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Energies 2017, 10(12), 2001; https://doi.org/10.3390/en10122001

Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
Department of Information Management, Oriental Institute of Technology, New Taipei 220, Taiwan
*
Author to whom correspondence should be addressed.
Received: 13 November 2017 / Revised: 24 November 2017 / Accepted: 28 November 2017 / Published: 1 December 2017
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Abstract

As a kind of clean and renewable energy, wind power is winning more and more attention across the world. Regarding wind power utilization, safety is a core concern and such concern has led to many studies on predicting wind speed. To obtain a more accurate prediction of the wind speed, this paper adopts a new hybrid forecasting model, combing empirical mode decomposition (EMD) and the general regression neural network (GRNN) optimized by the fruit fly optimization algorithm (FOA). In this new model, the original wind speed series are first decomposed into a collection of intrinsic mode functions (IMFs) and a residue. Next, the inherent relationship (partial correlation) of the datasets is analyzed, and the results are then used to select the input for the forecasting model. Finally, the GRNN with the FOA to optimize the smoothing factor is used to predict each sub-series. The mean absolute percentage error of the forecasting results in two cases are respectively 8.95% and 9.87%, suggesting that the hybrid approach outperforms the compared models, which provides guidance for future wind speed forecasting. View Full-Text
Keywords: wind speed forecasting; empirical mode decomposition; general regression neural network; fruit fly optimization algorithm wind speed forecasting; empirical mode decomposition; general regression neural network; fruit fly optimization algorithm
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Niu, D.; Liang, Y.; Hong, W.-C. Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA. Energies 2017, 10, 2001.

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