Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting

2017 ◽  
Vol 150 ◽  
pp. 90-107 ◽  
Author(s):  
Pei Du ◽  
Jianzhou Wang ◽  
Zhenhai Guo ◽  
Wendong Yang
Keyword(s):  
Wind Speed ◽  
Multi Objective Optimization ◽  
Wind Speed Forecasting ◽  
Multi Objective ◽  
Hybrid Forecasting ◽  
Forecasting System

scholarly journals A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network

2019 ◽  
Vol 11 (2) ◽  
pp. 526 ◽  
Author(s):  
Jianzhou Wang ◽  
Chunying Wu ◽  
Tong Niu
Keyword(s):  
Wind Speed ◽  
Power Systems ◽  
Data Preprocessing ◽  
Multi Objective Optimization ◽  
Forecasting Accuracy ◽  
Wind Speed Forecasting ◽  
Multi Objective ◽  
Forecasting System ◽  
The Mean ◽  
Wind Power Systems

Given the rapid development and wide application of wind energy, reliable and stable wind speed forecasting is of great significance in keeping the stability and security of wind power systems. However, accurate wind speed forecasting remains a great challenge due to its inherent randomness and intermittency. Most previous researches merely devote to improving the forecasting accuracy or stability while ignoring the equal significance of improving the two aspects in application. Therefore, this paper proposes a novel hybrid forecasting system containing the modules of a modified data preprocessing, multi-objective optimization, forecasting, and evaluation to achieve the wind speed forecasting with high precision and stability. The modified data preprocessing method can obtain a smoother input by decomposing and reconstructing the original wind speed series in the module of data preprocessing. Further, echo state network optimized by a multi-objective optimization algorithm is developed as a predictor in the forecasting module. Finally, eight datasets with different features are used to validate the performance of the proposed system using the evaluation module. The mean absolute percentage errors of the proposed system are 3.1490%, 3.0051%, 3.0618%, and 2.6180% in spring, summer, autumn, and winter, respectively. Moreover, the interval prediction is complemented to quantitatively characterize the uncertainty as developing intervals, and the mean average width is below 0.2 at the 95% confidence level. The results demonstrate the proposed forecasting system outperforms other comparative models considered from the forecasting accuracy and stability, which has great potential in the application of wind power systems.

Sign in / Sign up

Export Citation Format

Share Document