Abstract
Although clean and sustainable wind energy has long been recognized as one of the most attractive electric power sources, generation of wind power is still much easier than its integration into liberalized electricity markets. One of the key obstacles on the way of wider implementation of wind energy is its highly volatile and intermittent nature. This has boosted an interest in developing a fully probabilistic forecast of wind speed, aiming to assess a variety of related uncertainties. Nonetheless, most of the available methodology for constructing a future predictive density for wind speed are based on parametric distributional assumptions on the observed wind data, and such conditions are often too restrictive and infeasible in practice. In this paper we propose a new nonparametric data-driven approach to probabilistic wind speed forecasting, adaptively combining sieve bootstrap and regime switching models. Our new bootstrapped regime switching (BRS) model delivers highly competitive, sharp and calibrated ensembles of wind speed forecasts, governed by various states of wind direction, and imposes minimal requirements on the observed wind data. The proposed methodology is illustrated by developing probabilistic wind speed forecasts for a site in the Washington State, USA.
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References
Abramowski, J., & Posorski, R. (2000). Wind energy in developing countries. DEWI Magazine, 16, 46–53.
Agrawal, M. R., Boland, J., & Ridley, B. (2013). Analysis of wind farm output: Estimation of volatility using high-frequency data. Environmental Modeling & Assessment, 18(4), 481–492.
Alexiadis, M. C., Dokopoulos, P. S., & Sahsamanoglou, H. S. (1999). Wind speed and power forecasting based on spatial correlation models. IEEE Transactions on Energy Conversion, 14(3), 836–842.
Alonso, A. M., Peña, D., & Romo, J. (2002). Forecasting time series with sieve bootstrap. Journal of Statistical Planning and Inference, 100, 1–11.
Anastasiades, G., & McSharry, P. (2013). Quantile forecasting of wind power using variability indices. Energies, 6(2), 662–695.
Bos, R., De Waele, S., & Broersen, P. M. T. (2002). Autoregressive spectral estimation by application of the Burg algorithm to irregularly sampled data. IEEE Transactions on Instrumentation and Measurement, 51(6), 1289–1294.
Bühlmann, P. (1997). Sieve bootstrap for time series. Bernoulli, 3(2), 123–148.
Bühlmann, P. (2002). Bootstraps for time series. Statistical Science, 17(1), 52–72.
Chen, B., Gel, Y. R., Balakrishna, N., & Abraham, B. (2011). Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes. Journal of Forecasting, 30(1), 51–71.
Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., & Feitosa, E. (2008). A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews, 12(6), 1725–1744.
Efron, B. (1982). The jackknife, the bootstrap and other resampling plans (Vol. 38). Society of Industrial and Applied Mathematics CBMS-NSF Monographs.
Epstein, E. S. (1969). A scoring system for probability forecasts of ranked categories. Journal of Applied Meteorology, 8(6), 985–987.
Erdogan, E., Ma, S., Beygelzimer, A., & Rish, I. (2004). Statistical models for unequally spaced time series. In Proceedings of the Fifth SIAM International Conference on Data Mining, SIAM, pp. 626–630.
Gel, Y., Raftery, A. E., & Gneiting, T. (2004). Calibrated probabilistic mesoscale weather field forecasting: The geostatistical output perturbation method. Journal of the American Statistical Association, 99(467), 575–583.
Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477), 359–378.
Gneiting, T., Raftery, A. E., Westveld, A. H, I. I. I., & Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 133(5), 1098–1118.
Gneiting, T., Larson, K., Westrick, K., Genton, M. G., & Aldrich, E. (2006). Calibrated probabilistic forecasting at the Stateline wind energy center: The regime-switching space-time method. Journal of the American Statistical Association, 101(475), 968–979.
Gray, B. R., Lyubchich, V., Gel, Y. R., Rogala, J. T., Robertson, D. M., & Wei, X. (2016). Estimation of river and stream temperature trends under haphazard sampling. Statistical Methods & Applications, 25(1), 89–105.
Hamill, T. M. (2001). Interpretation of rank histograms for verifying ensemble forecasts. Monthly Weather Review, 129(3), 550–560.
Hering, A. S., & Genton, M. G. (2010). Powering up with space-time wind forecasting. Journal of the American Statistical Association, 105(489), 92–104.
