IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v74y2015icp106-115.html
   My bibliography  Save this article

Performance analysis of the first method for long-term turbulence intensity estimation at potential wind energy sites

Author

Listed:
  • Casella, Livio

Abstract

The paper presents a validation test of a recent algorithm implemented by the author to correlate turbulence intensity (TI) data recorded at two meteorological masts and based on the conditional probability to measure simultaneous events of wind speed, direction and TI. Two testing sites, located about 5 km apart from each other in a hilly terrain, in the South of Australia, are considered in this work. Three years of concurrent data (2005–2008) are analyzed to estimate a long-term (LT) representative TI. A complete examination of the scores is carried out by spanning dimension and temporal period of the data samples used in the correlation analysis. Root mean square error, committed by the method to approximate mean value of TI measured in each of the three years, can be correlated with number of used months by exponential decay functions. The intermonthly variations stronger affect the accuracy of the results than the yearly ones. However, the average errors are always moderate and good performances are achieved for all the considered wind speed thresholds and also when examining different periods of the year. The tested methodology represents an important step through standardization of Measure-correlate-predict (MCP) technique for TI assessment.

Suggested Citation

  • Casella, Livio, 2015. "Performance analysis of the first method for long-term turbulence intensity estimation at potential wind energy sites," Renewable Energy, Elsevier, vol. 74(C), pages 106-115.
    • Handle: RePEc:eee:renene:v:74:y:2015:i:c:p:106-115
    DOI: 10.1016/j.renene.2014.07.031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114004170
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2014.07.031?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. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    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. Arenas-López, J. Pablo & Badaoui, Mohamed, 2020. "Stochastic modelling of wind speeds based on turbulence intensity," Renewable Energy, Elsevier, vol. 155(C), pages 10-22.
    2. Han, Qinkai & Hao, Zhuolin & Hu, Tao & Chu, Fulei, 2018. "Non-parametric models for joint probabilistic distributions of wind speed and direction data," Renewable Energy, Elsevier, vol. 126(C), pages 1032-1042.
    3. Enevoldsen, Peter, 2016. "Onshore wind energy in Northern European forests: Reviewing the risks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1251-1262.

    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. Raphael Calel & Jonathan Colmer & Antoine Dechezleprêtre & Matthieu Glachant, 2021. "Do carbon offsets offset carbon?," CEP Discussion Papers dp1808, Centre for Economic Performance, LSE.
    2. Aliashim Albani & Mohd Zamri Ibrahim & Kim Hwang Yong, 2018. "Influence of the ENSO and Monsoonal Season on Long-Term Wind Energy Potential in Malaysia," Energies, MDPI, vol. 11(11), pages 1-18, November.
    3. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    4. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    5. He, J.Y. & Chan, P.W. & Li, Q.S. & Huang, Tao & Yim, Steve Hung Lam, 2024. "Assessment of urban wind energy resource in Hong Kong based on multi-instrument observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    6. Kresning, Boma & Hashemi, M. Reza & Shirvani, Amin & Hashemi, Javad, 2024. "Uncertainty of extreme wind and wave loads for marine renewable energy farms in hurricane-prone regions," Renewable Energy, Elsevier, vol. 220(C).
    7. Soukissian, Takvor H. & Papadopoulos, Anastasios, 2015. "Effects of different wind data sources in offshore wind power assessment," Renewable Energy, Elsevier, vol. 77(C), pages 101-114.
    8. Cheng-Dar Yue & Che-Chih Liu & Chien-Cheng Tu & Ta-Hui Lin, 2019. "Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources," Energies, MDPI, vol. 12(4), pages 1-24, February.
    9. Weekes, S.M. & Tomlin, A.S. & Vosper, S.B. & Skea, A.K. & Gallani, M.L. & Standen, J.J., 2015. "Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure–correlate–predict," Renewable Energy, Elsevier, vol. 81(C), pages 760-769.
    10. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "Dual-meta pool method for wind farm power forecasting with small sample data," Energy, Elsevier, vol. 267(C).
    11. Frank, Christopher W. & Pospichal, Bernhard & Wahl, Sabrina & Keller, Jan D. & Hense, Andreas & Crewell, Susanne, 2020. "The added value of high resolution regional reanalyses for wind power applications," Renewable Energy, Elsevier, vol. 148(C), pages 1094-1109.
    12. Anfeng Zhu & Qiancheng Zhao & Xian Wang & Ling Zhou, 2022. "Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network," Energies, MDPI, vol. 15(9), pages 1-17, April.
    13. Zhang, Fei & Li, Peng-Cheng & Gao, Lu & Liu, Yong-Qian & Ren, Xiao-Ying, 2021. "Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting," Renewable Energy, Elsevier, vol. 169(C), pages 129-143.
    14. José V. P. Miguel & Eliane A. Fadigas & Ildo L. Sauer, 2019. "The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment," Energies, MDPI, vol. 12(19), pages 1-15, September.
    15. Mifsud, Michael D. & Sant, Tonio & Farrugia, Robert N., 2018. "A comparison of Measure-Correlate-Predict Methodologies using LiDAR as a candidate site measurement device for the Mediterranean Island of Malta," Renewable Energy, Elsevier, vol. 127(C), pages 947-959.
    16. Carta, José A. & Cabrera, Pedro, 2021. "Optimal sizing of stand-alone wind-powered seawater reverse osmosis plants without use of massive energy storage," Applied Energy, Elsevier, vol. 304(C).
    17. Toft, Henrik Stensgaard & Svenningsen, Lasse & Sørensen, John Dalsgaard & Moser, Wolfgang & Thøgersen, Morten Lybech, 2016. "Uncertainty in wind climate parameters and their influence on wind turbine fatigue loads," Renewable Energy, Elsevier, vol. 90(C), pages 352-361.
    18. Birgir Hrafnkelsson & Gudmundur V. Oddsson & Runar Unnthorsson, 2016. "A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation," Energies, MDPI, vol. 9(4), pages 1-14, April.
    19. Li, Yang & Shen, Xiaojun & Zhou, Chongcheng, 2023. "Dynamic multi-turbines spatiotemporal correlation model enabled digital twin technology for real-time wind speed prediction," Renewable Energy, Elsevier, vol. 203(C), pages 841-853.
    20. James, Eric P. & Benjamin, Stanley G. & Marquis, Melinda, 2017. "A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model," Renewable Energy, Elsevier, vol. 102(PB), pages 390-405.

    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:74:y:2015:i:c:p:106-115. 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.