LSTM-GRU hybrid model for multi-layer microclimate prediction in solar greenhouse

for temperature and 5% for humidity, correlation coefficients (R²) of 0.97 and 0.99, respectively, and 89.63% of temperature predictions within ± 1.0℃ and 94.27% of humidity predictions within ± 5%. Compared with the recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models, the hybrid architecture generally showed the best overall predictive performance across canopy heights. For prediction at the 2 m reference height, incorporating equipment operational status (thermal blankets and ventilation) as input variables reduced prediction errors by 23-55% relative to environmental-only inputs. The results further revealed marked vertical microclimate gradients under different operational conditions. This study provides a data-driven framework for multi-layer microclimate prediction in solar greenhouses and may support future real-time monitoring and environmental control application.