Picture of sea vessel plough through rough maritime conditions

Innovations in marine technology, pioneered through Open Access research...

Strathprints makes available scholarly Open Access content by researchers in the Department of Naval Architecture, Ocean & Marine Engineering based within the Faculty of Engineering.

Research here explores the potential of marine renewables, such as offshore wind, current and wave energy devices to promote the delivery of diverse energy sources. Expertise in offshore hydrodynamics in offshore structures also informs innovations within the oil and gas industries. But as a world-leading centre of marine technology, the Department is recognised as the leading authority in all areas related to maritime safety, such as resilience engineering, collision avoidance and risk-based ship design. Techniques to support sustainability vessel life cycle management is a key research focus.

Explore the Open Access research of the Department of Naval Architecture, Ocean & Marine Engineering. Or explore all of Strathclyde's Open Access research...

Short-term spatio-temporal prediction of wind speed and direction

Dowell, Jethro and Weiss, Stephan and Hill, David and Infield, David (2014) Short-term spatio-temporal prediction of wind speed and direction. Wind Energy, 17 (12). pp. 1945-1955. ISSN 1095-4244

Full text not available in this repository. Request a copy from the Strathclyde author

Abstract

This paper aims to produce a low-complexity predictor for the hourly mean wind speed and direction from 1 to 6 h ahead at multiple sites distributed around the UK. The wind speed and direction are modelled via the magnitude and phase of a complex-valued time series. A multichannel adaptive filter is set to predict this signal on the basis of its past values and the spatio-temporal correlation between wind signals measured at numerous geographical locations. The filter coefficients are determined by minimizing the mean square prediction error. To account for the time-varying nature of the wind data and the underlying system, we propose a cyclo-stationary Wiener solution, which is shown to produce an accurate predictor. An iterative solution, which provides lower computational complexity, increased robustness towards ill-conditioning of the data covariance matrices and the ability to track time-variations in the underlying system, is also presented. The approaches are tested on wind speed and direction data measured at various sites across the UK. Results show that the proposed techniques are able to predict wind speed as accurately as state-of-the-art wind speed forecasting benchmarks while simultaneously providing valuable directional information.