IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v63y2014icp162-171.html
   My bibliography  Save this article

Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy

Author

Listed:
  • Weekes, S.M.
  • Tomlin, A.S.

Abstract

The feasibility of predicting the long-term wind resource at 22 UK sites using a measure-correlate-predict (MCP) approach based on just three months onsite wind speed measurements has been investigated. Three regression based techniques were compared in terms of their ability to predict the wind resource at a target site based on measurements at a nearby reference site. The accuracy of the predicted parameters of mean wind speed, mean wind power density, standard deviation of wind speeds and the Weibull shape factor was assessed, and their associated error distributions were investigated, using long-term measurements recorded over a period of 10 years. For each site, 120 wind resource predictions covering the entire data period were obtained using a sliding window approach to account for inter-annual and seasonal variations. Both the magnitude and sign of the prediction errors were found to be strongly dependent on the season used for onsite measurements. Averaged across 22 sites and all seasons, the best performing MCP approach resulted in mean absolute and percentage errors in the mean wind speed of 0.21 ms−1 and 4.8% respectively, and in the mean wind power density of 11 Wm−2 and 14%. The average errors were reduced to 3.6% in the mean wind speed and 10% in the mean wind power density when using the optimum season for onsite wind measurements. These values were shown to be a large improvement on the predictions obtained using an established semi-empirical model based on boundary layer scaling. The results indicate that the MCP approaches applied to very short onsite measurement periods have the potential to be a valuable addition to the wind resource assessment toolkit for small-scale wind developers.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:63:y:2014:i:c:p:162-171
    DOI: 10.1016/j.renene.2013.08.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148113004357
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2013.08.033?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. Lackner, Matthew A. & Rogers, Anthony L. & Manwell, James F., 2008. "The round robin site assessment method: A new approach to wind energy site assessment," Renewable Energy, Elsevier, vol. 33(9), pages 2019-2026.
    2. 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.
    3. Weekes, S.M. & Tomlin, A.S., 2013. "Evaluation of a semi-empirical model for predicting the wind energy resource relevant to small-scale wind turbines," Renewable Energy, Elsevier, vol. 50(C), pages 280-288.
    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. López-González, A. & Ranaboldo, M. & Domenech, B. & Ferrer-Martí, L., 2020. "Evaluation of small wind turbines for rural electrification: Case studies from extreme climatic conditions in Venezuela," Energy, Elsevier, vol. 209(C).
    2. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
    3. 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.
    4. 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.
    5. Drew, D.R. & Barlow, J.F. & Cockerill, T.T. & Vahdati, M.M., 2015. "The importance of accurate wind resource assessment for evaluating the economic viability of small wind turbines," Renewable Energy, Elsevier, vol. 77(C), pages 493-500.
    6. 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.
    7. 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.
    8. Izadyar, Nima & Ong, Hwai Chyuan & Chong, W.T. & Leong, K.Y., 2016. "Resource assessment of the renewable energy potential for a remote area: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 908-923.
    9. Katinas, Vladislovas & Marčiukaitis, Mantas & Gecevičius, Giedrius & Markevičius, Antanas, 2017. "Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania," Renewable Energy, Elsevier, vol. 113(C), pages 190-201.
    10. Herrero-Novoa, Cristina & Pérez, Isidro A. & Sánchez, M. Luisa & García, Ma Ángeles & Pardo, Nuria & Fernández-Duque, Beatriz, 2017. "Wind speed description and power density in northern Spain," Energy, Elsevier, vol. 138(C), pages 967-976.

    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. 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. Troncoso, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Riquelme, J.C. & Prieto, L., 2015. "Local models-based regression trees for very short-term wind speed prediction," Renewable Energy, Elsevier, vol. 81(C), pages 589-598.
    3. Woochul Nam & Ki-Yong Oh, 2020. "Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment," Mathematics, MDPI, vol. 8(10), pages 1-20, October.
    4. 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.
    5. 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.
    6. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
    7. 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.
    8. Deo, Ravinesh C. & Şahin, Mehmet & Adamowski, Jan F. & Mi, Jianchun, 2019. "Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 235-261.
    9. 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.
    10. Yossri, W. & Ben Ayed, S. & Abdelkefi, A., 2023. "Evaluation of the efficiency of bioinspired blade designs for low-speed small-scale wind turbines with the presence of inflow turbulence effects," Energy, Elsevier, vol. 273(C).
    11. Bush, Ruth & Jacques, David A. & Scott, Kate & Barrett, John, 2014. "The carbon payback of micro-generation: An integrated hybrid input–output approach," Applied Energy, Elsevier, vol. 119(C), pages 85-98.
    12. Millward-Hopkins, J.T. & Tomlin, A.S. & Ma, L. & Ingham, D.B. & Pourkashanian, M., 2013. "Mapping the wind resource over UK cities," Renewable Energy, Elsevier, vol. 55(C), pages 202-211.
    13. Allen, D.J. & Tomlin, A.S. & Bale, C.S.E. & Skea, A. & Vosper, S. & Gallani, M.L., 2017. "A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data," Applied Energy, Elsevier, vol. 208(C), pages 1246-1257.
    14. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    15. Millward-Hopkins, J.T. & Tomlin, A.S. & Ma, L. & Ingham, D.B. & Pourkashanian, M., 2013. "Assessing the potential of urban wind energy in a major UK city using an analytical model," Renewable Energy, Elsevier, vol. 60(C), pages 701-710.
    16. Osvaldo Rodriguez-Hernandez & Manuel Martinez & Carlos Lopez-Villalobos & Hector Garcia & Rafael Campos-Amezcua, 2019. "Techno-Economic Feasibility Study of Small Wind Turbines in the Valley of Mexico Metropolitan Area," Energies, MDPI, vol. 12(5), pages 1-26, March.
    17. Lo Zupone, Giacomo & Amelio, Mario & Barbarelli, Silvio & Florio, Gaetano & Scornaienchi, Nino Michele & Cutrupi, Antonino, 2017. "Lcoe evaluation for a tidal kinetic self balancing turbine: Case study and comparison," Applied Energy, Elsevier, vol. 185(P2), pages 1292-1302.
    18. Simões, Teresa & Estanqueiro, Ana, 2016. "A new methodology for urban wind resource assessment," Renewable Energy, Elsevier, vol. 89(C), pages 598-605.
    19. 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.
    20. 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.

    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:63:y:2014:i:c:p:162-171. 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.