1,079
Views
9
CrossRef citations to date
0
Altmetric
Research Article

Wind Speed Predictability Accuracy with Height Using LiDAR Based Measurements and Artificial Neural Networks

, , , &
Pages 605-622 | Received 19 Jun 2020, Accepted 19 Mar 2021, Published online: 06 May 2021

References

  • Akçay, H., and T. Filik. 2017. Short-term wind speed forecasting by spectral analysis from long-term observations with missing values. Applied Energy 191:653–62. doi:10.1016/j.apenergy.2017.01.063.
  • Akdag˘, S. A., H. S. Bagiorgas, and G. Mihalakakou. 2010. Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean. Applied Energy 87 (8):2566–73. doi:10.1016/j.apenergy.2010.02.033.
  • Aksoy, H., Z. F. Toprak, and A. Aytek. 2004. Unal NE. Stochastic generation of hourly mean wind speed data. Renewable Energy 29 (14):2111–31. doi:10.1016/j.renene.2004.03.011.
  • Carapellucci, R., and L. Giordano. 2013. The effect of diurnal profile and seasonal wind regime on sizing grid-connected and off-grid wind power plants. Applied Energy 107:364–76. doi:10.1016/j.apenergy.2013.02.044.
  • Catalão, J. P. S., H. M. I. Pousinho, and V. M. F. Mendes. 2011. Short-term wind power forecasting in Portugal by neural networks and wavelet Transform. Renewable Energy 36 (4):1245–51. doi:10.1016/j.renene.2010.09.016.
  • Chang, T. P. 2011. Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy 88 (1):272–82. doi:10.1016/j.apenergy.2010.06.018.
  • Doucoure, B., K. Agbossou, and A. Cardenas. 2016. Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data. Renewable Energy 92:202–11. doi:10.1016/j.renene.2016.02.003.
  • Filik, Ü. B., and T. Filik. 2017. Wind Speed Prediction Using Artificial Neural Networks Based on Multiple Local Measurements in Eskisehir. Energy Procedia 107 (February):264–69. doi:10.1016/j.egypro.2016.12.147.
  • Global Wind Report 2017 – Annual market update (GWEC-2017), http://gwec.net/wp-content/uploads/vip/GWEC_PRstats2017_EN-003_FINAL.pdf ( Accessed on 3rd Ma 2018)
  • Hoolohan, V., A. S. Tomlin, and T. Cockerill. 2018. Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data. Renewable Energy 126:1043–54. doi:10.1016/j.renene.2018.04.019.
  • Hu, Q., R. Zhang, and Y. Zhou. 2016. Transfer learning for short-term wind speed prediction with deep neural networks. Renewable Energy 85:83–95. doi:10.1016/j.renene.2015.06.034.
  • Jaramillo, O. A., and M. A. Borja. 2004. Winds peed analysis in La Ventosa, Mexico: A bimodal probability distribution case. Renewable Energy 29 (10):1613–30. doi:10.1016/j.renene.2004.02.001.
  • Jianzhou, W. J., S. Qin, Q. Zhou, and H. Jiang. 2015. Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China. Renewable Energy 76 (4):91–101. doi:10.1016/j.renene.2014.11.011.
  • Kalogirou, S. A., G. A. Florides, P. D. Pouloupatis, P. Christodoulides, and J. Joseph-Stylianou. 2015. Artificial neural networks for the generation of a conductivity map of the ground. Renewable Energy 77:400–07. doi:10.1016/j.renene.2014.12.033.
  • Kalogirou, S. A., S. Panteliou, and A. Dentsoras. 1999. Artificial neural networks used for the performance prediction of a thermosiphon solar water heater. Renewable Energy 18 (1):87–99. doi:10.1016/S0960-1481(98)00787-3.
  • Kaneko, T., A. Uehara, T. Senjyu, A. Yona, and N. Urasaki. 2011. An integrated control method for a wind farm to reduce frequency deviations in a small power system. Applied Energy 88 (4):1049–58. doi:10.1016/j.apenergy.2010.09.024.
  • Kang, A., Q. Tan, X. Yuan, X. Lei, and Y. Yuan, Short-Term Wind Speed Prediction Using EEMD-LSSVM Model, Advances in Meteorology Volume 2017, Article ID 6856139, 22 pages.
  • Koo, J., G. D. Han, H. J. Choi, and J. H. Shim. 2015. Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea. Energy 93:1296–302. doi:10.1016/j.energy.2015.10.026.
  • Liu, H., C. Chen, H. Qi Tian, and Y. Fei Li. 2012. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renew. Energy 48:545–56. (0). doi:10.1016/j.renene.2012.06.012.
  • Liu, H., H. Tian, D. Pan, and Y. Li. 2013. Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks. Applied Energy 107:191–208. doi:10.1016/j.apenergy.2013.02.002.
  • Liu, H., H. Tian, X. Liang, and Y. Li. 2015. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Applied Energy 157 (11):183–94. doi:10.1016/j.apenergy.2015.08.014.
