IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v81y2015icp760-769.html
   My bibliography  Save this article

Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure–correlate–predict

Author

Listed:
  • Weekes, S.M.
  • Tomlin, A.S.
  • Vosper, S.B.
  • Skea, A.K.
  • Gallani, M.L.
  • Standen, J.J.

Abstract

Output from a state-of-the-art, 4 km resolution, operational forecast model (UK4) was investigated as a source of long-term historical reference data for wind resource assessment. The data were used to implement measure–correlate–predict (MCP) approaches at 37 sites throughout the United Kingdom (UK). The monthly and hourly linear correlation between the UK4-predicted and observed wind speeds indicates that UK4 is capable of representing the wind climate better than the nearby meteorological stations considered. Linear MCP algorithms were implemented at the same sites using reference data from UK4 and nearby meteorological stations to predict the long-term (10-year) wind resource. To obtain robust error statistics, MCP algorithms were applied using onsite measurement periods of 1–12 months initiated at 120 different starting months throughout an 11 year data record. Using linear regression MCP over 12 months, the average percentage errors in the long-term predicted mean wind speed and power density were 3.0% and 7.6% respectively, using UK4, and 2.8% and 7.9% respectively, using nearby meteorological stations. The results indicate that UK4 is highly competitive with nearby meteorological observations as an MCP reference data source. UK4 was also shown to systematically improve MCP predictions at coastal sites due to better representation of local diurnal effects.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:81:y:2015:i:c:p:760-769
    DOI: 10.1016/j.renene.2015.03.066
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115002487
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2015.03.066?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. Weekes, S.M. & Tomlin, A.S., 2014. "Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP," Renewable Energy, Elsevier, vol. 68(C), pages 529-539.
    2. Monfared, Mohammad & Rastegar, Hasan & Kojabadi, Hossein Madadi, 2009. "A new strategy for wind speed forecasting using artificial intelligent methods," Renewable Energy, Elsevier, vol. 34(3), pages 845-848.
    3. Velázquez, Sergio & Carta, José A. & Matías, J.M., 2011. "Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kWh produced by a wind turbine at a candidate site: A case study in the Canary Islands," Applied Energy, Elsevier, vol. 88(11), pages 3869-3881.
    4. Weekes, S.M. & Tomlin, A.S., 2014. "Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy," Renewable Energy, Elsevier, vol. 63(C), pages 162-171.
    5. 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. 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.
    2. Antonio Rosato & Achille Perrotta & Luigi Maffei, 2024. "Commercial Small-Scale Horizontal and Vertical Wind Turbines: A Comprehensive Review of Geometry, Materials, Costs and Performance," Energies, MDPI, vol. 17(13), pages 1-50, June.
    3. 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.
    4. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    5. Elshafei, Basem & Peña, Alfredo & Popov, Atanas & Giddings, Donald & Ren, Jie & Xu, Dong & Mao, Xuerui, 2023. "Offshore wind resource assessment based on scarce spatio-temporal measurements using matrix factorization," Renewable Energy, Elsevier, vol. 202(C), pages 1215-1225.
    6. Romanic, Djordje & Parvu, Dan & Refan, Maryam & Hangan, Horia, 2018. "Wind and tornado climatologies and wind resource modelling for a modern development situated in “Tornado Alley”," Renewable Energy, Elsevier, vol. 115(C), pages 97-112.
    7. Jianxiao Wang & Liudong Chen & Zhenfei Tan & Ershun Du & Nian Liu & Jing Ma & Mingyang Sun & Canbing Li & Jie Song & Xi Lu & Chin-Woo Tan & Guannan He, 2023. "Inherent spatiotemporal uncertainty of renewable power in China," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    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. 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.
    2. Weekes, S.M. & Tomlin, A.S., 2014. "Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP," Renewable Energy, Elsevier, vol. 68(C), pages 529-539.
    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. 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.
    5. Wen-Ko Hsu & Chung-Kee Yeh, 2021. "Offshore Wind Potential of West Central Taiwan: A Case Study," Energies, MDPI, vol. 14(12), pages 1-20, June.
    6. Carta, José A. & Cabrera, Pedro & Matías, José M. & Castellano, Fernando, 2015. "Comparison of feature selection methods using ANNs in MCP-wind speed methods. A case study," Applied Energy, Elsevier, vol. 158(C), pages 490-507.
    7. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    8. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
    9. Zhang, Jie & Draxl, Caroline & Hopson, Thomas & Monache, Luca Delle & Vanvyve, Emilie & Hodge, Bri-Mathias, 2015. "Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods," Applied Energy, Elsevier, vol. 156(C), pages 528-541.
    10. Tang, Xiao-Yu & Zhao, Shumian & Fan, Bo & Peinke, Joachim & Stoevesandt, Bernhard, 2019. "Micro-scale wind resource assessment in complex terrain based on CFD coupled measurement from multiple masts," Applied Energy, Elsevier, vol. 238(C), pages 806-815.
    11. 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.
    12. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
    13. Díaz, Santiago & Carta, José A. & Matías, José M., 2018. "Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques," Applied Energy, Elsevier, vol. 209(C), pages 455-477.
    14. Sergio Velázquez Medina & José A. Carta & Ulises Portero Ajenjo, 2019. "Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands," Complexity, Hindawi, vol. 2019, pages 1-11, March.
    15. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    16. Pham, An & Jin, Tongdan & Novoa, Clara & Qin, Jin, 2019. "A multi-site production and microgrid planning model for net-zero energy operations," International Journal of Production Economics, Elsevier, vol. 218(C), pages 260-274.
    17. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    18. Raphael Calel & Jonathan Colmer & Antoine Dechezleprêtre & Matthieu Glachant, 2021. "Do carbon offsets offset carbon?," CEP Discussion Papers dp1808, Centre for Economic Performance, LSE.
    19. Xiao Liu & Xu Lai & Jin Zou, 2017. "A New MCP Method of Wind Speed Temporal Interpolation and Extrapolation Considering Wind Speed Mixed Uncertainty," Energies, MDPI, vol. 10(8), pages 1-21, August.
    20. 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.

    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:81:y:2015:i:c:p:760-769. 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.