Gaining CO 2 Reduction Insights with SHAP: Analyzing a Shower Heat Exchanger with Artificial Neural Networks

Sabina Kordana-Obuch (), Beata Piotrowska () and Mariusz Starzec
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Sabina Kordana-Obuch: Department of Infrastructure and Water Management, Rzeszow University of Technology, al. Powstańców Warszawy 6, 35-959 Rzeszow, Poland
Beata Piotrowska: Department of Infrastructure and Water Management, Rzeszow University of Technology, al. Powstańców Warszawy 6, 35-959 Rzeszow, Poland
Mariusz Starzec: Department of Infrastructure and Water Management, Rzeszow University of Technology, al. Powstańców Warszawy 6, 35-959 Rzeszow, Poland

Energies, 2025, vol. 18, issue 8, 1-23

Abstract: The application of shower heat exchangers (SHEs) allows for a reduction in the amount of energy necessary to heat domestic hot water (DHW). As a result, not only the costs of heating DHW but also the emission of harmful products of fuel combustion is reduced. However, the identification of key areas determining the resulting carbon dioxide emission remains an unexplored issue. For this reason, the main purpose of this paper was to comprehensively analyze the impact of parameters characterizing the operation of a horizontal SHE cooperating with an electric DHW heater on the potential reduction in CO 2 emission. As part of this research study, 16,200 CO 2 emission reduction values corresponding to different conditions of shower installation operation were determined. The analysis was carried out considering the location of the installation in different countries of the European Union. Artificial neural networks and SHAP analysis were used as tools. This research study showed that carbon intensity, corresponding to the location of the installation on the world map, and total daily shower length are of key importance in the prediction of carbon dioxide emission reduction. The efficiency of the DHW heater turned out to be the least important parameter. This research study proved that the greatest environmental benefits of using SHEs will be visible in countries where fossil fuels account for a large share of electricity production, such as Poland, and in buildings with significant water consumption.

Keywords: carbon emissions; graywater heat recovery; MLP neural networks; Python; SHAP analysis (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: 2025
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