Editorial Type:
Article
Article Type:
Research Article

A Short-Term Ensemble Wind Speed Forecasting System for Wind Power Applications

Justin J. Traiteur Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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David J. Callicutt Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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Maxwell Smith Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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Somnath Baidya Roy Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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Print Publication:
01 Oct 2012
DOI:
https://doi.org/10.1175/JAMC-D-11-0122.1
Page(s):
1763–1774
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Abstract

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 h ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model and persistence, autoregressive, and autoregressive moving-average models. The ensemble is calibrated against observations for a 6-month period (January–June 2006) at a potential wind-farm site in Illinois using the Bayesian model averaging technique. The forecasting system is evaluated against observations for the July 2006–December 2007 period at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble as well the time series models under all environmental stability conditions. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 min. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Corresponding author address: Somnath Baidya Roy, Dept. of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 105 S. Gregory St., Urbana, IL 61801. E-mail: sbroy@atmos.uiuc.edu

Abstract

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 h ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model and persistence, autoregressive, and autoregressive moving-average models. The ensemble is calibrated against observations for a 6-month period (January–June 2006) at a potential wind-farm site in Illinois using the Bayesian model averaging technique. The forecasting system is evaluated against observations for the July 2006–December 2007 period at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble as well the time series models under all environmental stability conditions. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 min. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Corresponding author address: Somnath Baidya Roy, Dept. of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 105 S. Gregory St., Urbana, IL 61801. E-mail: sbroy@atmos.uiuc.edu
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Journal of Applied Meteorology and Climatology

  • Bosveld, F. C., P. Baas, and A. A. M. Holtslag, 2010: The Third GABLS SCM Intercomparison and Evaluation Case. Preprints, 19th Symp. on Boundary Layer Turbulence, Keystone, CO, Amer. Meteor. Soc., 5.2. [Available online at http://ams.confex.com/ams/pdfpapers/172634.pdf.]

  • Bougeault, P., and P. Lacarrere, 1989: Parameterization of orography-induced turbulence in a mesobeta-scale model. Mon. Wea. Rev., 117, 1872–1890.

    • Search Google Scholar
    • Export Citation

  • Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 125, 2887–2908.

    • Search Google Scholar
    • Export Citation

  • Buizza, R., and Coauthors, 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133, 1076–1097.

    • Search Google Scholar
    • Export Citation

  • Cardell, J. B., and C. L. Anderson, 2009: Estimating the system costs of wind power forecast uncertainty. Proc. Power and Energy Society General Meeting, Calgary, AB, Canada, IEEE.

  • Costa, A., A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa, 2008: A review on the young history of the wind power short-term prediction. Renew. Sustain. Energy Rev., 12, 1725–1744.

    • Search Google Scholar
    • Export Citation

  • Dempster, A. P., N. M. Laird, and D. B. Rubin, 1977: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc., 39B, 1–38.

    • Search Google Scholar
    • Export Citation

  • Draxl, C., A. N. Hahmann, A. Pena, J. N. Nissen, and G. Giebel, 2010: Validation of boundary-layer winds from WRF mesoscale forecasts with applications to wind energy forecasting. Preprints, 19th Symp. on Boundary Layers and Turbulence, Keystone, CO, Amer. Meteor. Soc., 1B.1. [Available online at http://ams.confex.com/ams/pdfpapers/172440.pdf.]

  • Duran, M. J., D. Cros, and J. Riquelme, 2007: Short-term wind power forecast based on ARX models. J. Energy Eng., 133, 172–180.

  • Fabbri, A., T. G. S. Roman, J. R. Abbad, and V. H. M. Quezada, 2005: Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market. IEEE Trans. Power Syst., 20, 1440–1446.

    • Search Google Scholar
    • Export Citation

  • Fisher, R. A., 1922: On the mathematical foundations of theoretical statistics. Philos. Trans. Roy. Soc. London, 222A, 309–368.

  • Georgilakis, P. S., 2008: Technical challenges associated with the integration of wind power into power systems. Renew. Sustain. Energy Rev., 2, 852–863.

