Investigation of Data Size Variability in Wind Speed Prediction Using AI Algorithms
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Nian Zhang | Timothy Oladunni | Amir Shahirinia | M. A. Ehsan | Md. Amimul Ehsan | A. Shahirinia | N. Zhang | T. Oladunni
[1] D. H. Vu,et al. A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables , 2015 .
[2] Hui Liu,et al. Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network , 2018 .
[3] Lorenzo Fagiano,et al. Future emerging technologies in the wind power sector: A European perspective , 2019, Renewable and Sustainable Energy Reviews.
[4] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[5] Sunghwan Sohn,et al. Deep learning and alternative learning strategies for retrospective real-world clinical data , 2019, npj Digital Medicine.
[6] Michail Tsagris,et al. Multicollinearity. , 2021, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.
[7] R. Pal. Overview of predictive modeling based on genomic characterizations , 2017 .
[8] Tansu Filik,et al. Wind Speed Prediction Using Artificial Neural Networks Based on Multiple Local Measurements in Eskisehir , 2017 .
[9] Fatema Begum,et al. Advanced wind speed prediction using convective weather variables through machine learning application , 2019, Applied Computing and Geosciences.
[10] Chao Wang,et al. Wind speed prediction method based on Empirical Wavelet Transform and New Cell Update Long Short-Term Memory network , 2019, Energy Conversion and Management.
[11] Jiajuan Liang,et al. Characterization-based Q–Q plots for testing multinormality , 2004 .
[12] Nathaniel S. Pearre,et al. Statistical approach for improved wind speed forecasting for wind power production , 2018, Sustainable Energy Technologies and Assessments.
[13] Kai-Tai Fang,et al. Boosting Applied to Classification of Mass Spectral Data , 2021, Journal of Data Science.
[14] Qiang Sun,et al. Adaptive Huber Regression , 2017, Journal of the American Statistical Association.
[15] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[16] T. Cockerill,et al. Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data , 2018, Renewable Energy.
[17] Olivier Sigaud,et al. Many regression algorithms, one unified model: A review , 2015, Neural Networks.
[18] Chii-Chang Chen,et al. Application of the deep learning for the prediction of rainfall in Southern Taiwan , 2019, Scientific Reports.
[19] Ying Cao,et al. Advance and Prospects of AdaBoost Algorithm , 2013, ACTA AUTOMATICA SINICA.
[20] Yanfei Li,et al. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM , 2018 .
[21] Zengwei Zheng,et al. An Overview: the Development of Prediction Technology of Wind and Photovoltaic Power Generation , 2011 .
[22] Onur Avci,et al. 1D Convolutional Neural Networks and Applications: A Survey , 2019, Mechanical Systems and Signal Processing.
[23] Fernando A. Kuipers,et al. Optimal siting and sizing of wind farms , 2017 .
[24] Roman Khudorozhkov,et al. Not quite unreasonable effectiveness of machine learning algorithms , 2018, ArXiv.
[25] Brandon Bennett,et al. The Assessment of Machine Learning Model Performance for Predicting Alluvial Deposits Distribution , 2014, Complex Adaptive Systems.
[26] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[27] E. Newport,et al. Science Current Directions in Psychological Statistical Learning : from Acquiring Specific Items to Forming General Rules on Behalf Of: Association for Psychological Science , 2022 .
[28] Mohamed Abdel-Aty,et al. A Bayesian ridge regression analysis of congestion's impact on urban expressway safety. , 2016, Accident; analysis and prevention.
[29] Brian R. Huguenard,et al. Evaluating Aptness of a Regression Model , 2007 .
[30] A. Ghasemi,et al. Normality Tests for Statistical Analysis: A Guide for Non-Statisticians , 2012, International journal of endocrinology and metabolism.
[31] Yuanning Liu,et al. Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models , 2018, BMC Genomics.
[32] Fabrizio Argenti,et al. Mixed ℓ2 and ℓ1-norm regularization for adaptive detrending with ARMA modeling , 2018, J. Frankl. Inst..
[33] N. Güler,et al. A Study on Multiple Linear Regression Analysis , 2013 .
[34] Galit Shmueli,et al. To Explain or To Predict? , 2010, 1101.0891.
[35] Katerina M. Marcoulides,et al. Evaluation of Variance Inflation Factors in Regression Models Using Latent Variable Modeling Methods , 2019, Educational and psychological measurement.
[36] Danilo Bzdok,et al. Points of Significance: Statistics versus machine learning , 2018, Nature Methods.