Journal
APPLIED SOFT COMPUTING
Volume 80, Issue -, Pages 93-106Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2019.03.035
Keywords
Hybrid forecasting model; Wavelet neural network; Multi-objective Optimization Algorithm; Wind power forecasting
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Funding
- National Natural Science Foundation of China [71671029]
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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. (C) 2019 Elsevier B.V. All rights reserved.
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