Short-term wind speed forecasting method based on wavelet packet decomposition and improved Elman neural network

Abstract Adequate power provision to the customer and wind energy penetration into the electrical grid is necessitated for accurate wind speed forecasting in the short-term horizon to realize the scheduling, unit commitment, and control. According to the various meteorological parameters, the wind speed and energy production from wind energy are affected. Therefore, the author performs the multi-inputs associated Meta learning-based Elman Neural Network (MENN) forecasting model to overcome the uncertainty and generalization problem. The proposed forecasting approach applicability evaluated with real-time data concerning wind speed forecasting on a short-term time scale. Performance analysis reveals that the meta learning-based Elman neural network is robust and conscious than the existing methods, with a least mean square error of 0.0011.

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Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network

Energy Conversion and Management ◽

10.1016/j.enconman.2017.11.053 ◽

2018 ◽

Vol 156 ◽

pp. 498-514 ◽

Cited By ~ 134

Author(s):

Hui Liu ◽

Xi-wei Mi ◽

Yan-fei Li

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Learning Strategy ◽

Short Term ◽

Elman Neural Network ◽

Term Memory ◽

Wind Speed Forecasting ◽

Forecasting Method ◽

Empirical Wavelet Transform ◽

Long Short Term Memory

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A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets

Energy Conversion and Management ◽

10.1016/j.enconman.2020.112824 ◽

2020 ◽

Vol 213 ◽

pp. 112824 ◽

Cited By ~ 13

Author(s):

Gholamreza Memarzadeh ◽

Farshid Keynia

Keyword(s):

Neural Network ◽

Wind Speed ◽

Short Term ◽

Wind Speed Forecasting ◽

Optimal Input ◽

Forecasting Method

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Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM

Sustainability ◽

10.3390/su10103693 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3693 ◽

Cited By ~ 13

Author(s):

Yuansheng Huang ◽

Shijian Liu ◽

Lei Yang

Keyword(s):

Neural Network ◽

Wind Speed ◽

Short Term ◽

Wind Speed Forecasting ◽

Wind Speed Prediction ◽

Forecasting Methods ◽

Combination Forecasting ◽

Forecasting Method ◽

Speed Prediction ◽

Eemd Method

Short-term wind speed prediction is of cardinal significance for maximization of wind power utilization. However, the strong intermittency and volatility of wind speed pose a challenge to the wind speed prediction model. To improve the accuracy of wind speed prediction, a novel model using the ensemble empirical mode decomposition (EEMD) method and the combination forecasting method for Gaussian process regression (GPR) and the long short-term memory (LSTM) neural network based on the variance-covariance method is proposed. In the proposed model, the EEMD method is employed to decompose the original data of wind speed series into several intrinsic mode functions (IMFs). Then, the LSTM neural network and the GPR method are utilized to predict the IMFs, respectively. Lastly, based on the IMFs’ prediction results with the two forecasting methods, the variance-covariance method can determine the weight of the two forecasting methods and offer a combination forecasting result. The experimental results from two forecasting cases in Zhangjiakou, China, indicate that the proposed approach outperforms other compared wind speed forecasting methods.

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