ABSTRACTHolistic understanding of wind behaviour over space, time and height is essential for harvesting wind energy application. This study presents a novel approach for mapping frequent wind profile patterns using multi-dimensional sequential pattern mining (MDSPM). This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded (0.125° × 0.125°) wind data for the Netherlands every 6 h and at six height levels. The wind data were first transformed into two spatio-temporal sequence databases (for speed and direction, respectively). Then, the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multi-dimensional sequential patterns, which were then visualized using a 3D wind rose, a circular histogram and a geographical map. These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines. ...
['Norhakim Yusof', 'Raul Zurita-Milla']
Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining