Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting

Novel forecasting model based on improved wavelet transform, informative feature selection, and... Wind speed/power prediction plays an important role in large-scale wind power penetration because of the wind volatility and uncertainty. In this paper, an accurate forecast model is presented based on improved wavelet transform, informative feature selection and hybrid forecast engine. The proposed forecasting engine is based on support vector machine which is an appropriate prediction forecast engine due to its ability to discover natural structures of wind speed/power variation. The mentioned forecast engine is equipped with an intelligent algorithm and enhances its prediction accuracy. For this purpose, we applied a new version of enhanced particle swarm optimization in this work as the optimization algorithm. Effectiveness of the proposed forecast model is extensively evaluated by real-world electricity market through comparison with well-known forecasting methods. Obtained numerical results and analysis demonstrate the validity and superiority of the proposed method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Ambient Intelligence and Humanized Computing Springer Journals

Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting

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Abstract

Wind speed/power prediction plays an important role in large-scale wind power penetration because of the wind volatility and uncertainty. In this paper, an accurate forecast model is presented based on improved wavelet transform, informative feature selection and hybrid forecast engine. The proposed forecasting engine is based on support vector machine which is an appropriate prediction forecast engine due to its ability to discover natural structures of wind speed/power variation. The mentioned forecast engine is equipped with an intelligent algorithm and enhances its prediction accuracy. For this purpose, we applied a new version of enhanced particle swarm optimization in this work as the optimization algorithm. Effectiveness of the proposed forecast model is extensively evaluated by real-world electricity market through comparison with well-known forecasting methods. Obtained numerical results and analysis demonstrate the validity and superiority of the proposed method.

Journal

Journal of Ambient Intelligence and Humanized Computing – Springer Journals

Published: Jun 1, 2018

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