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Comparison of several measure-correlate-predict models using support vector regression techniques to estimate wind power densities. A case study
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Regularized Kriging: The Support Vectors Method Applied to Kriging
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Introduction to Machine Learning
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A Chronicle of Permutation Statistical Methods
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Support Vector Machines (SVMs)
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Permutation, Parametric and Bootstrap Tests of Hypotheses
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First-Principles Calculation of the Air–Water Second Virial Coefficient
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A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks
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Comparative Analysis of Different Air Density Equations
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Putnam's Power From the Wind
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The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria
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Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kW h produced by a wind turbine at a candidate site: A case study in the Canary Islands
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Comparison of feature selection methods using ANNs in MCP-wind speed methods. A case study
inferredcites →https://doi.org/10.1016/j.enconman.2006.06.019openalex-snapshot
A continuous bivariate model for wind power density and wind turbine energy output estimations
inferredcites →https://doi.org/10.1016/j.enconman.2010.09.008openalex-snapshot
Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site
inferredcites →https://doi.org/10.1016/j.enconman.2014.02.017openalex-snapshot
Wind speed estimation using multilayer perceptron
inferredcites →https://doi.org/10.1016/j.enconman.2014.07.057openalex-snapshot
A hybrid measure-correlate-predict method for long-term wind condition assessment
inferredcites →https://doi.org/10.1016/j.enconman.2015.11.015openalex-snapshot
Assessing different parameters estimation methods of Weibull distribution to compute wind power density
inferredcites →https://doi.org/10.1016/j.energy.2011.02.008openalex-snapshot
A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site
inferredcites →https://doi.org/10.1016/j.energy.2013.10.007openalex-snapshot
A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting
inferredcites →https://doi.org/10.1016/j.energy.2015.01.115openalex-snapshot
Optimal design of power system stabilizer for power systems including doubly fed induction generator wind turbines
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Evaluating wind power density models and their statistical properties
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Psychrometric charts for water vapour in natural gas
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Effect of direction on wind speed estimation in complex terrain using neural networks
inferredcites →https://doi.org/10.1016/j.renene.2011.07.007openalex-snapshot
Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography
inferredcites →https://doi.org/10.1016/j.renene.2013.08.033openalex-snapshot
Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy
inferredcites →https://doi.org/10.1016/j.renene.2014.02.020openalex-snapshot
Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP
inferredcites →https://doi.org/10.1016/j.rser.2010.11.036openalex-snapshot
Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study
inferredcites →https://doi.org/10.1016/j.rser.2013.07.004openalex-snapshot
A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site
inferredcites →https://doi.org/10.1017/cbo9780511801389openalex-snapshot
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
inferredcites →https://doi.org/10.1021/je60064a005openalex-snapshot
Density, thermal expansivity, and compressibility of liquid water from 0.deg. to 150.deg.. Correlations and tables for atmospheric pressure and saturation reviewed and expressed on 1968 temperature scale
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A Note on the Universal Approximation Capability of Support Vector Machines
inferredcites →https://doi.org/10.1080/00224065.1998.11979883openalex-snapshot
Applied Regression Analysis
inferredcites →https://doi.org/10.1080/00401706.1987.10488292openalex-snapshot
Goodness-of-Fit Techniques
inferredcites →https://doi.org/10.1080/00401706.1999.10485680openalex-snapshot
Applied Regression Analysis
inferredcites →https://doi.org/10.1080/10789669.2002.10391444openalex-snapshot
Formulation of High-Temperature Properties for Moist Air
inferredcites →https://doi.org/10.1080/10789669.2005.10391131openalex-snapshot
Psychrometric Charts and Property Formulations of Supersaturated Air
inferredcites →https://doi.org/10.1080/10789669.2009.10390874openalex-snapshot
Thermodynamic Properties of Real Moist Air, Dry Air, Steam, Water, and Ice (RP-1485)
inferredcites →https://doi.org/10.1088/0026-1394/29/1/008openalex-snapshot
Equation for the Determination of the Density of Moist Air (1981/91)
inferredcites →https://doi.org/10.1108/k.2001.30.1.103.6openalex-snapshot
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
inferredcites →https://doi.org/10.1109/tste.2014.2300150openalex-snapshot
Long-Term Wind Speed Forecasting and General Pattern Recognition Using Neural Networks
inferredcites →https://doi.org/10.1111/j.2517-6161.1995.tb02031.xopenalex-snapshot
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
inferredcites →https://doi.org/10.1142/9789811201967_0001openalex-snapshot
Introduction to Machine Learning
inferredcites →https://doi.org/10.1162/neco.1995.7.2.219openalex-snapshot
Regularization Theory and Neural Networks Architectures
inferredcites →https://doi.org/10.1198/tech.2005.s333openalex-snapshot
Permutation, Parametric, and Bootstrap Tests of Hypotheses
inferredcites →https://doi.org/10.1201/b14297?locatt=mode:legacyopenalex-snapshot
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions
inferredcites →https://doi.org/10.1214/009053607000000677openalex-snapshot
Kernel methods in machine learning
inferredcites →https://doi.org/10.1260/0309-524x.34.2.157openalex-snapshot
Intelligent Tools for Wind Resource Correlation
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A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index
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A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index
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kernlab - An S4 Package for Kernel Methods in R
inferredcites →https://doi.org/10.18637/jss.v028.i05openalex-snapshot
Building Predictive Models in R Using the caret Package
inferredcites →https://doi.org/10.2495/978-1-84564-205-1/02openalex-snapshot
Wind resource and site assessment
inferredcites →https://doi.org/10.3390/en7085251openalex-snapshot
Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)
inferredcites →https://doi.org/10.5860/choice.48-3904openalex-snapshot
Wind power generation and wind turbine design
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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