Holistic 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 multidimen...Holistic 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 multidimensional 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 multidimensional 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.Our analysis identified four frequent wind profile patterns.One of them highly suitable to harvest wind energy at a height of 128 m and 68.97%of the geographical area covered by this pattern already contains wind turbines.This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.展开更多
由于序列模式挖掘需要花费大量计算时间,并需要占用大量存储空间.减少计算量、节省存储空间开销成为序列模式挖掘的关键.因PrefixSpan算法不产生候选,而适当应用Bitmap数据结构可避免重复扫描数据库,基于此,本文提出了BM-PrefixSpan算法...由于序列模式挖掘需要花费大量计算时间,并需要占用大量存储空间.减少计算量、节省存储空间开销成为序列模式挖掘的关键.因PrefixSpan算法不产生候选,而适当应用Bitmap数据结构可避免重复扫描数据库,基于此,本文提出了BM-PrefixSpan算法,用于序列模式挖掘,设计并构造了PFPBM(Prefix of First Position on BitMap)表用于记录序列中的每个项在位图中第1次出现的位置.实验结果表明,BM-PrefixSpan算法综合了PrefixSpan和SPAM算法的优点,能够更快、更好地挖掘出序列模式.展开更多
基金This work was supported by the Malaysian Ministry of Education(SLAI)and Universiti Teknologi Malaysia(UTM).
文摘Holistic 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 multidimensional 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 multidimensional 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.Our analysis identified four frequent wind profile patterns.One of them highly suitable to harvest wind energy at a height of 128 m and 68.97%of the geographical area covered by this pattern already contains wind turbines.This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.
文摘由于序列模式挖掘需要花费大量计算时间,并需要占用大量存储空间.减少计算量、节省存储空间开销成为序列模式挖掘的关键.因PrefixSpan算法不产生候选,而适当应用Bitmap数据结构可避免重复扫描数据库,基于此,本文提出了BM-PrefixSpan算法,用于序列模式挖掘,设计并构造了PFPBM(Prefix of First Position on BitMap)表用于记录序列中的每个项在位图中第1次出现的位置.实验结果表明,BM-PrefixSpan算法综合了PrefixSpan和SPAM算法的优点,能够更快、更好地挖掘出序列模式.
