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

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

Content Provider	MDPI
Author	Yang, Hufang Jiang, Zaiping Lu, Haiyan
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.
File Size	2696192
File Format	PDF
e-ISSN	19961073
DOI	10.3390/en10091422
Journal	Energies
Issue Number	9
Volume Number	10
Language	English
Publisher Date	2017-09-16
Access Restriction	Open
Subject Keyword	wind speed forecasting hybrid forecasting system data pre-processing fuzzy time series comprehensive evaluation
Content Type	Text
Resource Type	Article

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