IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v243y2022ics0360544221029911.html
   My bibliography  Save this article

Long-range correlations of the wind speed in a northeast region of Brazil

Author

Listed:
  • Perini de Souza, Noéle Bissoli
  • Cardoso dos Santos, José Vicente
  • Sperandio Nascimento, Erick Giovani
  • Bandeira Santos, Alex Alisson
  • Moreira, Davidson Martins

Abstract

The objective of this work is to analyze the scaling behavior of wind speed in the region of the state of Bahia, northeastern Brazil, in search of long-range correlations and other information about the crossover phenomena. Thus, data from 41 automatic surface stations were used for a period of five years—between 2015 and 2020—for onshore reading. For offshore readings, data from a surface station located in the Abrolhos Archipelago were used. To achieve this goal, the DFA (detrended fluctuation analysis) technique was used in the analysis of measured data at the stations, along with numerical simulations using the WRF (weather research and forecasting) mesoscale model. The results of the analysis of hourly average wind speed from the measured and simulated data show the existence of scale behavior with the appearance, in most cases, of a double crossover—onshore and offshore. This suggests the phenomenon's dependence on the time period of the analyzed data, and also on the geographic location, showing a strong correlation with the Atlantic and Pacific oscillations (La Niña and El Niño), indicating the influence of local, mesoscale, and macroscale effects in the region of study. For the offshore case, the measured data and simulations presented a subdiffusive behavior (α≥1) before the first crossover, and persistence (0.5<α<1) for the other two scales. For the onshore case, the results showed different behaviors, with some stations and simulations showing subdiffusive behavior and others showing persistence before and after the first and second crossovers, but most showed persistence between the first and second crossovers. The results of the analysis of daily averages of station data and simulations confirm the existence of only one crossover as a reflection of global effects (macroscale), since local effects have a daily cycle (mesoscale). The DFA method does not detect fluctuations in the measured and simulated data at all locations—especially in regions with extreme slopes, or in lowland plains—as double crossover, and in a few cases even simple crossovers are not recorded. From a practical point of view, unlike other methods to detect wind persistence, the methodology considered in this study reveal the multifractality in the wind speed data, showing that fluctuations in wind speed can be dominated by atmospheric phenomena governed by a local or regional meteorological system, while fluctuations on long-range time scales in some locations can be influenced by global weather patterns, providing new perspectives about the best locations for wind energy.

