Mining Sequential Pattern of Multi-dimensional Wind Profiles

Wind has become increasingly important as a source of energy although the generation of wind energy is quite erratic because of its changeable nature. For a given location, wind speed and direction change over time and at different heights. Previous studies have discovered different pattern of wind profiles, however an improved understanding of its spatial, temporal and variation in heights is still lacking. Moreover, there is little prior information available to describe different form of wind profile patterns which frequently occurred in temporal wind dataset. In this paper, we propose a sequential pattern mining approach based on the Linear time Closed pattern Miner sequence (LCMSeq) algorithm for discovering wind profile patterns. This method involves cross-relationships among multiple dimensions associated with a particular space, time and height from the temporal wind dataset. The proposed approach was tested using Dutch 6-hourly wind speed and direction data for the period 1 January 1990 to 31 December 2013. This data extracted from the ECMWF’s ERA-40 reanalysis data provides values at six pressure levels (above ground heights). Results provides relevant and understandable wind patterns within the first 290 m over the ground surface.