Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China

Ruyang Yu, Kai Zhang, Brindha Ramasubramanian, Shu Jiang, Seeram Ramakrishna and Yuhang Tang

Energy, 2024, vol. 296, issue C

Abstract: Accurate prediction of the average thermal extraction load (ATEL) in hydrothermal heating systems optimizes energy recovery, though numerical models are constrained by modeling accuracy and computational time costs. Data-driven modeling can efficiently screen alternative production plans and enhance daily operational decisions. However, ensemble learning applications for forecasting ATEL lack comprehensive comparisons and appropriate algorithm selection. This study evaluates six ensemble learning algorithms: random forest (RF), adaptive boosting (AdaBoost), gradient boosting decision trees (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightBGM) and categorical boosting (CatBoost) on 1159 data points from a homogeneous thermal-hydraulic coupling numerical model. The superior performance of the CatBoost is characterized by the highest accuracy (with the lowest root mean square error (RMSE) of 150.6 kW–170.7 kW, minimal mean absolute percentage error (MAPE) of 0.66%–0.73%, and exceptional R2 values of 0.999), commendable stability, reasonable computational cost and low uncertainty. LightGBM, XGBoost, and GBDT are identified as suitable substitutes, whereas RF is a possible substitute. Moreover, the production rate is identified as the most significant factor contributing to the ATEL, followed by the well spacing and injection temperature. While perforation depth has the least influence on heat recovery.

Keywords: Geothermal energy; Machine learning; Heating load; CatBoost; Guanzhong Basin (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224009198
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:296:y:2024:i:c:s0360544224009198

DOI: 10.1016/j.energy.2024.131146

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().