Suggested Citation

  • Perini de Souza, Noéle Bissoli & Cardoso dos Santos, José Vicente & Sperandio Nascimento, Erick Giovani & Bandeira Santos, Alex Alisson & Moreira, Davidson Martins, 2022. "Long-range correlations of the wind speed in a northeast region of Brazil," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221029911
    DOI: 10.1016/j.energy.2021.122742
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221029911
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122742?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. de Oliveira Santos, Maíra & Stosic, Tatijana & Stosic, Borko D., 2012. "Long-term correlations in hourly wind speed records in Pernambuco, Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1546-1552.
    2. Yanhui Liu & Pierre Cizeau & Martin Meyer & Chung-Kang Peng & H. Eugene Stanley, 1997. "Correlations in Economic Time Series," Papers cond-mat/9706021, arXiv.org.
    3. Jiang, Zhi-Qiang & Chen, Wei & Zhou, Wei-Xing, 2009. "Detrended fluctuation analysis of intertrade durations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(4), pages 433-440.
    4. Jiang, Lei, 2018. "Mean wind speed persistence over China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 211-217.
    5. Soulouknga, M.H. & Doka, S.Y. & N.Revanna, & N.Djongyang, & T.C.Kofane,, 2018. "Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution," Renewable Energy, Elsevier, vol. 121(C), pages 1-8.
    6. Amir Bashan & Ronny Bartsch & Jan W. Kantelhardt & Shlomo Havlin, 2008. "Comparison of detrending methods for fluctuation analysis," Papers 0804.4081, arXiv.org.
    7. 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).
    8. Argüeso, D. & Businger, S., 2018. "Wind power characteristics of Oahu, Hawaii," Renewable Energy, Elsevier, vol. 128(PA), pages 324-336.
    9. Bashan, Amir & Bartsch, Ronny & Kantelhardt, Jan W. & Havlin, Shlomo, 2008. "Comparison of detrending methods for fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5080-5090.
    10. Cancino-Solórzano, Yoreley & Gutiérrez-Trashorras, Antonio J. & Xiberta-Bernat, Jorge, 2010. "Analytical methods for wind persistence: Their application in assessing the best site for a wind farm in the State of Veracruz, Mexico," Renewable Energy, Elsevier, vol. 35(12), pages 2844-2852.
    11. Santos, José Vicente Cardoso & Perini, Noéle Bissoli & Moret, Marcelo Albano & Nascimento, Erick Giovani Sperandio & Moreira, Davidson Martins, 2021. "Scaling behavior of wind speed in the coast of Brazil and the South Atlantic Ocean: The crossover phenomenon," Energy, Elsevier, vol. 217(C).
    12. Liu, Yanhui & Cizeau, Pierre & Meyer, Martin & Peng, C.-K. & Eugene Stanley, H., 1997. "Correlations in economic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 245(3), pages 437-440.
    13. Santos, J.V.C. & Moreira, D.M. & Moret, M.A. & Nascimento, E.G.S., 2019. "Analysis of long-range correlations of wind speed in different regions of Bahia and the Abrolhos Archipelago, Brazil," Energy, Elsevier, vol. 167(C), pages 680-687.
    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. de Aquino Ferreira, Saulo Custodio & Cyrino Oliveira, Fernando Luiz & Maçaira, Paula Medina, 2022. "Validation of the representativeness of wind speed time series obtained from reanalysis data for Brazilian territory," Energy, Elsevier, vol. 258(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. 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).
    2. Stavros-Richard G. Christopoulos & Nicholas V. Sarlis, 2017. "An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series," Complexity, Hindawi, vol. 2017, pages 1-27, February.
    3. Santos, José Vicente Cardoso & Perini, Noéle Bissoli & Moret, Marcelo Albano & Nascimento, Erick Giovani Sperandio & Moreira, Davidson Martins, 2021. "Scaling behavior of wind speed in the coast of Brazil and the South Atlantic Ocean: The crossover phenomenon," Energy, Elsevier, vol. 217(C).
    4. Wang, Lei & Liu, Lutao, 2020. "Long-range correlation and predictability of Chinese stock prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    5. Santos, J.V.C. & Moreira, D.M. & Moret, M.A. & Nascimento, E.G.S., 2019. "Analysis of long-range correlations of wind speed in different regions of Bahia and the Abrolhos Archipelago, Brazil," Energy, Elsevier, vol. 167(C), pages 680-687.
    6. Yang, Yan-Hong & Shao, Ying-Hui & Shao, Hao-Lin & Stanley, H. Eugene, 2019. "Revisiting the weak-form efficiency of the EUR/CHF exchange rate market: Evidence from episodes of different Swiss franc regimes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 734-746.
    7. Ni, Xiao-Hui & Jiang, Zhi-Qiang & Gu, Gao-Feng & Ren, Fei & Chen, Wei & Zhou, Wei-Xing, 2010. "Scaling and memory in the non-Poisson process of limit order cancelation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2751-2761.
    8. Anderson Palmeira & Éder Pereira & Paulo Ferreira & Luisa Maria Diele-Viegas & Davidson Martins Moreira, 2022. "Long-Term Correlations and Cross-Correlations in Meteorological Variables and Air Pollution in a Coastal Urban Region," Sustainability, MDPI, vol. 14(21), pages 1-12, November.
    9. Martin Magris & Jiyeong Kim & Esa Rasanen & Juho Kanniainen, 2017. "Long-range Auto-correlations in Limit Order Book Markets: Inter- and Cross-event Analysis," Papers 1711.03534, arXiv.org.
    10. Ruan, Yong-Ping & Zhou, Wei-Xing, 2011. "Long-term correlations and multifractal nature in the intertrade durations of a liquid Chinese stock and its warrant," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1646-1654.
    11. Francisco Gerardo Benavides-Bravo & Dulce Martinez-Peon & Ángela Gabriela Benavides-Ríos & Otoniel Walle-García & Roberto Soto-Villalobos & Mario A. Aguirre-López, 2021. "A Climate-Mathematical Clustering of Rainfall Stations in the Río Bravo-San Juan Basin (Mexico) by Using the Higuchi Fractal Dimension and the Hurst Exponent," Mathematics, MDPI, vol. 9(21), pages 1-11, October.
    12. Muchnik, Lev & Bunde, Armin & Havlin, Shlomo, 2009. "Long term memory in extreme returns of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4145-4150.
    13. Xiao, Di & Wang, Jun, 2021. "Attitude interaction for financial price behaviours by contact system with small-world network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    14. Alberto Ciacci & Takumi Sueshige & Hideki Takayasu & Kim Christensen & Misako Takayasu, 2020. "The microscopic relationships between triangular arbitrage and cross-currency correlations in a simple agent based model of foreign exchange markets," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    15. Seemann, Lars & Hua, Jia-Chen & McCauley, Joseph L. & Gunaratne, Gemunu H., 2012. "Ensemble vs. time averages in financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6024-6032.
    16. 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).
    17. Zhao, Xiaojun & Shang, Pengjian & Zhao, Chuang & Wang, Jing & Tao, Rui, 2012. "Minimizing the trend effect on detrended cross-correlation analysis with empirical mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 45(2), pages 166-173.
    18. Delignières, Didier & Marmelat, Vivien, 2014. "Strong anticipation and long-range cross-correlation: Application of detrended cross-correlation analysis to human behavioral data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 47-60.
    19. Xue Pan & Lei Hou & Mutua Stephen & Huijie Yang & Chenping Zhu, 2014. "Evaluation of Scaling Invariance Embedded in Short Time Series," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-27, December.
    20. Klaudia Kozlowska & Miroslaw Latka & Bruce J West, 2020. "Significance of trends in gait dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-25, October.

    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:energy:v:243:y:2022:i:c:s0360544221029911. 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/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.