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. 2023 May 19;23(10):4905.
doi: 10.3390/s23104905.

Wind Speed Prediction Based on Error Compensation

Xuguo Jiao  1   2 , Daoyuan Zhang  1 , Xin Wang  3 , Yanbing Tian  1 , Wenfeng Liu  4 , Liping Xin  1
Affiliations

Wind Speed Prediction Based on Error Compensation

Xuguo Jiao et al. Sensors (Basel). .

Abstract

Wind speed prediction is very important in the field of wind power generation technology. It is helpful for increasing the quantity and quality of generated wind power from wind farms. By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation. First, to explore the balance between the computation cost and the sufficiency of the input features, the characteristics of ARMA are employed to determine the number of historical wind speeds for the prediction model. According to the selected number of input features, the original data are divided into multiple groups that can be used to train the SVR-based wind speed prediction model. Furthermore, in order to compensate for the time lag introduced by the frequent and sharp fluctuations in natural wind speed, a novel Extreme Learning Machine (ELM)-based error correction technique is developed to decrease the deviations between the predicted wind speed and its real values. By this means, more accurate wind speed prediction results can be obtained. Finally, verification studies are conducted by using real data collected from actual wind farms. Comparison results demonstrate that the proposed method can achieve better prediction results than traditional approaches.

Keywords: Autoregressive Moving Average (ARMA); Extreme Learning Machine (ELM); Support Vector Regression (SVR); error compensation; time series prediction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The Flow Chart of The Experiment.
Figure 2
Figure 2
The Raw Data.
Figure 3
Figure 3
The Raw Data After Difference.
Figure 4
Figure 4
The ACF and PACF of Raw Data (The blue lines are the upper and lower confidence bounds).
Figure 5
Figure 5
Wind Speed Prediction of SVR.
Figure 6
Figure 6
The Prediction Error.
Figure 7
Figure 7
The Result of Raw Data Prediction.
Figure 8
Figure 8
The Raw Error Data.
Figure 9
Figure 9
The Differential Error Data.
Figure 10
Figure 10
The ACF and PACF of Error Series (The blue lines are the upper and lower confidence bounds).
Figure 11
Figure 11
Error Prediction of ELM.
Figure 12
Figure 12
The Final Prediction Result.
Figure 13
Figure 13
The Error Comparison.
Figure 14
Figure 14
Error Prediction of SVR.
Figure 15
Figure 15
The Final Prediction Result of SVR.
Figure 16
Figure 16
Error Prediction of BPNN.
Figure 17
Figure 17
The Final Prediction Result of BPNN.
Figure 18
Figure 18
The Comparison of Error Prediction Results of ELM, SVR and BPNN.
Figure 19
Figure 19
The Final Prediction Result of ELM, SVR and BPNN.
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