Jeon, J., & Taylor, J. W. (2012). Using conditional kernel density estimation for wind power density forecasting. Journal of the American Statistical Association, 107(497), 66–79.
Juban, J., Siebert, N., & Kariniotakis, G. N. (2007). Probabilistic short-term wind power forecasting for the optimal management of wind generation. In Proceedings of 2007 IEEE Lausanne Power Tech, pp. 683–688.
Kreiss, J. P., & Franke, J. (1992). Bootstrapping stationary autoregressive moving-average models. Journal of Time Series Analysis, 13(4), 297–317.
Larson, K. A., & Westrick, K. (2006). Short-term wind forecasting using off-site observations. Wind Energy, 9(1–2), 55–62.
Lau, A., & McSharry, P. (2010). Approaches for multi-step density forecasts with application to aggregated wind power. The Annals of Applied Statistics, 4(3), 1311–1341.
Lyubchich, V., Gray, B. R., & Gel, Y. R. (2015). Multilevel random slope approach and nonparametric inference for river temperature, under haphazard sampling. In Machine learning and data mining approaches to climate science (pp. 137–145). Cham: Springer.
McCulloch, R. E., & Tsay, R. S. (1994). Statistical analysis of economic time series via Markov switching models. Journal of Time Series Analysis, 15(5), 523–539.
Monteiro, C., Bessa, R., Miranda, V., Botterud, A., Wang, J., & Conzelmann, G. (2009). Wind power forecasting: State-of-the-art 2009. Tech. Rep. ANL/DIS-10-1, Decision and Information Sciences Division, Argonne National Laboratory (ANL).
Murphy, A. H. (1969). On the "ranked probability score". Journal of Applied Meteorology, 8(6), 988–989.
Palomares-Salas, J. C., de la Rosa, J. J. G., Ramiro, J. G., Melgar, J., Agüera, A., & Moreno, A. (2009). Comparison of models for wind speed forecasting. In Proceedings of The International Conference on Computational Science (ICCS).
Pinson, P. (2012). Very-short-term probabilistic forecasting of wind power with generalized logit-normal distributions. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(4), 555–576.
Pinson, P. (2013). Wind energy: Forecasting challenges for its operational management. Statistical Science, 28(4), 564–585.
Pinson, P., & Kariniotakis, G. (2010). Conditional prediction intervals of wind power generation. IEEE Transactions on Power Systems, 25(4), 1845–1856.
Pinson, P., & Madsen, H. (2012). Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models. Journal of Forecasting, 31(4), 281–313.
Politis, D. N. (2003). The impact of bootstrap methods on time series analysis. Statistical Science, 18(2), 219–230.
Tascikaraoglu, A., & Uzunoglu, M. (2014). A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews, 34, 243–254.
Wald, M. L. (2008). The energy challenge: Wind energy bumps into power grid’s limits. New York: New York Times.
Wan, C., Xu, Z., Pinson, P., Dong, Z. Y., & Wong, K. P. (2014). Optimal prediction intervals of wind power generation. IEEE Transactions on Power Systems, 29(3), 1166–1174.
Wang, X., Guo, P., & Huang, X. (2011). A review of wind power forecasting models. Energy Procedia, 12, 770–778.
Acknowledgements
The authors would like to thank the Bonneville Power Administration and the Oregon State University Energy Resources Research Laboratory, particularly, Stel Walker, for the generosity in providing the wind speed and direction data. We would like to thank William Weimin Yoo and Kimihiro Noguchi for the help at the initial stage of this project. Research of Ejaz Ahmed is supported in part by the Grant from the Natural Sciences and Engineering Research Council of Canada, and Yulia R. Gel is supported in part by the National Science Foundation Grant DMS1514808.
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Gel, Y.R., Lyubchich, V., Ahmed, S.E. (2016). Catching Uncertainty of Wind: A Blend of Sieve Bootstrap and Regime Switching Models for Probabilistic Short-Term Forecasting of Wind Speed. In: Li, W., Stanford, D., Yu, H. (eds) Advances in Time Series Methods and Applications . Fields Institute Communications, vol 78. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-6568-7_14
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DOI: https://doi.org/10.1007/978-1-4939-6568-7_14
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