Abstract
An extensive record of current velocities at all levels in the water column is an indispensable requirement for a tidal resource assessment and is fully necessary for accurate determination of available energy throughout the water column as well as estimating likely energy capture for any particular device. Traditional tidal prediction using the least squares method requires a large number of harmonic parameters calculated from lengthy acoustic Doppler current profiler (ADCP) measurements, while long-term in situ ADCPs have the advantage of measuring the real current but are logistically expensive. This study aims to show how these issues can be overcome with the use of a neural network to predict current velocities throughout the water column, using surface currents measured by a high-frequency radar. Various structured neural networks were trained with the aim of finding the network which could best simulate unseen subsurface current velocities, compared to ADCP data. This study shows that a recurrent neural network, trained by the Bayesian regularisation algorithm, produces current velocities highly correlated with measured values: r2 (0.98), mean absolute error (0.05 ms−1), and the Nash–Sutcliffe efficiency (0.98). The method demonstrates its high prediction ability using only 2 weeks of training data to predict subsurface currents up to 6 months in the future, whilst a constant surface current input is available. The resulting current predictions can be used to calculate flow power, with only a 0.4% mean error. The method is shown to be as accurate as harmonic analysis whilst requiring comparatively few input data and outperforms harmonics by identifying non-celestial influences; however, the model remains site specific.
Highlights
As the demand for electricity increases globally, with the concurrent commitment of many countries to lower emission levels, the number of renewable energy developments is soaring
The aim of this paper is to assess the capability of a technique combining HF radar surface currents and Artificial Neural Network (ANN) for quantification of subsurface currents, to show comparable accuracy to in situ measurements and harmonic analysis, decreasing the resources required for a reliable tidal stream resource assessment of a large area
Network sizes, while Levenberg–Marquardt algorithm (LM) and backpropagation method (BR) began at
Summary
As the demand for electricity increases globally, with the concurrent commitment of many countries to lower emission levels, the number of renewable energy developments is soaring. The kinetic energy caused by flood and ebb tides is too low in most areas. In some locations, the combination of tidal factors and local bathymetry can result in velocities that have an energy potential that is high enough over a large spatial and temporal range in order to enable production of electricity at a cost-efficient rate, potentially even higher than an efficient wind site [1]. As high-energy sites are developed, and turbine technology improves to viably produce energy at lower velocities, resource assessments will need to be conducted for site characterisation of new areas
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
