| Title |
Nonlinear Structural Assessment of Greenhouses Based on Predicted Wind Speed and Snow Depth |
| Authors |
김동우(Kim, Dongwoo);서병훈(Seo, Byunghun);김동수(Kim, Dongsu);조예림(Jo, Yerim);이종혁(Lee, Jonghyuk);최원(Choi, Won) |
| DOI |
https://doi.org/10.12652/Ksce.2025.45.4.0431 |
| Keywords |
비닐온실; 시계열 예측; 트랜스포머; 구조해석 Greenhouse; Time series prediction; Transformer; Structural analysis |
| Abstract |
With the increasing frequency and severity of extreme weather events such as strong winds and heavy snowfall due to climate change, structural damage to plastic greenhouses in Korea has also been on the rise. To mitigate such damage in advance, this study developed a Transformer-based time series forecasting model to predict snow depth and wind speed, and integrated the predictions with a nonlinear structural analysis model to estimate greenhouse safety factors. The proposed model was designed to forecast snow depth and wind speed 1 to 12 hours ahead based on the previous 6 hours of meteorological data, and its hyperparameters were optimized using Bayesian optimization. The model demonstrated high accuracy in predicting snow depth, with RMSE ranging from 0.66 to 3.78 cm, while the prediction performance for wind speed showed R2 values between 0.17 and 0.67. Comparative analysis with an LSTM model
under the same conditions revealed that the Transformer model was less sensitive to the non-stationary nature of time series data and exhibited more stable performance in long-term forecasting. Finally, the model was applied to actual damage cases caused by heavy snowfall, and the resulting safety factors from structural analysis were found to be correlated with the observed damage area. This study provides a technical foundation for the preemptive prediction and response to greenhouse damage caused by heavy snowfall and is expected to contribute meaningfully to reducing agricultural losses in the future. |