Abstract
Renewable 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.
Highlights
The multifaceted phenomenon of climate change is a matter of increasing concern
Despite the fact that the rapid technological progress leads to the improvement of human well-being, some of the industrial sectors are responsible for a significant part of greenhouse gas emissions
Based on the above information, in the present study we address the optimal positioning of Wave Energy Converters (WEC) establishments using a Deep Neural Networks (DNN) approach
Summary
The multifaceted phenomenon of climate change is a matter of increasing concern. The objective of EU is to cover the 32% of the total European energy demand from renewable sources by 2030 and to reduce the greenhouse gas emissions by 40% compared to 1999 [3]. It is necessary for various renewable energy plants to be developed in order to achieve the aforementioned objectives. Wave energy systems are characterized by high efficiency and their contribution to Europe’s energy demand is estimated to be 15% by 2050 [4] Towards this end, Wave Energy Converters (WEC) are systems that exploit the wave energy, converting it into electricity and they can be installed either independently or in combination with Offshore Wind Generators [5]
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