Two-Stage Short-Term Wind Speed Prediction Based on LSTM-LSSVM-CFA

Zhang, Liming; Wang, Bo; Fang, Biwu; Ma, Hengrui; Zheng, Yang; Xu, Yeyan

doi:10.1109/ei2.2018.8582618

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Compared with SVM, the LSSVM facilitates the solution of Lagrange multiplier a and improves the convergence speed (Xie et al, 2021). Different kernel functions have different effects on the regression performance of LSSVM, and radial basis functions (RBFs) (Zhang et al, 2018) are used here as shown in equation ( 12) (Zhang et al, 2018)…”

Section: Lssvmmentioning

confidence: 99%

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Ultra-short-term wind power prediction based on double decomposition and LSSVM

Qin

Huang

Wang

et al. 2023

Transactions of the Institute of Measurement and Control

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To reduce the influence of the random fluctuation on wind power prediction, a new ultra-short-term wind power prediction model, based on wavelet decomposition (WD), variational mode decomposition (VMD), and least-squares support vector machine (LSSVM), is proposed in this paper. The method is based on the double decomposition and LSSVM, where the wind power sequence is decomposed by WD into low- and high-frequency components, which are further decomposed by VMD to obtain many modal components with tendency and periodicity. Multiple LSSVM prediction models are then established with historical wind power data and weather data as the inputs to obtain the predicted values of the multiple modal components. The final predicted values of wind power are achieved by data fusion of outputs of these LSSVM models. The experimental results show that the MAPE (mean absolute percentage error) of the combined prediction model is 4.66%, which is the best compared with nine benchmark models. This demonstrates the high performance of the proposed WD-VMD-LSSVM model for short-term prediction of wind power.

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Section: Lssvmmentioning

confidence: 99%

“…Set { ( x 1 , u k 1 ) ,… , ( x n , u k n ) } as the input training sample set and use the following high-dimensional linear mapping to fit the training sample set, as shown in equation (13) (Zhang et al, 2018)…”

Section: Ultra-short-term Wind Power Prediction Modeling Based On Wd-...mentioning

confidence: 99%

Ultra-short-term wind power prediction based on double decomposition and LSSVM

Qin

Huang

Wang

et al. 2023

Transactions of the Institute of Measurement and Control

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show abstract

“…In [28,29], the authors optimized the signal time domain waveform using FFT, although they did not consider the local features of the time frequency. In [30,31], the authors used Wavelet Transform to decompose the wind speed time series, which has the drawback of requiring manual setting of the parameters. Compared to the two methods mentioned above, EMD [32] can actively identify the features of the input data with self-adaptability and multi-resolution.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

2022

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Improving the accuracy of wind power forecasting can guarantee the stable dispatch and safe operation of the grid system. Here, we propose an EMD-PCA-RF-LSTM wind power forecasting model to solve problems in traditional wind power forecasting such as incomplete consideration of influencing factors, inaccurate feature identification, and complex space–time relationships between variables. The proposed model incorporates Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), Random Forest (RF), and Long Short-Term Memory (LSTM) neural networks, And environmental factors are filtered by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm when pre-processing the data. First, the environmental factors are extended by the EMD algorithm to reduce the non-stationarity of the series. Second, the key influence series are extracted by the PCA algorithm in order to remove noisy information, which can seriously interfere with the data regression analysis. The data are then subjected to further feature extraction by calculating feature importance through the RF algorithm. Finally, the LSTM algorithm is used to perform dynamic time modeling of multivariate feature series for wind power forecasting. The above combined model is beneficial for analyzing the effects of different environmental factors on wind power and for obtaining more accurate prediction results. In a case study, the proposed combined forecasting model was verified using actual measured data from a power station. The results indicate that the proposed model provides the most accurate results when compared to benchmark models: MSE 7.26711 MW, RMSE 2.69576 MW, MAE 1.73981 MW, and adj-R2 0.9699203s.

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