Next Article in Journal
Profiles of Violence and Alcohol and Tobacco Use in Relation to Impulsivity: Sustainable Consumption in Adolescents
Previous Article in Journal
Land-Use/Land-Cover Change from Socio-Economic Drivers and Their Impact on Biodiversity in Nan Province, Thailand
Open AccessArticle

Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(3), 650; https://doi.org/10.3390/su11030650
Received: 19 December 2018 / Revised: 22 January 2019 / Accepted: 23 January 2019 / Published: 26 January 2019
The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models. View Full-Text
Keywords: short-term wind power prediction; extreme-point symmetric mode decomposition; sample entropy theory; combined model short-term wind power prediction; extreme-point symmetric mode decomposition; sample entropy theory; combined model
Show Figures

Figure 1

MDPI and ACS Style

Zhou, J.; Xu, X.; Huo, X.; Li, Y. Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks. Sustainability 2019, 11, 650.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop