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Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition

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  • Xu, Li
  • Ou, Yanxia
  • Cai, Jingjing
  • Wang, Jin
  • Fu, Yang
  • Bian, Xiaoyan

Abstract

This work proposes a novel offshore wind speed prediction approach by combining statistical method and attention-based neural network with seasonal-trend decomposition procedure with loess (STL). STL is utilized to decompose the processed data into season, trend and residual terms. Then, an attention-based long short-term memory neural network model (AT-LSTM), possessing the advantages of generalization and high-dimensional function approximation, is modeled to train season and residual terms with obvious volatility characteristics. And a hybrid model of auto-regressive integrated moving average (ARIMA) and LSTM is applied to predict the linear and nonlinear sequences in trend term with relatively gentle feature, yielding the proposed STL-AR-LSTM-ATLSTM model. Wherein, the proposed method is verified through sufficient pre-judgment experiments on season, trend and residual terms, as well as detailed multi-model comparison experiments. Finally, microcosmic prediction results and predicted statistical frequency distributions indicate that the new model has better prediction effect on offshore wind, compared to ARIMA, AT-LSTM, ARIMA-AT-LSTM models. Meanwhile, the presented model can reduce the lag problem of predicted values and perform well in the prediction of extreme values.

Suggested Citation

  • Xu, Li & Ou, Yanxia & Cai, Jingjing & Wang, Jin & Fu, Yang & Bian, Xiaoyan, 2023. "Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition," Renewable Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:renene:v:216:y:2023:i:c:s096014812301011x
    DOI: 10.1016/j.renene.2023.119097
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    1. Bergmeir, Christoph & Hyndman, Rob J. & Benítez, José M., 2016. "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 303-312.
    2. Camilo Carrillo & José Cidrás & Eloy Díaz-Dorado & Andrés Felipe Obando-Montaño, 2014. "An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)," Energies, MDPI, vol. 7(4), pages 1-25, April.
    3. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    4. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Yuan, Ziting & Li, Chen & Shao, Shuai & Zhang, Jian, 2021. "New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory," Renewable Energy, Elsevier, vol. 179(C), pages 2174-2186.
    5. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    6. Wang, Jianzhou & Wang, Shiqi & Yang, Wendong, 2019. "A novel non-linear combination system for short-term wind speed forecast," Renewable Energy, Elsevier, vol. 143(C), pages 1172-1192.
    7. Geng, Xiulin & Xu, Lingyu & He, Xiaoyu & Yu, Jie, 2021. "Graph optimization neural network with spatio-temporal correlation learning for multi-node offshore wind speed forecasting," Renewable Energy, Elsevier, vol. 180(C), pages 1014-1025.
    8. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    9. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    10. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    11. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
    12. Wang, Jianzhou & Du, Pei & Niu, Tong & Yang, Wendong, 2017. "A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting," Applied Energy, Elsevier, vol. 208(C), pages 344-360.
    13. Yang, Shaobo & Deng, Zegui & Li, Xingfei & Zheng, Chongwei & Xi, Lintong & Zhuang, Jucheng & Zhang, Zhenquan & Zhang, Zhiyou, 2021. "A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast," Renewable Energy, Elsevier, vol. 173(C), pages 531-543.
    14. Jung, Jaesung & Tam, Kwa-Sur, 2013. "A frequency domain approach to characterize and analyze wind speed patterns," Applied Energy, Elsevier, vol. 103(C), pages 435-443.
    15. Dong, Yao & Wang, Jianzhou & Jiang, He & Shi, Xiaomeng, 2013. "Intelligent optimized wind resource assessment and wind turbines selection in Huitengxile of Inner Mongolia, China," Applied Energy, Elsevier, vol. 109(C), pages 239-253.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Md. Ahasan Habib & M. J. Hossain, 2024. "Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering," Energies, MDPI, vol. 17(5), pages 1-23, March.
    3. Zeng, Hang & Zhang, Hongmei & Guo, Jiansheng & Ren, Bo & Cui, Lijie & Wu, Jiangnan, 2024. "A novel hybrid STL-transformer-ARIMA architecture for aviation failure events prediction," Reliability Engineering and System Safety, Elsevier, vol. 246(C).

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