Performance of artificial neural networks in nearshore wave power prediction
Graphical abstract
Introduction
Fossil fuels, energy dense and relatively inexpensive, supply nowadays the majority of the global energy consumption. Nevertheless, it is clear that, in the short to medium term, they should be replaced to a great extent by carbon-free renewable sources [1]. While wind and solar energy exploitation have matured over the last decades and are increasingly being installed today, ocean wave energy exploitation is still unproven at a commercial scale. However, its enormous potential explains the intensive research currently dedicated to the development of wave energy conversion systems [2], [3], [4], [5], [6], [7], [8] and to the assessment of the wave resource in various regions [9], [10], [11].
In spite of their importance, the technological considerations are not the only factor to be considered in bringing wave energy to a commercial stage. Another crucial aspect is the spatial and temporal variability of the resource, which is particularly significant in the nearshore – the area with the greatest practical interest; thus, the first step to exploiting wave energy is understanding the resource and being able to perform a thorough and accurate assessment of the energy available at a site of interest [12].
There exist different methodologies and data sources to carry out a wave resource assessment which have been implemented in different coastal regions. Wave buoy data are indeed very useful, but the wave buoys may be too expensive to maintain for the periods of time needed for long-term wave climate assessment. The back-scattered signal from satellite altimeters can provide relatively cheaply [13] enormous amounts of wave data with nearly the same accuracy as a wave buoy if correctly interpreted [14]. These data, together with data obtained from global wind-wave models, are an effective approximation to the wave power in deepwater. However, they provide a poor estimate of the wave power at nearshore locations, where the complex bathymetry and coastline gives rise to shoaling, refraction and diffraction and thus to significant variations in the distribution of wave energy over small areas.
Currently, spectral numerical models are the most popular tool to investigate these wave transformations and thus the available wave energy resource in the nearshore. These models compute accurately the propagation of swell in nearshore areas [15], [16], [17] without the need of an important investment of resources as it is the case of in situ measurements. However, they have some critical disadvantages, they are very time consuming, need of care and expertise when implementing the model, and are very sensitive to different parameters (as it may be the bathymetric data). For these reasons, in the last years, different attempts have been made to supplement or replace numerical results with other techniques [13], [18], [19].
In this paper, a new approach to characterising the wave energy resource at a particular coastal point based on Artificial intelligence (AI) is presented. In particular, the AI tool developed is an Artificial neural network (hereafter ANN) model, which is capable of predicting wave power at a nearshore location. Artificial neural networks have proven to be a very powerful technique capable of resolving complex physical problems [20], [21], [22], [23]. Indeed, they have already been applied to other Coastal engineering problems with excellent results, such as the forecasting of wind and wave climate time series [13], [24], wave reflection at submerged breakwaters [25], floating boom performance [26], headland-bay beach planforms [27], [28], [29] or rubble-mound breakwater stability [30]. These works have shown that ANN modelling presents key advantages such as computational efficiency or potentially predictive power, but without the need of testing numerous physical and numerical parameters or to obtain detailed geographic information [31].
The final aim of this work is to assess the performance of the ANN model and its suitability for nearshore wave power prediction. For this purpose the model results are compared with those obtained from in situ measurements and from a state-of-art spectral numerical model.
Section snippets
Wave data and wave energy characterisation
The data used in this work were obtained from two types of buoys operated by Spain's State Ports: one offshore (Vilán-Sisargas) and one nearshore (Langosteira) (Fig. 1), covering nearly 13 years (from 13/5/1998 to 8/4/2011) with an hourly frequency. The offshore and nearshore buoys are located at water depths of 386 m and 40 m, respectively.
The data records from the buoys present gaps of different nature. First, there exist an important number of small gaps of a few hours which are presumably due
ANN model
An ANN [32], [33] is an information-processing system based on neural biology capable of storing observed knowledge related to some physical problem and making it applicable to new cases. It consists of a certain number of artificial neurons linked by means of interneuron connections simulating the structure of a human brain. An ANN learns from its environment through a training process and stores the acquired knowledge in the interneuron connections by means of synaptic weights in the same way
ANN model
The architecture of an ANN model is defined by means of its number of layers and its number of neurons per layer. For a given problem, the number of neurons of the input and output layer must be equal to the number of input and output variables, respectively. Therefore, in this specific case the input layer consists of three input neurons corresponding to the three input variables at the offshore location (Hm0, Te and θm) and the output layer must consist of one output neuron corresponding to
Conclusions
In this work, an ANN model for predicting the nearshore wave power has been developed. It uses as input three offshore parameters: significant wave height, energy period and mean wave direction. In order to select the most appropriate ANN architecture for this specific problem, a thorough experimental study involving twelve different architectures with one and two hidden layers was conducted. The performance of each architecture was characterised in terms of minimum NMSE obtained after 40
Acknowledgements
This work was supported by project DPI2009-14546-C02-02 (“Assessment of Renewable Energy Resources”) of the Spanish Ministry of Science and Innovation and the project “Development of a geospatial database for the exploitation of the energy resource along the Galician Coast” funded by the Barrie de la Maza Foundation. The authors are indebted to Puertos del Estado (Spain's State Ports), in particular to Dr. I. Rodríguez-Arévalo, Dr. E. Fanjul, Ms. P. Gil and Ms. S. Pérez, for kindly providing
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