Investigation of Data Size Variability in Wind Speed Prediction Using AI Algorithms

References

• Alin, A. 2010. Multicollinearity. Wiley Interdisciplinary Reviews: Computational Statistics 2 (3):370–4. doi:10.1002/wics.84.
   Google Scholar
• Breiman, L. 1996. Bagging predictors. Machine Learning 24 (2):123–40. doi:10.1023/A:1018054314350.
   Web of Science ®Google Scholar
• Brownlee, J. 2018. Statistical methods for machine learning: Discover how to transform data into knowledge with Python. Machine Learning Mastery.
   Google Scholar
• Bruce, P. C., and A. Bruce. 2017. Practical statistics for data scientists: 50 Essential concepts. 1st ed. Sebastopol, CA: O'Reilly Media, Inc.
   Google Scholar
• Bzdok, D., N. Altman, and M. Krzywinski. 2018. Statistics versus machine learning. Nature Methods 15 (4):233–4. doi:10.1038/nmeth.4642.
   PubMed Web of Science ®Google Scholar
• Cao, Y., Q.-G. Miao, J.-C. Liu, and L. Gao. 2013. Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica 39 (6):745–58. doi:10.1016/S1874-1029(13)60052-X.
   Google Scholar
• Cetinay, H., F. A. Kuipers, and A. N. Guven. 2017. Optimal siting and sizing of wind farms. Renewable Energy 101:51–8. doi:10.1016/j.renene.2016.08.008.
   Web of Science ®Google Scholar
• Chen, D., S. Liu, P. Kingsbury, S. Sohn, C. B. Storlie, E. B. Habermann, J. M. Naessens, D. W. Larson, and H. Liu. 2019. Deep learning and alternative learning strategies for retrospective real-world clinical data. NPJ Digital Medicine 2 (1):43. doi:10.1038/s41746-019-0122-0.
   PubMedGoogle Scholar
• Dodge, Y. 2008. Spearman rank correlation coefficient. In The concise encyclopedia of statistics, 502–5. New York, NY: Springer New York.
   Google Scholar
• Ehsan, B. M. A., F. Begum, S. J. Ilham, and R. S. Khan. 2019. Advanced wind speed prediction using convective weather variables through machine learning application. Applied Computing and Geosciences 1:100002. DOI: 10.1016/J.ACAGS.2019.100002.
   Google Scholar
• Exterkate, P., P. J. F. Groenen, C. Heij, and D. van Dijk. 2016. Nonlinear forecasting with many predictors using kernel ridge regression. International Journal of Forecasting 32 (3):736–53. doi:10.1016/j.ijforecast.2015.11.017.
   Web of Science ®Google Scholar
• Filik, Ü. B., and T. Filik. 2017. Wind speed prediction using artificial neural networks based on multiple local measurements in Eskisehir. Energy Procedia 107:264–9. doi:10.1016/j.egypro.2016.12.147.
   Google Scholar
• Ghasemi, A., and S. Zahediasl. 2012. Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism 10 (2):486–9. doi:10.5812/ijem.3505.
   PubMedGoogle Scholar
• Giarré, L., and F. Argenti. 2018. Mixed ℓ2 and ℓ1-norm regularization for adaptive detrending with ARMA modeling. Journal of the Franklin Institute 355 (3):1493–511. doi:10.1016/j.jfranklin.2017.12.009.
   Web of Science ®Google Scholar
• Goodfellow, I. 2016. Deep learning. Cambridge, MA: MIT Press.
   Google Scholar
• Gulli, A. 2017. Deep learning with Keras. 1st ed. Birmingham: Packt Publishing.
   Google Scholar
• Hill, J., and A. Gelman. 2007. Data analysis usinf regression and multilevel/hierarchical models. New York, NY: Cambridge University Press.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Ibrahim, A. M., and B. Bennett. 2014. The assessment of machine learning model performance for predicting alluvial deposits distribution. Procedia Computer Science 36:637–42. doi:10.1016/j.procs.2014.09.067.
   Google Scholar
• Illarionov, E., and R. Khudorozhkov. 2018. Not quite unreasonable effectiveness of machine learning algorithms. https://arxiv.org/abs/1804.02543.
