Journal ArticleDOI
Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system
TLDR
Comparison of the statistical indices for the predicted and actual data indicate that the SVR-RBF model outperforms the MLFFNN and ANFIS-PSO models to predict the output power of the wind turbine.About:
This article is published in Sustainable Energy Technologies and Assessments.The article was published on 2018-02-01. It has received 209 citations till now. The article focuses on the topics: Wind direction & Wind speed.read more
Citations
More filters
Journal ArticleDOI
COVID-19 outbreak prediction with machine learning
Sina Ardabili,Amir Mosavi,Pedram Ghamisi,Filip Ferdinand,Annamária R. Várkonyi-Kóczy,Uwe Reuter,Timon Rabczuk,Peter M. Atkinson +7 more
TL;DR: A comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models suggests machine learning as an effective tool to model the outbreak.
Journal ArticleDOI
Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil
TL;DR: The results demonstrated that the GMDH model for all time intervals can successfully predict the time-series wind speed data with a high accuracy and the combination of ANfIS models with PSO and GA algorithms can increase the prediction accuracy of the ANFIS model forall time intervals.
Journal ArticleDOI
Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression
TL;DR: Experimental results show that SWLSTM-GPR can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction problems.
Journal ArticleDOI
Short-term wind speed prediction model based on GA-ANN improved by VMD
TL;DR: Variational mode decomposition (VMD) algorithm can use VMD to decompose the wind speed signal to obtain different scale fluctuations or trends, so as to fully exploit the potential information of wind speed, and obtain more accurate prediction results.
Journal ArticleDOI
Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression
TL;DR: In the proposed SSA-CNNGRU-SVR model, the CNNGRU can have good prediction performance in the main trend component forecasting, the SVR can haveGood predictionperformance in the detail components forecasting, and the proposed model can obtain good results in wind speed forecasting.
References
More filters
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Thermal design and optimization
TL;DR: In this article, the authors present an overview of thermal system design using thermodynamics, modeling, and design analysis, including exergy analysis, energy analysis, and economic analysis.
Journal ArticleDOI
On comparing three artificial neural networks for wind speed forecasting
TL;DR: A comprehensive comparison study on the application of different artificial neural networks in 1-h-ahead wind speed forecasting shows that even for the same wind dataset, no single neural network model outperforms others universally in terms of all evaluation metrics.
Journal ArticleDOI
Wind speed forecasting for wind farms: A method based on support vector regression
TL;DR: Results show that, forecasts made with the proposed hybrid methodology are more accurate for medium (5–23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models.
Related Papers (5)
ARMA based approaches for forecasting the tuple of wind speed and direction
Ergin Erdem,Jing Shi +1 more