕκδότηςMDPIen
ΠερίληψηRenewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output. en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2022-09-29-
Ημερομηνία Δημοσίευσης2021-
Θεματική ΚατηγορίαWave energy convertersen
Θεματική ΚατηγορίαDeep neural networksen
Θεματική ΚατηγορίαRenewable energy sourcesen
Θεματική ΚατηγορίαSpatial planningen
Θεματική ΚατηγορίαSentinel satellite imageryen
Βιβλιογραφική ΑναφοράG. Batsis, P. Partsinevelos, and G. Stavrakakis, “A deep learning and GIS approach for the optimal positioning of wave energy converters,” Energies, vol. 14, no. 20, Oct. 2021, doi: 10.3390/en14206773.en

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