    • Search Google Scholar
    • Export Citation

  • Giebel, G., R. Brownsword, G. Kariniotakis, M. Denhard, and C. Draxl, 2011: The state-of-the-art in short-term prediction of wind power. Project ANEMOS Deliverable Rep. D1.2, Roskilde, Denmark. [Available online at http://www.prediktor.dk/publ/GGiebelEtAl-StateOfTheArtInShortTermPrediction_ANEMOSplus_2011.pdf.]

  • Gneiting, T., 2011: Making and evaluating point forecasts. J. Amer. Stat. Assoc., 106, 746–762.

  • Hacker, J. P., J. L. Anderson, and M. Pagowski, 2007: Improved vertical covariance estimates for ensemble-filter assimilation of near-surface observations. Mon. Wea. Rev., 135, 1021–1036.

    • Search Google Scholar
    • Export Citation

  • Holtslag, A. A. M., G. J. Steeneveld, and B. J. H. van de Wiel, 2007: Role of land-surface temperature feedback on model performance for the stable boundary layer. Bound.-Layer Meteor., 125, 361–376.

    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., and S.-W. Kim, 2008: Stable boundary layer mixing in a vertical diffusion scheme. Preprints, 18th Symp. on Boundary Layers and Turbulence, Stockholm, Sweden, Amer. Meteor. Soc., 16B.2. [Available online at http://ams.confex.com/ams/pdfpapers/140120.pdf.]

  • Janjić, Z. I., 1994: The step-mountain eta-coordinate model: Further developments of the convection, viscous sublayer and turbulent closure schemes. Mon. Wea. Rev., 122, 927–945.

    • Search Google Scholar
    • Export Citation

  • Janjić, Z. I., 2001: Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP meso model. NOAA/NWS/NCEP Office Note 437, 61 pp. [Available online at http://www.emc.ncep.noaa.gov/officenotes/newernotes/on437.pdf.]

  • Kulkarni, M. A., S. Patil, G. V. Rama, and P. N. Sen, 2008: Wind speed prediction using statistical regression and neural network. J. Earth Syst. Sci., 117, 457–463.

    • Search Google Scholar
    • Export Citation

  • Lei, M., L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, 2009: A review on the forecasting of wind speed and generated power. Renew. Sustain. Energy Rev., 13, 915–920.

    • Search Google Scholar
    • Export Citation

  • McLachlan, G. J., and T. Krishnan, 1997: The EM Algorithm and Extensions. John Wiley and Sons, 274 pp.

  • McSharry, P. E., S. Bouwman, and G. Bloemhof, 2005: Probabilistic forecasts of the magnitude and timing of peak electricity demand. IEEE Trans. Power Syst., 20, 1166–1172.

    • Search Google Scholar
    • Export Citation

  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF Ensemble Prediction System: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73–119.

    • Search Google Scholar
    • Export Citation

  • Monfared, M., H. Rastegar, and H. M. Kojabadi, 2009: A new strategy for wind speed forecasting using artificial intelligent methods. Renew. Energy, 34, 845–848.

    • Search Google Scholar
    • Export Citation

  • Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci. USSR, 151, 163–187.

    • Search Google Scholar
    • Export Citation

  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895–912.

    • Search Google Scholar
    • Export Citation

  • NARUC, 2007: FERC Order 890: What does it mean for the West? National Association of Regulatory Utility Commissioners (NARUC), National Wind Coordinating Collaborative (NWCC), and the Western Governors’ Association, 8 pp. [Available online at http://www.nationalwind.org/assets/publications/ferc890.pdf.]

  • Oztopal, A., 2006: Artificial neural network approach to spatial estimation of wind velocity data. Energy Convers. Manage., 47, 395–406.

    • Search Google Scholar
    • Export Citation

  • Palmer, T. N., 2000: Predicting uncertainty in forecasts of weather and climate. Rep. Prog. Phys., 63, 71–116.

  • Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1160–1174.

