Machine learning-based multi-performance prediction and analysis of Earth-Air Heat Exchanger

Abstract

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 performance. Complex correlations and mutual exclusions exist among target variables, with a strong negative correlation (−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 performance, crucial for promoting its widespread application and enhancing renewable energy utilization.