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An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction

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  • Sareen, Karan
  • Panigrahi, Bijaya Ketan
  • Shikhola, Tushar
  • Sharma, Rajneesh

Abstract

Due to its renewable and ecological attributes, wind energy is receiving attention on a global scale. However, exact forecasting of wind speed is challenging owing to its variable and stochastic nature. Further, many algorithms established for wind speed forecasting has neglected the missing value imputation study. In fact, the prediction accuracy of any forecasting system may suffer significantly if any missing data occurs during forecasting due to power off of any unexpected device, failure of communication/transmission equipment or sensor, measurement error, or other unknown causes. In order to take care of this, first k-NN imputation algorithm is used. Thereafter, in the proposed framework, the characteristics of several data decomposition approaches namely EMD, EEMD & CEEMDAN are integrated with BiDLSTM Neural Network for de-noising the signal. For testing the accuracy of the proposed forecasting technique, this study investigates several wind speed datasets gathered from NIWE data portal for 5 cities i.e. Chandori, Bhogat, Gandhinagar, Charanka and Surat situated in the Gujarat state (India). The empirical finding demonstrates that the proposed k–NN–CEEMDAN-BiDLSTM based hybrid technique in terms of prediction accuracy outperforms other hybrid techniques exist in literature.

Suggested Citation

  • Sareen, Karan & Panigrahi, Bijaya Ketan & Shikhola, Tushar & Sharma, Rajneesh, 2023. "An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction," Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:c:s0360544223011933
    DOI: 10.1016/j.energy.2023.127799
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    References listed on IDEAS

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    14. Zhang, Chunyu & Fu, Xueqian & Qiu, Dawei & Badihi, Hamed & Gu, Haitong, 2026. "Robust imputation of missing photovoltaic power data using a weather- and context-aware hybrid transformer framework," Renewable Energy, Elsevier, vol. 256(PH).
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