    • Search Google Scholar
    • Export Citation

  • Riahy, G. H., and M. Abedi, 2008: Short term wind speed forecasting for wind turbine applications using linear prediction method. Renew. Energy, 33, 35–41.

    • Search Google Scholar
    • Export Citation

  • Roquelaure, S., and T. Bergot, 2008: A local ensemble prediction for fog and low clouds: Construction, Bayesian model averaging calibration, and validation. J. Appl. Meteor. Climatol., 47, 3072–3088.

    • Search Google Scholar
    • Export Citation

  • Sfetsos, A., 2000: A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renew. Energy, 21, 23–35.

    • Search Google Scholar
    • Export Citation

  • Shin, H. H., and S.-Y. Hong, 2011: Intercomparison of planetary boundary layer parameterizations in the WRF model for a single day from CASES-99. Bound.-Layer Meteor., 139, 261–281, doi:10.1007/s10546-010-9583-z.

    • Search Google Scholar
    • Export Citation

  • Singh, S., T. S. Bhatti, and D. P. Kothari, 2007: Wind power estimation using artificial neural network. J. Energy Eng., 133, 46–52.

  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. D. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp.

  • Sloughter, J. M., A. E. Raftery, T. Gneiting, and C. Fraley, 2007: Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon. Wea. Rev., 135, 3209–3220.

    • Search Google Scholar
    • Export Citation

  • Sloughter, J. M., T. Gneiting, and A. E. Raftery, 2010: Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. J. Amer. Stat. Assoc., 105, 25–35, doi:10.1198/jasa.2009.ap08615.

    • Search Google Scholar
    • Export Citation

  • Smith, J. C., E. A. DeMeo, B. Parsons, and M. Milligan, 2004: Wind power impacts on electric power system operating costs: Summary and perspective on work to date. NREL/CP-500-35946, National Renewable Energy Laboratory, 13 pp. [Available online at http://www.nrel.gov/docs/fy04osti/35946.pdf.]

  • Sukoriansky, S., B. Galperin, and V. Perov, 2006: A quasi-normal scale-elimination model of turbulence and its application to stably stratified flows. Nonlinear Processes Geophys., 13, 9–22.

    • Search Google Scholar
    • Export Citation

  • Sumner, J., and C. Masson, 2006: Influence of atmospheric stability on wind turbine power performance curves. J. Sol. Energy Eng., 128, 531–538.

    • Search Google Scholar
    • Export Citation

  • Taylor, J. W., 2004: Forecasting weather variable densities for weather derivatives and energy prices. Modelling Prices in Competitive Electricity Markets, D. W. Bunn, Ed., Wiley, 307–330.

  • Taylor, J. W., and R. Buizza, 2006: Density forecasting for weather derivative pricing. Int. J. Forecasting, 22, 29–42.

  • Taylor, J. W., P. E. McSharry, and R. Buizza, 2009: Wind power density forecasting using ensemble predictions and time series models. IEEE Trans. Power Syst., 24, 775–782.

    • Search Google Scholar
    • Export Citation

  • Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297–3319.

  • Traiteur, J., 2011: A short-term wind speed forecasting system for wind power applications. M.S. thesis, Dept. of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 76 pp.

  • U.S. Department of Energy, 2008: 20% wind energy by 2030: Increasing wind energy’s contribution to U.S. electricity supply. DOE/GO-102008-2567, 248 pp. [Available online at http://www.20percentwind.org/20percent_wind_energy_report_revOct08.pdf.]

  • U.S. Department of Energy, cited 2011: U.S. installed capacity and wind project locations. [Available online at: http://www.windpoweringamerica.gov/wind_installed_capacity.asp.]

  • von Storch, H., and F. W. Zwiers, 2001: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

  • Wagner, R., M. S. Courtney, T. J. Larsen, and U. Schmidt Paulsen, 2010: Simulation of shear and turbulence impact on wind turbine performance. Risø DTU National Laboratory for Sustainable Energy, Riskilde, Denmark, 55 pp.

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