Document Type

Conference Paper


This item is available under a Creative Commons License for non-commercial use only


Electrical and electronic engineering

Publication Details

The IET Digital Library - 24th IET Irish Signals and Systems Conference (ISSC 2013)


Signal Processing within the frequency domain has long been associated with electrical engineering as a means to quantify the characteristics of voltage/current waveforms. Historically, wind speed data (speed/direction) have been captured and stored as statistical markers within a time series description. This form of storage, while cumbersome, is applicable in wind regimes that are relatively laminar. In urban environments, where the associated topographies and building morphologies are heterogeneous, wind speeds are highly turbulent and chaotic. In such environments and with particular reference to wind energy, time series statistics are of limited use, unless the generic probability distribution function (PDF) is also considered. Furthermore, the industry standard metric that quantifies the turbulent component of wind speed, Turbulence Intensity (TI), is computationally cumbersome and resource intensive. An alternative model to quantify turbulence is proposed here. This paper will describe how Fourier dimension modelling (Df), through linkage with the Weibull probability density function, can quantify turbulence in a more efficient manner. This model could potentially be developed to facilitate urban wind power prediction and is relevant to the planning and development considerations within the built environment.