   Google Scholar
• Jager, D., and A. Andreas. 1996. NREL National Wind Technology Center (NWTC): M2 Tower; Boulder, CO (Data); NREL Report No. DA-5500-56489. doi:10.5439/1052222.
   Google Scholar
• James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. Statistical learning. In An introduction to statistical learning: With applications, ed. R. G. James, D. Witten, T. Hastie, and R. Tibshirani, 15–57. New York, NY: Springer New York.
   Google Scholar
• Joshi, P. 2016. Python: Real world machine learning. Birmingham: Packt Publishing.
   Google Scholar
• Kanal, L. N. 2001. Perceptrons. In International encyclopedia of the social & behavioral sciences, ed. N. J. Smelser and P. B. Baltes, 11218–21. Oxford: Pergamon.
   Google Scholar
• Kiranyaz, S. 2019. 1D convolutional neural networks and applications: A survey. https://arxiv.org/abs/1905.03554.
   Google Scholar
• Liang, J., W. S. Y. Pan, and Z.-H. Yang. 2004. Characterization-based Q–Q plots for testing multinormality. Statistics & Probability Letters 70 (3):183–90. doi:10.1016/J.SPL.2004.10.002.
   Web of Science ®Google Scholar
• Liu, H., X. Mi, and Y. Li. 2018a. Smart deep learning-based wind speed prediction model using wavelet packet decomposition, convolutional neural network, and convolutional long short term memory network. Energy Conversion and Management 166:120–31. doi:10.1016/j.enconman.2018.04.021.
   Web of Science ®Google Scholar
• Liu, H., X. Mi, and Y. Li. 2018b. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Conversion and Management 159:54–64. doi:10.1016/j.enconman.2018.01.010.
   Web of Science ®Google Scholar
• Liu, J. N. K., Y. Hu, J. J. You, and P. W. Chan. 2014. Deep neural network-based feature representation for weather forecasting. In Proceedings of International Conference on Artificial Intelligence (ICAI), Las Vegas.
   Google Scholar
• Marcoulides, K. M., and T. Raykov. 2019. Evaluation of variance inflation factors in regression models using latent variable modeling methods. Educational and Psychological Measurement 79 (5):874–82. doi:10.1177/0013164418817803.
   PubMed Web of Science ®Google Scholar
• Matson, J. E., and B. R. Huguenard. 2007. Evaluating aptness of a regression model. Journal of Statistics Education 15 (2). doi:10.1080/10691898.2007.11889469.
   Google Scholar
• Mehryar Mohri, A. R., and A. Talwalka. 2012. Foundations of machine learning. Cambridge, MA: The MIT Press.
   Google Scholar
• Office of Energy Efficiency & Renewable Energy. 2018a. Wind Technologies Market Report. Washington, DC: U.S. Department of Energy.
   Google Scholar
• Office of Energy Efficiency & Renewable Energy. 2018b. Offshore Wind Technologies Market Report. Washington, DC: U.S. Department of Energy.
   Google Scholar
• OpenAI. 2018. AI and compute. OpenAI. Accessed December 14, 2019. https://openai.com/blog/ai-and-compute/.
   Google Scholar
• Pal, R. 2017. Chapter 6 - Overview of predictive modeling based on genomic characterizations. In Predictive modeling of drug sensitivity, ed. R. Pal, 121–48. London: Academic Press.
   Google Scholar
• Pearre, N. S., and L. G. Swan. 2018. Statistical approach for improved wind speed forecasting for wind power production. Sustainable Energy Technologies and Assessments 27:180–91. doi:10.1016/j.seta.2018.04.010.
   Web of Science ®Google Scholar
• Pei, S., H. Qin, Z. Zhang, L. Yao, Y. Wang, C. Wang, Y. Liu, Z. Jiang, J. Zhou, T. Yi, et al. 2019. Wind speed prediction method based on empirical wavelet transform and new cell update long short-term memory network. Energy Conversion and Management 196:779–92. doi:10.1016/j.enconman.2019.06.041.
   Web of Science ®Google Scholar
• Rivas, M. D. G., and J. Gonzalo. 2019. Trends in distributional characteristics: Existence of global warming. Journal of Econometrics 214 (1):153–74.
