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Open AccessJournal ArticleDOI

Short term wind speed prediction using support vector machine model

TLDR
In this paper, a Support Vector Machine (SVM) model was used to predict wind speed in short-term using the values of other atmospheric variables, such as pressure, moisture content, humidity, rainfall etc.
Abstract
Wind speed prediction in short term is required to asses the effect of wind on different objects in action in free space, like rockets, navigating ships and planes, guided missiles satellites in launch etc. Forecasting also helps in usage of wind energy as an alternative source of energy in Electrical power generation plants. The wind speed depends on the values of other atmospheric variables, such as pressure, moisture content, humidity, rainfall etc. This paper reports a Support Vector Machine model for short term wind speed prediction. The model uses the values of these parameters, obtained from a nearest weather station, as input data. The trained model is validated using a part of data. The model is then used to predict the wind speed, using the same meteorological information.

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Citations
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Journal ArticleDOI

Application of support vector machine models for forecasting solar and wind energy resources: A review

TL;DR: In this article, a hybrid support vector machine (SVM) model was proposed to forecast both solar and wind energy resources for most of the locations in the United States, where the authors highlighted main problems, opportunities and future work in this research area.
Journal ArticleDOI

Multistage Wind-Electric Power Forecast by Using a Combination of Advanced Statistical Methods

TL;DR: It has been shown that the proposed multistage cascaded statistical model performs better than the reference models in terms of short-term forecast accuracy, especially for WPPs in complex terrains with a scattered wind regime.
Journal ArticleDOI

Current gust forecasting techniques, developments and challenges

TL;DR: Increases in the resolution of operational NWP models mean that phenomena traditionally posing a challenge for gust forecasting, such as convective cells, sting jets and mountain lee waves may now be at least partially represented in the model fields.
Proceedings ArticleDOI

Use of support vector machine for wind speed prediction

TL;DR: In this paper, the SVM is used for day ahead prediction of wind speed using historical data of wind speeds at site It is observed that the Mean Absolute Percentage Error (MAPE) is around 7% and correlation coefficient is close to 1 This justifies the ability of SVM for wind speed prediction task.
Journal Article

Statistical analysis and evaluation of Hurst coefficient for annual and monthly precipitation time series

TL;DR: In this paper, the authors focus on the long range dependence (LRD) property of time series and compare the results of different estimators of LRD for ten annual and monthly data series collected in Dobrudja region.
References
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Journal ArticleDOI

LIBSVM: A library for support vector machines

TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Book

Neural Networks: A Comprehensive Foundation

Simon Haykin
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI

A Tutorial on Support Vector Machines for Pattern Recognition

TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Journal ArticleDOI

Data mining and knowledge discovery: making sense out of data

TL;DR: Without a concerted effort to develop knowledge discovery techniques, organizations stand to forfeit much of the value from the data they currently collect and store.