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Machine learning-based performance prediction for energy storage medium-deep borehole ground source heat pump systems

delete2025-04-01
delete4
PRE
AI
H
Huan Wang
C
Changfeng Wang
H
Hang Wen
Y
Yang Li
DOI：10.1016/j.jobe.2024.111471delete
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Abstract

Abstract

En 中文
With the rapid development of artificial intelligence, data-driven prediction models play an important role in energy demand forecasting and performance prediction. This study established performance prediction models for medium-deep borehole ground source heat pump (MDBGSHP) systems to predict the coefficient of performance (COP) of GSHP, utilizing back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), and particle swarm optimization-support vector machine (PSO-SVM) method that optimizes the penalty parameter (C), insensitive loss parameter (epsilon), and kernel parameter (gamma) of SVM using PSO. Operational data were collected through field experiments, and typical parameters were selected as features to establish the feature set. The feature set was then optimized using Pearson correlation analysis and recursive feature elimination (RFE), resulting in the creation of five distinct feature sets. Error analysis and trend evaluation results indicate that the combined feature set (FS5), which incorporates the advantages of multiple feature selection methods, demonstrates the best performance. The PSO-SVM model exhibits outstanding performance under various input conditions, achieving an accuracy of over 90 % within the error range. When using FS5, the PSOSVM model achieves a mean absolute percentage error (MAPE) of 0.037, mean absolute error (MAE) of 0.103, root mean square error (RMSE) of 0.125, coefficient of multiple determinations (R2) of 0.970, and explained variance score (EVS) of 0.967, showcasing excellent predictive performance. The research results can provide a basis for the formulation of storage-related operational parameters and the optimization of system operation for energy storage in MDBGSHP heating systems.
Keywords:
Machine learning
Energy storage
Medium-deep borehole ground source heat
pump
Coefficient of performance
Performance prediction

Journal

Journal of Building Engineering cover
Journal of Building Engineering
IF:
7.4
Papers:
1.6W
Citations:
6.6W

Organization

No organization information available