Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series

Abstract

Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition. Firstly, the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis (DFA), and the corresponding scaling exponents are larger than 1. This indicates that all these wind speed time series have non-stationary characteristics. Secondly, concerning this special feature (i.e., non-stationarity) of wind signals, a cross-correlation analysis method, namely detrended cross-correlation analysis (DCCA) coefficient, is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs. Finally, experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component, while three traditional cross-correlation techniques (i.e., Pearson coefficient, cross-correlation function, and DCCA method) cannot give us these information directly.

Keywords

temporal-spatial cross-correlation / near-surface wind speed time series / detrended cross-correlation analysis (DCCA) / cross-correlation coefficient / Pearson coefficient / cross-correlation function

References

[1] GovindanR B, KantzH. Long-term correlations and multifractality in surface wind speed [J]. Europhysics Letters, 2004, 68(2): 184-190

[2] KavasseriR G, NagarajanR. Evidence of crossover phenomena in wind-speed data [J]. IEEE Transactions on Circuits and Systems, 2004, 51(11): 2255-2262

[3] KavasseriR G, NagarajanR. A multifractal description of wind speed records [J]. Chaos Solitons & Fractals, 2005, 24(1): 165-173

[4] KocakK. Examination of persistence properties of wind speed records using detrended fluctuation analysis [J]. Energy, 2009, 34(11): 1980-1985

[5] FengT, FuZ-t, DengX, MaoJ-yu. A brief description to different multi-fractal behaviors of daily wind speed records over China [J]. Physics Letters A, 2009, 373(45): 4134-4141

[6] TelescaL, LovalloM. Analysis of the time dynamics in wind records by means of multifractal detrended fluctuation analysis and the Fisher-Shannon information plane [J]. Journal of Statistical Mechanics-Theory and Experiment, 2011, 2011(7): 07001

[7] SantosM D O, StosicT, StosicB D. Long-term correlations in hourly wind speed records in Pernambuco, Brazil [J]. Physica A, 2012, 391: 1546-1552

[8] AnjosP S D, SilvaA S A D, StosicB, StosicT. Long-term correlations and cross-correlations in wind speed and solar radiation temporal series from Fernando de Noronha Island, Brazil [J]. Physica A, 2015, 424: 90-96

[9] BeckV, DotzekN. Reconstruction of near-surface tornado wind fields from forest damage [J]. Journal of Applied Meteorology and Climatology, 2010, 49(7): 1517-1537

[10] AzadH B, MekhilefS, GanapathyV G. Long-term wind speed forecasting and general pattern recognition using neural networks [J]. IEEE Transactions on Sustainable Energy, 2014, 5(2): 546-553

[11] KristoufekL. Measuring correlations between non-stationary series with DCCA coefficient [J]. Physica A, 2014, 402: 291-298

[12] ChenY-guang. A new methodology of spatial cross-correlation analysis [J]. PLOS ONE, 2015, 10(5): 01261585

[13] PodobnikB, StanleyH E. Detrended cross-correlation analysis: A new method for analyzing two nonstationary time series [J]. Physical Review Letters, 2008, 100(8): 38-71

[14] ZebendeG F. DCCA cross-correlation coefficient: Quantifying level of cross-correlation [J]. Physica A, 2011, 390(4): 614-618

[15] PengC K, BuldyrevS V, HavlinS, SimonsM, StanleyH E, GoldbergerA L. Mosaic organization of DNA nucleotides [J]. Physical Review E, 1994, 49(2): 1685-1689

[16] da SilvaM F, De Area Leao PereiraE J, Da Silva FilhoA M, MirandaJ G V, ZebendeG F. Quantifying cross-correlation between Ibovespa and Brazilian blue-chips: The DCCA approach [J]. Physica A, 2015, 424: 124-129

[17] DongK-q, FanJ, GaoYou. Cross-correlations and structures of aero-engine gas path system based on DCCA coefficient and rooted tree [J]. Fluctuation and Noise Letters, 2015, 2(14): 1550014

[18] DongK-q, GaoY, JingL-ming. Correlation tests of the engine performance parameter by using the detrended crosscorrelation coefficient [J]. Journal of the Korean Physical Society, 2015, 66(4): 539-543

[19] CaoG-x, HanYan. Does the weather affect the Chinese stock markets? Evidence from the analysis of DCCA cross-correlation coefficient [J]. International Journal of Modern Physics B, 2015, 29(01): 14502361

[20] ShenC-h, LiC-l, SiY-li. A detrended cross-correlation analysis of meteorological and API data in Nanjing, China [J]. Physica A, 2015, 419: 417-428

[21] PodobnikB, JiangZ-q, ZhouW-x, StaleyH E. Statistical tests for power-law cross-correlated processes [J]. Physical Review E, 2011, 84: 066118

[22] KristoufekL. Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components [J]. Physica A, 2015, 428: 194-205

[23] IvanovP C, AmaralL, GoldbergerA L, HavlinS, RosenblumM G, StruzikllZ R, StanleyH E. Multifractality in human heartbeat dynamics [J]. Nature, 1999, 399(6735): 461-465