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Energies 2017, 10(9), 1422; https://doi.org/10.3390/en10091422

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

1
School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China
2
Faculty of Engineering and Information Technology, University of Technology, Sydney, 20000, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Francisco Martínez-Álvarez
Received: 25 August 2017 / Revised: 12 September 2017 / Accepted: 13 September 2017 / Published: 16 September 2017
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
View Full-Text   |   Download PDF [2633 KB, uploaded 18 September 2017]   |  

Abstract

Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the ‘decomposition and ensemble’ strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules—data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases. View Full-Text
Keywords: wind speed forecasting; hybrid forecasting system; data pre-processing; fuzzy time series; comprehensive evaluation wind speed forecasting; hybrid forecasting system; data pre-processing; fuzzy time series; comprehensive evaluation
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Yang, H.; Jiang, Z.; Lu, H. A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series. Energies 2017, 10, 1422.

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