Journal Article10.1016/J.RSER.2013.12.054
Current status and future advances for wind speed and power forecasting
Jaesung Jung,Robert Broadwater +1 more
668
TL;DR: An overview of existing research on wind speed and power forecasting can be found in this article, where state-of-the-art approaches for wind power and wind speed forecasting are discussed.
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Abstract: This paper presents an overview of existing research on wind speed and power forecasting. It first discusses state-of-the-art wind speed and power forecasting approaches. Then, forecasting accuracy is presented based on variable factors. Finally, potential techniques to improve the accuracy of forecasting models are reviewed. A full survey on all existing models is not presented, but attempts to highlight the most promising body of knowledge concerning wind speed and power forecasting.
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TL;DR: A bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting and further direction for additional research and application is proposed.
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Current methods and advances in forecasting of wind power generation
TL;DR: A review of the current methods and advances in wind power forecasting and prediction can be found in this article, where numerical wind power prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed.
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A review of wind power and wind speed forecasting methods with different time horizons
Saurabh S. Soman,Hamidreza Zareipour,Om P. Malik,Paras Mandal +3 more
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TL;DR: In this article, the main challenges and problems associated with wind power prediction are discussed, and an overview of comparative analysis of various available forecasting techniques is discussed as well as a major challenges and major challenges.
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ARMA based approaches for forecasting the tuple of wind speed and direction
Ergin Erdem,Jing Shi +1 more
TL;DR: In this paper, four approaches based on autoregressive moving average (ARMA) method are employed for short-term forecasting of wind speed and direction are employed to forecast wind turbine operation and efficient energy harvesting.
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Statistical Analysis of Wind Power Forecast Error
TL;DR: In this article, an indirect algorithm based on the Beta pdf is proposed to obtain a more appropriate probability density function (pdf) of the wind power forecast error, which can be categorized as fat-tailed.
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