IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v154y2020icp1346-1356.html
   My bibliography  Save this article

Fractal characteristics of tall tower wind speeds in Missouri

Author

Listed:
  • Balkissoon, Sarah
  • Fox, Neil
  • Lupo, Anthony

Abstract

The Hurst exponent H is used to determine the measure of predictability of a time series. The value between 0 and 1 with 0.5 representative of a random or uncorrelated series, H>0.5 and H<0.5 reflect a data set which is persistent and anti-persistent respectively. The fractal dimension can be given from the Hurst exponent. The fractal dimension is a factor of the complexity of which the system is being repeated at various scales. If the fractal dimension does not change with scale it is deemed monofractal if not, multifractal. The Hurst exponents were determined in this study using the Rescale Range Analysis (R/S Analysis) and Multifractal Detrended Fluctuation Analysis (MF-DFA) for monofractal and multifractal investigations respectively. These methods were applied to daily 10 min wind speed time series data for the year 2009 from three locations within Missouri: Columbia, Neosho and Blanchard for three tall tower stations. The results obtained from the monofractal analysis showed minor variations in the Hurst exponents for the three stations and heights for all the months in 2009. These values ranged from 0.7 to 0.9 and its corresponding fractal dimension was ranged between 1.3 and 1.1. The results for the MF-DFA showed that the wind speed time series were multifractal in nature as the Hurst exponents were functions of the scaling parameters. Also, the plots of the Renyi Exponent were non-linear for the stations and the various channels; this is representative of multifractal signals. The fractal dimensions of the time series using multifractal analysis were determined to be greater than these values determined using monofractal analysis. However, there were no indications of consistent increases in the complexity of the systems’ multifractality with increasing heights for the various stations’ tall towers.

