Co-Occurrence Predictor for Wind Power Output
Publication: Journal of Energy Engineering
Volume 141, Issue 3
Abstract
Wind power prediction has received extensive attention in recent years. In the open literature, problems such as imprecise statistical data and inaccurate prediction models still exist. To solve these problems, the authors first used -means algorithm to cluster the measured data and initialized the centers by simulated annealing. Second, considering the co-occurrence information between wind power output and the relevant parameters, several matrices are introduced to calculate the co-occurrence number. Finally, a co-occurrence predictor was designed to calculate the wind power output by the approximate posterior probability. Experiments were conducted on a set of measured data. Experimental results suggest that this method outperforms the state of the art. Specifically, one only needs to calculate the co-occurrence tables, so the calculation of this model is less than any of the other wind power prediction model.
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References
Aarts, E., and Korst, J. (1988). Simulated annealing and Boltzmann machines: A stochastic approach to combinatorial optimization and neural computing, Wiley, New York.
Alessandrini, S., Sperati, S., and Pinson, P. (2013). “A comparison between the ECMWF and COSMO ensemble prediction systems applied to short-term wind power forecasting on real data.” Appl. Energy, 107(2013), 271–280.
Alexiadis, M. C., Dokopoulos, P. S., Sahsamanoglou, H. S., and Monous, I. M. (1998). “Short-term forecasting of wind speed and related electrical power.” Solar Energy, 63(1), 61–68.
Barber, C., Bockhorst, J., and Roebber, P. (2010). “Auto-regressive HMM inference with incomplete data for short-horizon wind forecasting.” Adv. Neural Inform. Process. Syst., 23(2010), 136–144.
Bossanyi, E. A. (1985). “Short-term wind prediction using Kalman filters.” Wind Eng., 9(1), 1–8.
Chen, P., Pedersen, T., Bak-Jensen, B., and Chen, Z. (2010). “ARIMA-based time series model of stochastic wind power generation.” IEEE Trans. Power Syst., 25(2), 667–676.
El-Fouly, T. H. M., El-Saadany, E. F., and Salama, M. M. A. (2006). “Grey predictor for wind energy conversion systems output power prediction.” IEEE Trans. Power Syst., 21(3), 1450–1452.
Guo, Z., Wu, J., Lu, H., and Wang, J. (2011). “A case study on a hybrid wind speed forecasting method using BP neural network.” Knowl. Base Syst., 24(7), 1048–1056.
Jiang, W. J., Joens, J. A., Dionysiou, D. D., and O’Shea, K. E. (2013). “Optimization of photocatalytic performance of coated glass microspheres using response surface methodology and the application for degradation of dimethyl phthalate.” J. Photochem. Photobiol. A Chem., 262(2013), 7–13.
Kamat, P. V. (2007). “Meeting the clean energy demand: Nano-structure architectures for solar energy conversion.” J. Phys. Chem., 111(7), 2834–2860.
Kou, P., Gao, F., and Guan, X. H. (2013). “Sparse online warped Gaussian process for wind power probabilistic forecasting.” Appl. Energy, 108(2013), 410–428.
Landberg, L. (1998). “A mathematical look at a physical power prediction model.” Wind Energy, 1(1), 23–28.
Lee, S. C., and Shih, L. H. (2011). “Forecasting of electricity costs based on an enhanced gray-based learning model: A case study of renewable energy in Taiwan.” Technol. Forecasting Soc. Change, 78(7), 1242–1253.
Lee, S. H., Lee, S. H., Lee, J., Jang, K., and Hur, N. (2010). “A numerical study for the optimal arrangement of ocean current turbine generators in the ocean current power parks.” Curr. Appl. Phys., 10(2), 137–141.
Li, R. P., and Mukaidono, M. (1995). “A maximum-entropy approach to fuzzy clustering.” Fuzzy Systems, Int. Joint Conf. 4th IEEE Int. Conf. on Fuzzy Systems and The 2nd Int. Fuzzy Engineering Symp., Proc., 1995 IEEE Int. Conf., Vol. 4, IEEE, New York, 2227–2232.
Liu, H., Tian, H. Q., Chen, C., and Li, Y. (2010). “A hybrid statistical method to predict wind speed and wind power.” Renew. Energy, 35(8), 1857–1861.
Lu, J., Yang, R., and Zhang, C. (2013). “Study of short-term wind power prediction based on advanced BP neural network model.” Informatics and management science IV, Springer, London, 169–177.
Pinson, P., Nielsen, H. A., Madsen, H., and Nielsen, T. S. (2008). “Local linear regression with adaptive orthogonal fitting for the wind power application.” Stat. Comput., 18(1), 59–71.
Shi, M., Sun, X., Tao, D., and Xu, C. (2012). “Exploiting visual word co-occurrence for image retrieval.” Proc., 20th ACM Int. Conf. on Multimedia, Association for Computing Machinery, New York, 69–78.
Torres, J. L., Garcia, A., Blas, M. D., and Francisco, A. D. (2005). “Forecast of hourly average wind speed with ARMA models in Navarre (Spain).” Solar Energy, 79(1), 65–77.
Tuo, H. F. (2013a). “Energy and exergy based working fluid selection for organic Rankine cycle recovering waste heat from high temperature solid oxide fuel cell and gas turbine hybrid systems.” Int. J. Energy Res., 37(14), 1831–1841.
Tuo, H. F. (2013b). “Thermal economic analysis of a transcritical Rankine power cycle with reheat enhancement for a low-grade heat source.” Int. J. Energy Res., 37(8), 857–867.
Wagsta, K., Cardie, C., Rogers, S., and Schroedl, S. (2001). “Constrained k-means clustering with background knowledge.” Proc., 18th Int. Conf. on Machine Learning, Williams College, Williamstown, MA, 577–584.
Yan, J., Liu, Y. Q., Han, S., and Qiu, M. (2013). “Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine.” Renew. Sustain. Energy Rev., 27(2013), 613–621.
Yeh, W. C., Yeh, Y. M., Chang, P. C., Ke, Y. C., and Chung, V. (2014). “Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization.” Int. J. Electr. Power Energy Syst., 55(2014), 741–748.
Zhang, Q., Lai, K. K., Niu, D. X., Wang, Q., and Zhang, X. B. (2012). “A fuzzy group forecasting model based on least squares support vector machine (LS-SVM) for short-term wind power.” Energies, 5(12), 3329–3346.
Zhou, W., Lou, C., Li, Z., Lu, L., and Yang, H. (2010). “Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation system.” Appl. Energy, 87(2), 380–389.
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© 2014 American Society of Civil Engineers.
History
Received: Dec 4, 2013
Accepted: Mar 4, 2014
Published online: Apr 7, 2014
Discussion open until: Sep 7, 2014
Published in print: Sep 1, 2015
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