A novel hybrid model for short-term wind power forecasting |
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Affiliation: | 1. School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China;2. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;1. School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China;2. School of Software, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia;1. School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China;2. Gansu Meteorological Service Centre, Lanzhou 730020, China |
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Abstract: | Wind energy prediction has a significant effect on the planning, economic operation and security maintenance of the wind power system. However, due to the high volatility and intermittency, it is difficult to model and predict wind power series through traditional forecasting approaches. To enhance prediction accuracy, this study developed a hybrid model that incorporates the following stages. First, an improved complete ensemble empirical mode decomposition with adaptive noise technology was applied to decompose the wind energy series for eliminating noise and extracting the main features of original data. Next, to achieve high accurate and stable forecasts, an improved wavelet neural network optimized by optimization methods was built and used to implement wind energy prediction. Finally, hypothesis testing, stability test and four case studies including eighteen comparison models were utilized to test the abilities of prediction models. The experimental results show that the average values of the mean absolute percent errors of the proposed hybrid model are 5.0116% (one-step ahead), 7.7877% (two-step ahead) and 10.6968% (three-step ahead), which are much lower than comparison models. |
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Keywords: | Hybrid forecasting model Wavelet neural network Multi-objective Optimization Algorithm Wind power forecasting |
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