Advanced thermal prediction for green roofs: CNN-LSTM model with SSA optimization

Green roofs serve as an innovative strategy for urban architecture, playing a pivotal role in mitigating urban heat island effects and enhancing building energy efficiency. This study proposes a hybrid model based on the Sparrow Search Algorithm (SSA) optimized Convolutional Neural Network - Long Short-Term Memory Network (CNN-LSTM) for rapid and accurate prediction of the thermal performance of green roofs. The model integrates the spatial feature extraction of CNN with the temporal analysis capability of LSTM, enhancing the predictive accuracy of multi-feature time series through global optimization by SSA. Taking the green roof in Guangzhou China as a case study and utilizing hourly meteorological and thermal property data, this model has demonstrated superior predictive performance: the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Rsquared (R2), and Mean Absolute Percentage Error (MAPE) for the prediction of inner surface temperature are 0.718, 0.908 %, 0.922, and 0.025 %, respectively; for the outer surface temperature prediction, the corresponding values are 0.618, 0.886 %, 0.983, and 0.028 %. Comparative analysis with existing AI models and ablation experiments further confirm the superiority of this model. Additionally, through Spearman correlation analysis, this study reveals the correlation between key input parameters and surface temperatures, providing data support for the effective design and management of green roofs. The model presented in this study not only provides an efficient tool for predicting the thermal performance of green roofs but also holds significant importance for promoting green building and sustainable urban development.