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A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy

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  • Nascimento, Erick Giovani Sperandio
  • de Melo, Talison A.C.
  • Moreira, Davidson M.

Abstract

This work presents a novel transformer-based deep neural network architecture integrated with wavelet transform for forecasting wind speed and wind energy (power) generation for the next 6 h ahead, using multiple meteorological variables as input for multivariate time series forecasting. To evaluate the performance of the proposed model, different case studies were investigated, using data collected from anemometers installed in three different regions in Bahia, Brazil. The performance of the proposed transformer-based model with wavelet transform was compared with an LSTM (Long Short Term Memory) model as a baseline, since it has been successfully used for time series processing in deep learning, as well as with previous state-of-the-art (SOTA) similar works. Results of the forecasting performance were evaluated using statistical metrics, along with the time for training and performing inferences, both using quantitative and qualitative analysis. They showed that the proposed method is effective for forecasting wind speed and power generation, with superior performance than the baseline model and comparable performance to previous similar SOTA works, presenting potential suitability for being extended for the general purpose of multivariate time series forecasting. Furthermore, results demonstrated that the integration of the transformer model with wavelet decomposition improved the forecast accuracy.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:c:s0360544223010721
    DOI: 10.1016/j.energy.2023.127678
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    References listed on IDEAS

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    1. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
    2. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    3. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    4. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
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    Citations

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    Cited by:

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    2. Wu, Tangjie & Ling, Qiang, 2024. "STELLM: Spatio-temporal enhanced pre-trained large language model for wind speed forecasting," Applied Energy, Elsevier, vol. 375(C).
    3. Hexiang Zheng & Hongfei Hou & Ziyuan Qin, 2024. "Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves," Sustainability, MDPI, vol. 16(21), pages 1-18, October.
    4. Raghu Raman & Sangeetha Gunasekar & Deepa Kaliyaperumal & Prema Nedungadi, 2024. "Navigating the Nexus of Artificial Intelligence and Renewable Energy for the Advancement of Sustainable Development Goals," Sustainability, MDPI, vol. 16(21), pages 1-25, October.
    5. 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).
    6. Sun, Xiaoying & Liu, Haizhong, 2024. "Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S," Energy, Elsevier, vol. 305(C).
    7. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.

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