This paper presents a novel approach for accurately modeling and ultimately predicting wind speed for selected sites when incomplete data sets are available. The application of a seasonal simulation for the synthetic generation of wind speed data is achieved using the Markov chain Monte Carlo technique with only one month of data from each season. This limited data model was used to produce synthesized data that sufficiently captured the seasonal variations of wind characteristics. The model was validated by comparing wind characteristics obtained from time series wind tower data from two countries with Markov chain Monte Carlo simulations, demonstrating that one month of wind speed data from each season was sufficient to generate synthetic wind speed data for the related season. 1. Introduction One of the most challenging features of wind energy application is the uncertainty of the wind resource. Wind speed and hence wind energy potential has a key influence on the profitability of a wind farm and on the management of transmission and distribution networks by utility systems operators. Accurate and reliable data related to both long-term and short-term wind characteristics are essential for site selection and technology specification [1]. Wind data acquisition is typically required for one to two years and is achieved through the use of anemometers and 60？m meteorological towers. Because of the stochastic nature of the wind resource, long-term wind speed recordings are required to characterize wind patterns, but in some cases wind data may be corrupted or missing for short periods, making accurate and reliable prediction of wind characteristics difficult. Operational wind speed prediction is also a challenge with historic data playing a major role in the prediction of future wind data, both of which require long-term reliable and complete historic data. The topic of wind speed prediction has been addressed by a number of researchers with approaches typically based on the analysis of historical time series wind data [2] or model based using a range of meteorological and/or local topographical input data. Lei et al. [3] provide a comprehensive overview of wind speed forecasting and power prediction methods using numerical and statistical approaches and conclude that different models have different strengths based on inputs and applications such as short-term or long-term prediction. Wind speed data is typically analyzed to characterize the potential annual resource, presented, for example, as a Weibull Density Function [4] for the estimation of wind
References
[1]
“The European Wind Energy Association, Wind Energy—The Facts,” 2011, http://www.ewea.org/fileadmin/ewea_documents/documents/publications/WETF/1565_ExSum_ENG.pdf.
[2]
C. Stathopoulos, A. Kaperoni, G. Galanis, and G. Kallos, “Wind power prediction based on numerical and statistical models,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 112, pp. 25–38, 2013.
[3]
M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, vol. 13, no. 4, pp. 915–920, 2009.
[4]
D. Weisser, “A wind energy analysis of Grenada: an estimation using the “Weibull” density function,” Renewable Energy, vol. 28, no. 11, pp. 1803–1812, 2003.
[5]
A. M. Foley, P. G. Leahy, A. Marvuglia, and E. J. McKeogh, “Current methods and advances in forecasting of wind power generation,” Renewable Energy, vol. 37, no. 1, pp. 1–8, 2012.
[6]
D. Watts and D. Jara, “Statistical analysis of wind energy in Chile,” Renewable Energy, vol. 36, no. 5, pp. 1603–1613, 2011.
[7]
E. Cadenas and W. Rivera, “Wind speed forecasting in the South Coast of Oaxaca, México,” Renewable Energy, vol. 32, no. 12, pp. 2116–2128, 2007.
[8]
I. Fyrippis, P. J. Axaopoulos, and G. Panayiotou, “Wind energy potential assessment in Naxos Island, Greece,” Applied Energy, vol. 87, no. 2, pp. 577–586, 2010.
[9]
A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa, “A review on the young history of the wind power short-term prediction,” Renewable and Sustainable Energy Reviews, vol. 12, no. 6, pp. 1725–1744, 2008.
[10]
I. J. Ramirez-Rosado, L. A. Fernandez-Jimenez, C. Monteiro, J. Sousa, and R. Bessa, “Comparison of two new short-term wind-power forecasting systems,” Renewable Energy, vol. 34, no. 7, pp. 1848–1854, 2009.
[11]
L. Landberg, “Short-term prediction of the power production from wind farms,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 80, no. 1-2, pp. 207–220, 1999.
[12]
P. Louka, G. Galanis, N. Siebert et al., “Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 96, no. 12, pp. 2348–2362, 2008.
[13]
R. Calif and F. G. Schmitt, “Modeling of atmospheric wind speed sequence using a lognormal continuous stochastic equation,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 109, pp. 1–8, 2012.
[14]
L. Morales, F. Lang, and C. Mattar, “Mesoscale wind speed simulation using CALMET model and reanalysis information: an application to wind potential,” Renewable Energy, vol. 48, pp. 57–71, 2012.