Suggested Citation

  • Balkissoon, Sarah & Fox, Neil & Lupo, Anthony, 2020. "Fractal characteristics of tall tower wind speeds in Missouri," Renewable Energy, Elsevier, vol. 154(C), pages 1346-1356.
    • Handle: RePEc:eee:renene:v:154:y:2020:i:c:p:1346-1356
    DOI: 10.1016/j.renene.2020.03.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120303438
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2020.03.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fox, Neil I., 2011. "A tall tower study of Missouri winds," Renewable Energy, Elsevier, vol. 36(1), pages 330-337.
    2. Salat, Hadrien & Murcio, Roberto & Arcaute, Elsa, 2017. "Multifractal methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 467-487.
    3. Yuan, Ying & Zhuang, Xin-tian & Jin, Xiu, 2009. "Measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(11), pages 2189-2197.
    4. Kavasseri, Rajesh G. & Nagarajan, Radhakrishnan, 2005. "A multifractal description of wind speed records," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 165-173.
    5. Laib, Mohamed & Golay, Jean & Telesca, Luciano & Kanevski, Mikhail, 2018. "Multifractal analysis of the time series of daily means of wind speed in complex regions," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 118-127.
    6. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    7. Shang, Pengjian & Lu, Yongbo & Kamae, Santi, 2008. "Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis," Chaos, Solitons & Fractals, Elsevier, vol. 36(1), pages 82-90.
    8. Zhang, Xiaonei & Zeng, Ming & Meng, Qinghao, 2018. "Multivariate multifractal detrended fluctuation analysis of 3D wind field signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 513-523.
    9. Ludescher, Josef & Bogachev, Mikhail I. & Kantelhardt, Jan W. & Schumann, Aicko Y. & Bunde, Armin, 2011. "On spurious and corrupted multifractality: The effects of additive noise, short-term memory and periodic trends," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2480-2490.
    10. Breslin, M.C. & Belward, J.A., 1999. "Fractal dimensions for rainfall time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 48(4), pages 437-446.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Méndez-Gordillo, Alma Rosa & Campos-Amezcua, Rafael & Cadenas, Erasmo, 2022. "Wind speed forecasting using a hybrid model considering the turbulence of the airflow," Renewable Energy, Elsevier, vol. 196(C), pages 422-431.
    2. Santos, Fábio Sandro dos & Nascimento, Kerolly Kedma Felix do & Jale, Jader da Silva & Stosic, Tatijana & Marinho, Manoel H.N. & Ferreira, Tiago A.E., 2021. "Mixture distribution and multifractal analysis applied to wind speed in the Brazilian Northeast region," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    3. Santos, E.C.O. & Guedes, E.F. & Zebende, G.F. & da Silva Filho, A.M., 2022. "Autocorrelation of wind speed: A sliding window approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    4. Balkissoon, Sarah & Fox, Neil & Lupo, Anthony & Haupt, Sue Ellen & Penny, Stephen G., 2023. "Classification of tall tower meteorological variables and forecasting wind speeds in Columbia, Missouri," Renewable Energy, Elsevier, vol. 217(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shaw, Pankaj Kumar & Saha, Debajyoti & Ghosh, Sabuj & Janaki, M.S. & Iyengar, A.N. Sekar, 2017. "Investigation of multifractal nature of floating potential fluctuations obtained from a dc glow discharge magnetized plasma," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 363-371.
    2. Pavón-Domínguez, P. & Serrano, S. & Jiménez-Hornero, F.J. & Jiménez-Hornero, J.E. & Gutiérrez de Ravé, E. & Ariza-Villaverde, A.B., 2013. "Multifractal detrended fluctuation analysis of sheep livestock prices in origin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4466-4476.
    3. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    4. Li, Xing, 2021. "On the multifractal analysis of air quality index time series before and during COVID-19 partial lockdown: A case study of Shanghai, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    5. Stosic, Tatijana & Telesca, Luciano & Stosic, Borko, 2021. "Multiparametric statistical and dynamical analysis of angular high-frequency wind speed time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    6. Guan, Sihai & Wan, Dongyu & Yang, Yanmiao & Biswal, Bharat, 2022. "Sources of multifractality of the brain rs-fMRI signal," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    7. Pawe{l} O'swik{e}cimka & Stanis{l}aw Dro.zd.z & Mattia Frasca & Robert Gk{e}barowski & Natsue Yoshimura & Luciano Zunino & Ludovico Minati, 2020. "Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analyses," Papers 2004.03319, arXiv.org.
    8. Baranowski, Piotr & Gos, Magdalena & Krzyszczak, Jaromir & Siwek, Krzysztof & Kieliszek, Adam & Tkaczyk, Przemysław, 2019. "Multifractality of meteorological time series for Poland on the base of MERRA-2 data," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 318-333.
    9. Leonarduzzi, R. & Wendt, H. & Abry, P. & Jaffard, S. & Melot, C. & Roux, S.G. & Torres, M.E., 2016. "p-exponent and p-leaders, Part II: Multifractal analysis. Relations to detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 319-339.
    10. Olivares, Felipe & Zanin, Massimiliano, 2022. "Corrupted bifractal features in finite uncorrelated power-law distributed data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    11. Ruan, Qingsong & Zhou, Mi & Yin, Linsen & Lv, Dayong, 2021. "Hedging effectiveness of Chinese Treasury bond futures: New evidence based on nonlinear analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    12. Gómez-Gómez, Javier & Carmona-Cabezas, Rafael & Sánchez-López, Elena & Gutiérrez de Ravé, Eduardo & Jiménez-Hornero, Francisco José, 2022. "Multifractal fluctuations of the precipitation in Spain (1960–2019)," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    13. Gulich, Damián & Zunino, Luciano, 2014. "A criterion for the determination of optimal scaling ranges in DFA and MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 397(C), pages 17-30.
    14. Ghosh, Dipak & Dutta, Srimonti & Chakraborty, Sayantan, 2014. "Multifractal detrended cross-correlation analysis for epileptic patient in seizure and seizure free status," Chaos, Solitons & Fractals, Elsevier, vol. 67(C), pages 1-10.
    15. Xu, Kaiye & Shang, Pengjian & Feng, Guochen, 2015. "Multifractal time series analysis using the improved 0–1 test model," Chaos, Solitons & Fractals, Elsevier, vol. 70(C), pages 134-143.
    16. Zunino, Luciano & Figliola, Alejandra & Tabak, Benjamin M. & Pérez, Darío G. & Garavaglia, Mario & Rosso, Osvaldo A., 2009. "Multifractal structure in Latin-American market indices," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2331-2340.
    17. Faheem Aslam & Paulo Ferreira & Haider Ali & Ana Ercília José, 2022. "Application of Multifractal Analysis in Estimating the Reaction of Energy Markets to Geopolitical Acts and Threats," Sustainability, MDPI, vol. 14(10), pages 1-23, May.
    18. Dutta, Srimonti & Ghosh, Dipak & Chatterjee, Sucharita, 2016. "Multifractal detrended Cross Correlation Analysis of Foreign Exchange and SENSEX fluctuation in Indian perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 188-201.
    19. Akash P. POOJARI & Siva Kiran GUPTHA & G Raghavender RAJU, 2022. "Multifractal analysis of equities. Evidence from the emerging and frontier banking sectors," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(632), A), pages 61-80, Autumn.
    20. He, Xiaoli & Wang, Hongwu & Du, Ziping, 2014. "The complexity and fractal structures of CSI300 before and after the introduction of CSI300IF," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 76-85.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:154:y:2020:i:c:p:1346-1356. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.