所属单位：

建筑学院

发表刊物：

Renewable Energy

摘要：

The Earth-Air Heat Exchanger (EAHE) plays a crucial role in global renewable energy utilization. Despite various EAHE models, a machine learning-based multi-performance prediction model is lacking. This study introduces a framework based on diverse machine learning models for precise prediction and comprehensive analysis of EAHE’s multiple performance indicators. Initially, the theoretical EAHE model is established and validated. Six feature variables and four target variables are input to generate a dataset for machine learning. Six machine learning models are then selected for training, optimization, and validation. Subsequently, model interpretation and analysis are conducted using SHapley additive exPlanations and Partial dependence plots. Results indicate the optimized-extreme gradient boosting model performs the best, with a determination coefficient of 0.98, root mean squared error of 22.98, and mean absolute error of 8.88, identified as the top multi-performance prediction model for EAHE. Notably, pipe diameter, air velocity, and ground type significantly influence EAHE perfor mance. Complex correlations and mutual exclusions exist among target variables, with a strong negative cor relation ( 0.59) between the coefficient of performance and payback time, highlighting the challenge of simultaneous improvement. This study presents a valuable framework for predicting and analyzing EAHE per formance, crucial for promoting its widespread application and enhancing renewable energy utilization.

合写作者：

姚珊珊

第一作者：

闫增峰

论文类型：

期刊论文

通讯作者：

倪平安,雷馥铭

卷号：

277

ISSN号：

0960-1481

是否译文：

否

发表时间：

2024-06-06