[15]
W. Wang, W. J. Shaw, T. E. Seiple, J. P. Rishel, and Y. Xie, “An evaluation of a diagnostic wind model (CALMET),” Journal of Applied Meteorology and Climatology, vol. 47, no. 6, pp. 1739–1756, 2008.
[16]
Z.-H. Guo, J. Wu, H.-Y. Lu, and J.-Z. Wang, “A case study on a hybrid wind speed forecasting method using BP neural network,” Knowledge-Based Systems, vol. 24, no. 7, pp. 1048–1056, 2011.
[17]
H. Liu, C. Chen, H. Q. Tian, and Y. F. Li, “A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks,” Renewable Energy, vol. 48, pp. 545–556, 2012.
[18]
S. Han, Y. Liu, and Y. Yang, “Taboo search algorithm based ANN model for wind speed prediction,” in Proceedings of the 2nd IEEE Conference on Industrial Electronics and Applications, pp. 2599–2602, 2007.
[19]
D. I. Jones and M. H. Lorenz, “An application of a Markov chain noise model to wind generator simulation,” Mathematics and Computers in Simulation, vol. 28, no. 5, pp. 391–402, 1986.
[20]
A. D. Sahin and Z. Sen, “First-order Markov chain approach to wind speed modelling,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 89, no. 3-4, pp. 263–269, 2001.
[21]
H. Nfaoui, H. Essiarab, and A. A. M. Sayigh, “A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco,” Renewable Energy, vol. 29, no. 8, pp. 1407–1418, 2004.
[22]
Y. F. Ettoumi, H. Sauvageot, and A.-E.-H. Adane, “Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution,” Renewable Energy, vol. 28, no. 11, pp. 1787–1802, 2003.
[23]
S. Karatepe, Modeling the economic value of renewable wind energy with Markov chain method: an application in Yalova, Turkey [M.S. thesis], Department of Econometrics, Institute of Social Sciences, Uludag University, Uludag, Turkey, 2011.
[24]
F. O. Hocao？lu, O. N. Gerek, and M. Kurban, The Effect of Markov Chain State Size for Synthetic Wind Speed Generation, IEEE Xplore, 2009.
[25]
F. Kaminsky, R. H. Kirchoff, C. Y. Syu, and J. F. Manwell, “A comparison of alternative approaches for the synthetic generation of wind speed time series,” Journal of Solar Energy Engineering, vol. 113, pp. 280–289, 1991.
[26]
A. Shamshad, M. A. Bawadi, W. M. A. Wan Hussin, T. A. Majid, and S. A. M. Sanusi, “First and second order Markov chain models for synthetic generation of wind speed time series,” Energy, vol. 30, no. 5, pp. 693–708, 2005.
[27]
G. Papaefthymiou and B. Kl？ckl, “MCMC for wind power simulation,” IEEE Transactions on Energy Conversion, vol. 23, no. 1, pp. 234–240, 2008.
[28]
K. Brokish and J. Kirtley, Pitfalls of Modeling Wind Power Using Markov Chains, IEEE Xplore, 2009.
[29]
T. Wu, X. Ai, W. Lin, J. Wen, and L. Weihua, Markov Chain Monte Carlo Method for the Modeling of Wind Power Time Series, IEEE XPlore, 2012.
[30]
P. Erto, A. Lanzotti, and A. Lepore, “Wind speed parameter estimation from one-month sample via Bayesian approach,” Quality and Reliability Engineering International, vol. 26, no. 8, pp. 853–862, 2010.
[31]
W.-K. Pang, J. J. Forster, and M. D. Troutt, “Estimation of wind speed distribution using Markov chain Monte Carlo techniques,” Journal of Applied Meteorology, vol. 40, no. 8, pp. 1476–1484, 2001.
[32]
N. B. Negra, O. Holmstr？m, B. Bak-Jensen, and P. S？rensen, “Model of a synthetic wind speed time series generator,” Wind Energy, vol. 11, no. 2, pp. 193–209, 2008.
[33]
W. L. Winston, Operations Research: Applications and Algorithms, Duxburg Press, Duxburg, Calif, USA, 1994.
[34]
M. Hollander and A. D. Wolfe, Nonparametric Statistical Methods, John Wiley & Sons, Hoboken, NJ, USA, 2nd edition, 1999.
