BP neural network multi-module green roof thermal performance prediction model optimized based on sparrow search algorithm

• Published in: Journal of Building Engineering 2024-11, Vol.96, p.110615, Article 110615
• Main Authors: Wang, Jun, Chen, Bochao, Yang, Wansheng, Xu, Ding, Yan, Biao, Zou, Endian
• Format: Article
• Language: English
• Subjects: Artificial neural network; Building energy efficiency; Green roof; Thermal performance prediction
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2024.110615

Green roofs, as a passive building energy-saving technology, offer a range of benefits including reduced energy consumption and mitigation of the urban heat island effect. To analyze the thermal performance of green roofs more accurately and efficiently, this study introduces a novel predictive model that utilizes a Sparrow Search Algorithm (SSA) optimized Backpropagation Neural Network (BPNN) for forecasting the thermal performance of green roofs in subtropical regions. Compared to traditional BPNN, the SSA-BPNN model enhances the network's convergence and generalization capabilities by optimizing initial weights and thresholds. The model, established using meteorological data from Guangzhou, China, and simulated green roof temperature data, underwent rigorous training and validation. The results indicate that predictive accuracy has significantly improved across all seasons, with substantial reductions in all 12 evaluation metrics, and the model GR3 (SSA-BP) emerging as the optimal model. Particularly in summer, the SSA-BPNN model exhibits the highest accuracy, underscoring its effectiveness for subtropical climates. The findings suggest that the SSA-BPNN model is a powerful tool for sustainable urban planning and green roof design in subtropical regions, marking a significant advancement in the integration of artificial intelligence in environmental engineering. •A neural network approach for thermal performance prediction of green roofs is proposed.•An artificial neural network prediction framework was developed.•The effect of input parameters on the prediction results was considered.•Demonstrating the benefits of the Sparrow search algorithm.