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.read more
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.
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Multistage Wind-Electric Power Forecast by Using a Combination of Advanced Statistical Methods
Serkan Buhan,Isik Cadirci +1 more
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.
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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.
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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.
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