A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting
1
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
2
Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Academic Editors: José C. Riquelme, Alicia Troncoso and Francisco Martínez Álvarez
Energies 2016, 9(10), 757; https://doi.org/10.3390/en9100757
Received: 12 July 2016 / Revised: 14 August 2016 / Accepted: 18 August 2016 / Published: 22 September 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.
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Keywords:
hybrid method; short-term wind speed series forecasting; forecasting accuracy; neural network; artificial intelligence; optimization algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Gao, Y.; Qu, C.; Zhang, K. A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting. Energies 2016, 9, 757. https://doi.org/10.3390/en9100757
AMA Style
Gao Y, Qu C, Zhang K. A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting. Energies. 2016; 9(10):757. https://doi.org/10.3390/en9100757
Chicago/Turabian StyleGao, Yuyang; Qu, Chao; Zhang, Kequan. 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting" Energies 9, no. 10: 757. https://doi.org/10.3390/en9100757
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