scholarly journals Chaotic time series prediction model of wind power based on ensemble empirical mode decomposition-approximate entropy and reservoir

2013 ◽  
Vol 62 (5) ◽  
pp. 050505
Author(s):  
Zhang Xue-Qing ◽  
Liang Jun
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Prediction ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Chaotic Time Series ◽  
Mode Decomposition ◽  
Chaotic Time Series Prediction

Toward a Digital Twin: Time Series Prediction Based on a Hybrid Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Weifei Hu ◽  
Yihan He ◽  
Zhenyu Liu ◽  
Jianrong Tan ◽  
Ming Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Prediction ◽  
Ensemble Empirical Mode Decomposition ◽  
Bayesian Optimization ◽  
Series Data ◽  
Digital Twin ◽  
Mode Decomposition ◽  
Highly Nonlinear

Abstract Precise time series prediction serves as an important role in constructing a digital twin (DT). The various internal and external interferences result in highly nonlinear and stochastic time series. Although artificial neural networks (ANNs) are often used to forecast time series because of their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on an ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components (each component is a single-frequency and stationary signal) and a residual signal. The decomposed signals are used to train the neural networks, in which the hyperparameters are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed EEMD-BO-LSTM neural networks, this paper conducts two case studies (the wind speed prediction and the wave height prediction) and implements a comprehensive comparison between the proposed method and other approaches including the persistence model, autoregressive integrated moving average (ARIMA) model, LSTM neural networks, BO-LSTM neural networks, and EEMD-LSTM neural networks. The results show an improved prediction accuracy using the proposed method by multiple accuracy metrics.

A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin

2020 ◽  
Author(s):  
Weifei Hu ◽  
Yihan He ◽  
Zhenyu Liu ◽  
Jianrong Tan ◽  
Ming Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wind Speed ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Digital Twin ◽  
Mode Decomposition

Abstract Precise time series prediction serves as an important role in constructing a Digital Twin (DT). The various internal and external interferences result in highly non-linear and stochastic time series data sampled from real situations. Although artificial Neural Networks (ANNs) are often used to forecast time series for their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components, each of which is composed of single-frequency and stationary signal, and a residual signal. The decomposed signals are used to train the BO-LSTM neural networks, in which the hyper-parameters of the LSTM neural networks are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed hybrid method (EEMD-BO-LSTM), this paper conducts a case study of wind speed time series prediction and has a comprehensive comparison between the proposed method and other approaches including the persistence model, ARIMA, LSTM neural networks, B0-LSTM neural networks, and EEMD-LSTM neural networks. Results show an improved prediction accuracy using the EEMD-BO-LSTM method by multiple accuracy metrics.

scholarly journals Application of Ensemble Empirical Mode Decomposition based Support Vector Regression Model for Wind Power Prediction

2020 ◽  
Vol 22 (1) ◽  
pp. 11-16
Author(s):  
Irene Karijadi ◽  
Ig. Jaka Mulyana
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
System Stability ◽  
Support Vector ◽  
Power Prediction ◽  
Mode Decomposition ◽  
Wind Power Prediction

Improving accuracy of wind power prediction is important to maintain power system stability. However, wind power prediction is difficult due to randomness and high volatility characteristics. This study applies a hybrid algorithm that combines ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to develop a prediction model for wind power prediction. Ensemble empirical mode decomposition is employed to decompose original data into several Intrinsic Mode Functions (IMF). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of wind power Numerical testing demonstrated that the proposed method can accurately predict the wind power in Belgian.

scholarly journals The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1666 ◽  
Author(s):  
Neeraj Bokde ◽  
Andrés Feijóo ◽  
Nadhir Al-Ansari ◽  
Siyu Tao ◽  
Zaher Mundher Yaseen
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Arima Model ◽  
Ensemble Empirical Mode Decomposition ◽  
Power Modeling ◽  
Short Term ◽  
Mode Decomposition ◽  
Eemd Method

In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.

scholarly journals Sunspots Time-Series Prediction Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Guohui Li ◽  
Siliang Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Empirical Mode Decomposition ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Ensemble Empirical Mode Decomposition ◽  
Wavelet Neural Network ◽  
Hybrid Prediction ◽  
Mode Decomposition

The sunspot numbers are the major target which describes the solar activity level. Long-term prediction of sunspot activity is of great importance for aerospace, communication, disaster prevention, and so on. To improve the prediction accuracy of sunspot time series, the prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and wavelet neural network (WNN) is proposed. First, the sunspot time series are decomposed by CEEMD to obtain a set of intrinsic modal functions (IMFs). Then, the IMFs and residuals are reconstructed to obtain the training samples and the prediction samples, and these samples are trained and predicted by WNN. Finally, the reconstructed IMFs and residuals are the final prediction results. Five kinds of prediction models are compared, which are BP neural network prediction model, WNN prediction model, empirical mode decomposition and WNN hybrid prediction model, ensemble empirical mode decomposition and WNN hybrid prediction model, and the proposed method in this paper. The same sunspot time series are predicted with five kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

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