Long-term thermomechanical displacement prediction of energy piles using machine learning techniques

Huafu Pei, Huaibo Song, Fanhua Meng and Weiling Liu

Renewable Energy, 2022, vol. 195, issue C, 620-636

Abstract: Energy piles have attracted much attention in recent years because they provide a profitable solution for efficient shallow geothermal energy utilization in different climatic regions. However, the long-term performance design of energy piles under different thermal scenarios currently relies on time-consuming and computationally expensive methods such as numerical simulations, which seriously hinders the further engineering application of energy piles. To this end, this paper provides a high-precision and computationally efficient model for predicting the long-term performance of energy pile design through an artificial neural network machine learning process. First, experimentally validated numerical models are developed. Then, they are utilized to generate the training and evaluate datasets for the proposed model by inputting sixty typical thermal load distributions in different regions of China. Finally, the performance of the proposed model is evaluated by comparing its calculations with those obtained using numerical models. The results show that the proposed model can predict the long-term performance of energy piles and enrich the current methods for the long-term design of energy piles. Furthermore, its features that tremendously reduce the computational time and minimum required resources make it an excellent supplement compared with numerical simulations.

Keywords: Energy pile; Geothermal energy; Machine learning; Long-term behavior (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:195:y:2022:i:c:p:620-636

DOI: 10.1016/j.renene.2022.06.057

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