A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm

2018 ◽  
Vol 163 ◽  
pp. 134-150 ◽  
Author(s):  
Jianzhou Wang ◽  
Wendong Yang ◽  
Pei Du ◽  
Tong Niu
Keyword(s):  
Wind Speed ◽  
Multi Objective ◽  
Hybrid Forecasting ◽  
Forecasting System ◽  
Sine Cosine Algorithm

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.

scholarly journals Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3588 ◽  
Author(s):  
Wei ◽  
Wang ◽  
Ni ◽  
Tang
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Short Term Memory ◽  
Electrical Power ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Grey Wolf Optimizer ◽  
Power Load ◽  
Hybrid Forecasting ◽  
Forecasting System

In recent years, although deep learning algorithms have been widely applied to various fields, ranging from translation to time series forecasting, researchers paid limited attention to modelling parameter optimization and the combination of the fuzzy time series. In this paper, a novel hybrid forecasting system, named CFML (complementary ensemble empirical mode decomposition (CEEMD)-fuzzy time series (FTS)-multi-objective grey wolf optimizer (MOGWO)-long short-term memory (LSTM)), is proposed and tested. This model is based on the LSTM model with parameters optimized by MOGWO, before which a fuzzy time series method involving the LEM2 (learning from examples module version two) algorithm is adopted to generate the final input data of the optimized LSTM model. In addition, the CEEMD algorithm is also used to de-noise and decompose the raw data. The CFML model successfully overcomes the nonstationary and irregular features of wind speed data and electrical power load series. Several experimental results covering four wind speed datasets and two electrical power load datasets indicate that our hybrid forecasting system achieves average improvements of 49% and 70% in wind speed and electrical power load, respectively, under the metric MAPE (mean absolute percentage error).

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