  • Liu, H., H.-Q. Tian, and C. Chen. fei Li Y. 2010.A hybrid statistical method to predict wind speed and wind power, Renew. Energy 358:1857–1861. 10.1016/j.renene.2009.12.011
  • Marović, I., I. Sušanj, and N. Ožanić, Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System, Complexity Volume 2017, Article ID 3418145, 10 pages.
  • Marquardt, D. W. 1963. An algorithm for least-squares estimation of nonlinear parameters. Journal for the Society Industrial and Applied Mathematics 11 (2):431–41. doi:10.1137/0111030.
  • Mellit, A., S. A. Kalogirou, and M. Drif. 2010. Application of neural networks and genetic algorithms for sizing of photovoltaic systems. Renewable Energy 35 (12):2881–93. doi:10.1016/j.renene.2010.04.017.
  • Mohandes, M., S. Rehman, and T. Halawani. 1998. A neural networks approach for wind speed prediction. Renew. Energy 13 (3):345–54. doi:10.1016/S0960-1481(98)00001-9.
  • Mohandes, M., T. Halawani, S. Rehman, and A. A. Hussain. 2004. Support vector machines for wind speed prediction. Renew. Energy 29 (6):939–47. doi:10.1016/j.renene.2003.11.009.
  • Mohandes, M., T. Halawani, S. Rehman, and A. A. Hussain. 2007. A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation. Neurocomputing 70 (79):1525–42. doi:10.1016/j.neucom.2006.01.032.
  • Moreno, S. R., L. Coelho, and S. Dos. 2018. Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference. System, Renewable Energy 126:736–54. doi:10.1016/j.renene.2017.11.089.
  • Naik, J., R. Bisoi, and P. K. Dash. 2018. Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression. Renewable Energy 129:357–83. doi:10.1016/j.renene.2018.05.031.
  • Niu, T., J. Wang, K. Zhang, and P. Du. 2018. Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy. Renewable Energy 118:213–29. doi:10.1016/j.renene.2017.10.075.
  • Rehman, S. 2009. Empirical Model Development and Comparison with Existing Correlations. Applied Energy 64 (1–4):369–78. doi:10.1016/S0306-2619(99)00108-7.
  • Reza, T. 2013. Comparison result of inversion of gravity data of a fault by particle swarm optimization and levenberg-marquardt methods. Springer Plus. doi:10.1186/2193-1801-2-462.
  • Rosin, C. D., Halliday, R. S., Hart, W. E., R.K. Below, R. K.1997. A comparison of global and local search methods in drug docking. In Proceedings of the seventh International Conference on Genetic Algorithms:221-229. T. Baeck, Ed., Morgan Kaufmann, Pub. San Francisco, CA.
  • Santamaría-Bonfil, G., A. Reyes-Ballesteros, and C. Gershenson. 2016. Wind speed forecasting for wind farms: A method based on support vector regression. Renewable Energy 85 (1):790–809. doi:10.1016/j.renene.2015.07.004.
  • Şencan, A., K. A. Yakut, and S. A. Kalogirou. 2006. Thermodynamic analysis of absorption systems using artificial neural network. Renewable Energy Pages. 31 (1):29–43. doi:10.1016/j.renene.2005.03.011.
  • Sfetsos, A. 2000. A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renew. Energy 21 (1):23–35. doi:10.1016/S0960-1481(99)00125-1.
  • Shukur, O. B., and M. H. Lee. 2015. Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA. Renewable Energy 76 (4):637–47. doi:10.1016/j.renene.2014.11.084.
  • Voyant, C., G. Notton, S. A. Kalogirou, N. Marie-Laure, C. Paoli, F. Motte, and A. Fouilloy. 2017. Machine learning methods for solar radiation forecasting: A review. Renewable Energy 105:569–82. doi:10.1016/j.renene.2016.12.095.
  • Wang, Y., J. Wang, and X. Wei. 2015. A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China. Energy 91 (11):556–72. doi:10.1016/j.energy.2015.08.039.
  • Wu, X., Z. Zhu, X. Su, S. Fan, Z. Du, Y. Chang, and Q. Zeng. 2015. A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction. Energy 88:194–201. doi:10.1016/j.energy.2015.04.075.
  • Ye, L., Y. Zhao, C. Zeng, and C. Zhang. 2017. Short-term wind power prediction based on spatial model. Renewable Energy 101:1067–74. doi:10.1016/j.renene.2016.09.069.
  • Zhang, C., H. Wei, J. Zhao, T. Liu, T. Zhu, and K. Zhang. 2016. Short-term wind speed forecasting using empirical mode decomposition and feature selection. Renewable Energy 96:727–37. doi:10.1016/j.renene.2016.05.023.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.