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Short-term wind speed interval prediction using improved quality-driven loss based gated multi-scale convolutional sequence model

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  • Saeed, Adnan
  • Li, Chaoshun
  • Gan, Zhenhao

Abstract

Efficient estimation of the uncertainty associated with wind speed forecast is crucial for evaluating wind farms' power quality and operation. Typically, the performance of prediction interval (PI) generating models; is restricted in terms of computational efficiency inheritably from the sequential data processing; whereas restrictions in forecast quality are derived from the PI generation techniques. This paper presents an efficient Gated Multi-Scale Convolutional Sequence Model (GMSCSM) to forecast wind speed PIs. GMSCSM while conserving the ‘recurrence’ of LSTMs also offers ‘parallel input’ advantage of CNNs for better computational efficiency which is highly desirable for short-term forecasting models. In addition, capturing features at various scales in the sequence, GMSCSM learns both local details and global context which is vital for multi-horizon forecasts. The model generates quality PIs utilizing an improved quality-driven loss which we proposed by invoking calibration assessment in its existing definition. Forecasts generated for eight different datasets obtained from two different wind farms show an improvement of 30 % and 6 % in the average coverage width criterion index while reducing the model training time to nearly half and one third of the respective values obtained from traditional and LSTM based models which showcases the proposed model's excellent prediction capability and efficiency.

Suggested Citation

  • Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao, 2024. "Short-term wind speed interval prediction using improved quality-driven loss based gated multi-scale convolutional sequence model," Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:energy:v:300:y:2024:i:c:s036054422401363x
    DOI: 10.1016/j.energy.2024.131590
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    1. Harya Widiputra & Adele Mailangkay & Elliana Gautama & Abdelalim Elsadany, 2021. "Multivariate CNN-LSTM Model for Multiple Parallel Financial Time-Series Prediction," Complexity, Hindawi, vol. 2021, pages 1-14, October.
    2. Shrivastava, Nitin Anand & Lohia, Kunal & Panigrahi, Bijaya Ketan, 2016. "A multiobjective framework for wind speed prediction interval forecasts," Renewable Energy, Elsevier, vol. 87(P2), pages 903-910.
    3. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    4. Li, Chaoshun & Tang, Geng & Xue, Xiaoming & Chen, Xinbiao & Wang, Ruoheng & Zhang, Chu, 2020. "The short-term interval prediction of wind power using the deep learning model with gradient descend optimization," Renewable Energy, Elsevier, vol. 155(C), pages 197-211.
    5. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting," Energy, Elsevier, vol. 282(C).
    6. Nascimento, Erick Giovani Sperandio & de Melo, Talison A.C. & Moreira, Davidson M., 2023. "A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy," Energy, Elsevier, vol. 278(C).
    7. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao & Xie, Yuying & Liu, Fangjie, 2022. "A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution," Energy, Elsevier, vol. 238(PC).
    8. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    9. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    10. Dehghan Shoorkand, Hassan & Nourelfath, Mustapha & Hajji, Adnène, 2024. "A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    11. Peng, Xiaosheng & Wang, Hongyu & Lang, Jianxun & Li, Wenze & Xu, Qiyou & Zhang, Zuowei & Cai, Tao & Duan, Shanxu & Liu, Fangjie & Li, Chaoshun, 2021. "EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning," Energy, Elsevier, vol. 220(C).
    12. Quan, Hao & Srinivasan, Dipti & Khosravi, Abbas, 2014. "Uncertainty handling using neural network-based prediction intervals for electrical load forecasting," Energy, Elsevier, vol. 73(C), pages 916-925.
    13. Wenjie Lu & Jiazheng Li & Yifan Li & Aijun Sun & Jingyang Wang, 2020. "A CNN-LSTM-Based Model to Forecast Stock Prices," Complexity, Hindawi, vol. 2020, pages 1-10, November.
    14. 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).
    15. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
    16. 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.
    17. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
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