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VMD-SCINet: a hybrid model for improved wind speed forecasting

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Abstract

Wind energy is gaining importance owing to its renewable and environmentally friendly characteristics. However, the variability and stochastic nature of wind speed makes accurate forecasting difficult. Hence, this study introduces a novel approach (VMD-SCINet) for wind speed forecasting (WSF) by integrating the strengths of variational mode decomposition (VMD) and sample convolution and interaction network (SCINet) architecture for the prediction of wind speed. This study utilizes VMD as a denoising technique for wind speed data and incorporates SCINet to capture global patterns and long-range dependencies for the WSF. The wind speed data acquired from two distinct sites: Leicester, and Portland is used for the evaluation. To evaluate the WSF capability of the proposed hybrid model, it’s performance is compared to robust models using data from two wind farms across six different time horizons such as 5-min, 10-min, 15-min, 30-min, 1-hour, and 2-hours. The results from two experiments demonstrate that the proposed approach outperforms other models, leading to a significant improvement in WSF accuracy across all evaluated time intervals.

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Availability of Data and Materials

The datasets that support the conclusions of this study can be obtained from the corresponding author on reasonable request.

Abbreviations

AI:

Artificial Intelligence

ARIMA:

Autoregressive Integrated Moving Average

ARMA:

Autoregressive Moving Average

Bi-LSTM:

Bidirectional LSTM

BiRNN:

Bidirectional RNN

BPNN:

Back Propagation Neural Network

CEEMDAN:

Complete Ensemble Empirical Mode Decomposition

DLM:

Deep Learning Models

DT:

Decision Tree

EEMD:

Ensemble Empirical Mode Decomposition

ELM:

Extreme Learning Machine

EMD:

Empirical Mode Decomposition

EWT:

Empirical Wavelet Transform

IMF:

Intrinsic Mode Function

KNN:

K-Nearest Neighbours

LSTM:

Kernel Method Based Support Vector Regression

k-SVR:

Long Short Term Memory

MAE:

Mean Absolute Error

MLM:

Machine Learning Models

MSE:

Mean Square Error

RF:

Random Forest

RMSE:

Root Mean Square Error

RNN:

Recurrent Neural Network

\(R^2\) :

R-Square Score

SARIMA:

Seasonal Autoregressive Moving Average

SCINet:

Sample Convolution and Interaction Network

SSA:

Singular Spectrum Analysis

TCN:

Temporal Convolutional Network

VMD:

Variational Mode Decomposition

WSF:

Wind Speed Forecasting

WT:

Wavelet Transform

1D-CNN:

1-Dimensional Convolutional Neural Network

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Funding

The authors received no funding for conducting this study.

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Author notes

  1. Kiran Teeparthi contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, 534102, Andhra Pradesh, India

    Srihari Parri & Kiran Teeparthi

Authors
  1. Srihari Parri
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  2. Kiran Teeparthi
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Contributions

Srihari Parri: Conceptualization, methodology, software. visualization, performed the experiments. Kiran Teeparthi: writing - review & editing, supervision, validation.

Corresponding author

Correspondence to Kiran Teeparthi.

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Communicated by: H. Babaie.

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Parri, S., Teeparthi, K. VMD-SCINet: a hybrid model for improved wind speed forecasting. Earth Sci Inform 17, 329–350 (2024). https://doi.org/10.1007/s12145-023-01169-3

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