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

Guanghui Che, Daocheng Zhou, Rui Wang, Lei Zhou, Hongfu Zhang () and Sheng Yu ()
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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

Sustainability, 2024, vol. 16, issue 2, 1-17

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.

Keywords: forested region; WRF model; long short-term time neural network; wind field simulation; wind energy assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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