   Web of Science ®Google Scholar
• Shi, Q., M. Abdel-Aty, and J. Lee. 2016. A Bayesian ridge regression analysis of congestion’s impact on urban expressway safety. Accident; Analysis and Prevention 88:124–37. doi:10.1016/j.aap.2015.12.001.
   PubMed Web of Science ®Google Scholar
• Shmueli, G. 2010. To explain or to predict? Statistical Science 25 (3):289–310. doi:10.1214/10-STS330.
   Web of Science ®Google Scholar
• Shobha, G., and S. Rangaswamy. 2018. Chapter 8 - Machine learning. In Handbook of statistics, eds. Venkat N. Gudivada and C. R. Rao, vol. 38, 197–228. Amsterdam: ScienceDirect. doi:10.1016/bs.host.2018.07.004.
   Google Scholar
• Smola, A. J., and B. Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14 (3):199–222. doi:10.1023/B:STCO.0000035301.49549.88.
   Web of Science ®Google Scholar
• Stulp, F., and O. Sigaud. 2015. Many regression algorithms, one unified model: A review. Neural Networks: The Official Journal of the International Neural Network Society 69:60–79. doi:10.1016/j.neunet.2015.05.005.
   PubMed Web of Science ®Google Scholar
• Sun, Q., W.-X. Zhou, and J. Fan. 2020. Adaptive Huber regression. Journal of the American Statistical Association 115 (529):254–24. doi:10.1080/01621459.2018.1543124.
   PubMed Web of Science ®Google Scholar
• Sutton, C. D. 2005. 11 - Classification and regression trees, bagging, and boosting. In Handbook of statistics, ed. C. R. Rao, E. J. Wegman, and J. L. Solka, Vol. 24, 303–29. Amsterdam: Elsevier.
   Google Scholar
• Tintner, G. 1975. Multicollinearity. Applied Mathematics and Computation 1 (3):233–42. doi:10.1016/0096-3003(75)90035-1.
   Google Scholar
• U.S. EIA. 2020. How much of U.S. carbon dioxide emissions are associated with electricity generation? Accessed October 30, 2019. https://www.eia.gov/tools/faqs/faq.php?id=77&t=11.
   Google Scholar
• Uyanık, G. K., and N. Güler. 2013. A study on multiple linear regression analysis. Procedia - Social and Behavioral Sciences 106:234–40. doi:10.1016/j.sbspro.2013.12.027.
   Google Scholar
• Varmuza, K. 2003. Boosting applied to classification of mass spectral data. Journal of Data Science 1:391–404.
   Google Scholar
• Vu, D. H., K. M. Muttaqi, and A. P. Agalgaonkar. 2015. A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables. Applied Energy 140:385–94. doi:10.1016/j.apenergy.2014.12.011.
   Web of Science ®Google Scholar
• Watson, S., A. Moro, V. Reis, C. Baniotopoulos, S. Barth, G. Bartoli, F. Bauer, E. Boelman, D. Bosse, A. Cherubini, et al. 2019. Future emerging technologies in the wind power sector: A European perspective. Renewable and Sustainable Energy Reviews 113:109270. doi:10.1016/j.rser.2019.109270.
   Web of Science ®Google Scholar
• Yen, M. H., D. W. Liu, Y. C. Hsin, C. E. Lin, and C. C. Chen. 2019. Application of the deep learning for the prediction of rainfall in Southern Taiwan. Scientific Reports 9 (1):12774. doi:10.1038/s41598-019-49242-6.
   PubMedGoogle Scholar
• Zhao, Q., Q. Mao, Z. Zhao, T. Dou, Z. Wang, X. Cui, Y. Liu, and X. Fan. 2018. Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models. BMC Genomics 19 (1). doi:10.1186/s12864-018-5227-3.
   Google Scholar
• Zheng, Z. W., Y. Y. Chen, M. M. Huo, and B. Zhao. 2011. An overview: The development of prediction technology of wind and photovoltaic power generation. Energy Procedia 12:601–8. doi:10.1016/j.egypro.2011.10.081.
   Google Scholar