Surface Temperature Prediction of Grain Piles: VMD-SampEn-vLSTM-E Prediction Method Based on Decomposition and Reconstruction

Peiru Li, Bangyu Li, Jin Qian and Liang Qi ()
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Peiru Li: School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Bangyu Li: School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Jin Qian: School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Liang Qi: School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China

Sustainability, 2025, vol. 17, issue 20, 1-21

Abstract: The surface temperature of grain piles is sensitive to environmental fluctuations and exhibits nonlinear, multi-scale temporal patterns, making accurate prediction crucial for grain storage risk early warning. This paper proposes a decomposition–reconstruction prediction method integrating Sample Entropy (SampEn), variational mode decomposition (VMD), and a variant Long Short-Term Memory network (vLSTM). SampEn determines the optimal decomposition parameters, VMD extracts intrinsic mode functions (IMFs), and vLSTM, with peephole connections and coupled gates, conducts synchronous multi-IMF prediction. To explicitly account for environmental influences, a support vector regression (SVR) model driven by dew point temperature and vapor pressure deficit is employed to estimate the surface temperature variation Δ T . This component enhances the adaptability of the framework to dynamic storage conditions. The environment-derived Δ T is then integrated with the VMD-SampEn-vLSTM output to obtain the final forecast. Experiments on real-granary data from Liaoning, China demonstrate that the proposed method reduces mean absolute error (MAE) and root mean square error (RMSE) by 25% and 14%, respectively, compared with baseline models, thus achieving a significant improvement in prediction performance. This integration of data-driven prediction with environmental adjustment significantly improves forecasting accuracy and robustness.

Keywords: grain security; temperature forecasting; VMD; vLSTM; multiple time series (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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