Comparative Analysis of Methods for Predicting Brine Temperature in Vertical Ground Heat Exchanger—A Case Study

Joanna Piotrowska-Woroniak (), Krzysztof Nęcka (), Tomasz Szul () and Stanisław Lis
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Joanna Piotrowska-Woroniak: Heating, Ventilation and Air Conditioning Department, Bialystok University of Technology, Wiejska 45E, 15-351 Bialystok, Poland
Krzysztof Nęcka: Faculty of Production and Power Engineering, University of Agriculture, Balicka 116 B, 30-149 Krakow, Poland
Tomasz Szul: Faculty of Production and Power Engineering, University of Agriculture, Balicka 116 B, 30-149 Krakow, Poland
Stanisław Lis: Faculty of Production and Power Engineering, University of Agriculture, Balicka 116 B, 30-149 Krakow, Poland

Energies, 2024, vol. 17, issue 6, 1-13

Abstract: This research was carried out to compare selected forecasting methods, such as the following: Artificial Neural Networks (ANNs), Classification and Regression Trees (CARTs), Chi-squared Automatic Interaction Detector (CHAID), Fuzzy Logic Toolbox (FUZZY), Multivariant Adaptive Regression Splines (MARSs), Regression Trees (RTs), Rough Set Theory (RST), and Support Regression Trees (SRTs), in the context of determining the temperature of brine from vertical ground heat exchangers used by a heat pump heating system. The subject of the analysis was a public building located in Poland, in a temperate continental climate zone. The results of this study indicate that the models based on Rough Set Theory (RST) and Artificial Neural Networks (ANNs) achieved the highest accuracy in predicting brine temperature, with the choice of the preferred method depending on the input variables used for modeling. Using three independent variables (mean outdoor air temperature, month of the heating season, mean solar irradiance), Rough Set Theory (RST) was one of the best models, for which the evaluation rates were as follows: CV RMSE 21.6%, MAE 0.3 °C, MAPE 14.3%, MBE 3.1%, and R 2 0.96. By including an additional variable (brine flow rate), Artificial Neural Networks (ANNs) achieved the most accurate predictions. They had the following evaluation rates: CV RMSE 4.6%, MAE 0.05 °C, MAPE 1.7%, MBE 0.4%, and R 2 0.99.

Keywords: brine water heat pumps; heating system energy efficiency; brine temperature from vertical ground heat exchangers; prediction model based: ANN; CART; CHAID; FUZZY; MARS; RT; RST; SRT (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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