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Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models

Author

Listed:
  • Guanghui Che

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Daocheng Zhou

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Rui Wang

    (Jinan Park Development Service Center, Jinan 250000, China)

  • Lei Zhou

    (Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China)

  • Hongfu Zhang

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Sheng Yu

    (School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China)

Abstract

In recent years, the energy crisis has become increasingly severe, and global attention has shifted towards the development and utilization of wind energy. The establishment of wind farms is gradually expanding to encompass forested regions. This paper aims to create a Weather Research and Forecasting (WRF) model suitable for simulating wind fields in forested terrains, combined with a long short-term time (LSTM) neural network enhanced with attention mechanisms. The simulation focuses on capturing wind characteristics at various heights, short-term wind speed prediction, and wind energy assessment in forested areas. The low-altitude observational data are obtained from the flux tower within the study area, while high-altitude data are collected using mobile radar. The research findings indicate that the WRF simulations using the YSU boundary layer scheme and MM5 surface layer scheme are applicable to forested terrains. The LSTM model with attention mechanisms exhibits low prediction errors for short-term wind speeds at different heights. Furthermore, based on the WRF simulation results, a wind energy assessment is conducted for the study area, demonstrating abundant wind energy resources at the 150 m height in forested regions. This provides valuable support for the site selection in wind farm development.

Suggested Citation

  • Guanghui Che & Daocheng Zhou & Rui Wang & Lei Zhou & Hongfu Zhang & Sheng Yu, 2024. "Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models," Sustainability, MDPI, vol. 16(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:898-:d:1323236
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    References listed on IDEAS

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    Cited by:

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