Comparative analysis of the optimized ANN, SVM, and tree ensemble models using Bayesian optimization for predicting GSHP COP

Abstract

This study analyzes the performances of three machine learning (ML) methods for predicting the ground source heat pump (GSHP) coefficient of performance (COP) by applying Bayesian optimization to maximize the ML performances. Predicting accurate COP is a prerequisite for the efficient operation of a GSHP system with energy saving potentials, and ML application has been proven to be an ideal solution for predicting COP. ML performances can be further enhanced by tuning each factor affecting the learning process, yielding the overall efficient operation of the GSHP system. To derive a compelling COP prediction model for GSHP system control, a residential building with a GSHP was modeled using TRNSYS 18, and data were acquired for ML based on temperature variables and flow rate scenarios. Using different ML methods, i.e., an artificial neural network (ANN), a tree ensemble, and a support vector machine (SVM), three COP prediction models were developed in MATLAB. The performance of each model was optimized using Bayesian optimization to determine the optimal combination of the hyperparameters, which dramatically affect the ML methods. The R-squared (R-2) and coefficient of variation of the root mean square error (Cv(RMSE)) results indicate that the ANN model exhibited the highest prediction accuracy, followed by the tree ensemble and SVM models. Particularly, the R-2 of the SVM model did not meet the standards recommended by ASHRAE. Additionally, the ANN model exhibited the lowest maximum error (-0.025 <= x < 0.025), demonstrating high predictive ability. Thus, the proposed ANN-based prediction model can be employed in the control algorithm of GSHP systems to promote energy efficiency by determining the system variables affording the highest COP.