2019
DOI: 10.1177/0309524x19849867
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Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation

Abstract: In order to improve the forecasting accuracy of short-term wind speed, a forecasting method based on autoregressive moving average with echo state network compensation is proposed in this article. First, the linear and nonlinear characteristics of short-term wind speed can be determined by Brock–Dechert–Scheinkman statistics method. Then, autoregressive moving average model is used for modeling and to forecast the linear component of short-term wind speed. The linear component of short-term wind speed sequence… Show more

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Cited by 31 publications
(9 citation statements)
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References 42 publications
(46 reference statements)
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“…The ARIMA model performs better with linear time series and stationary data than with nonlinear and non-stationary data [15]. A forecasting method based on the autoregressive moving average has been proposed to improve the accuracy of short-term wind speed prediction [16]. Recurrent neural networks (RNNs) are one of the most powerful models for processing sequential data such as time series.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The ARIMA model performs better with linear time series and stationary data than with nonlinear and non-stationary data [15]. A forecasting method based on the autoregressive moving average has been proposed to improve the accuracy of short-term wind speed prediction [16]. Recurrent neural networks (RNNs) are one of the most powerful models for processing sequential data such as time series.…”
Section: Literature Reviewmentioning
confidence: 99%
“…This study focused on 1 hour ahead wind speed forecasts and the results are satisfactory for a statistical method. Besides, a more recent study that couples ARMA and echo state network compensation for linear and nonlinear components of the short-term wind speed time series is presented by Tian et al (2020c), and the proposed method showed better forecasting accuracy than the other state-of-the-art methods proposed in this study. Another study which is quite successful and uses a couple of different statistical methods of ensemble empirical decomposition (EEMD), permutation entropy, and regularized extreme learning machine Tian et al (2020a) is also proposed by Tian and, while the author showed that this study has a good application potential, the proposed method improves the prediction accuracy.…”
Section: Introductionmentioning
confidence: 79%
“…The first type of combination prediction model is linear and non-linear combination. For example, autoregressive moving average compensates echo state network (Tian et al, 2020c). The second type is weighted combination model.…”
Section: Physical Prediction Modelmentioning
confidence: 99%