Complex-valued prediction of wind profile using augmented complex statistics

This paper presents a novel approach for the simultaneous modelling and forecasting of wind whereby the wind field is considered as a vector of its speed and direction components in the field of complex numbers C. To account for the intermittency and coupling of wind speed and direction, we propose to use the recently introduced framework of augmented complex statistics. The augmented complex least mean square (ACLMS) algorithm is introduced and its usefulness in wind forecasting is analysed. Simulations over different wind regimes support the approach.