Data-driven analysis and prediction of indoor characteristic temperature in district heating systems

Yanmin Wang, Zhiwei Li, Junjie Liu, Mingzhe Pei, Yan Zhao and Xuan Lu

Energy, 2023, vol. 282, issue C

Abstract: Many indoor temperature sensors have been installed in building rooms with district heating systems (DHSs) in China. To apply the collected indoor temperature data to district heating substation regulation, the indoor characteristic temperature of the heat substation (ICTS) should be calculated. However, previous calculation methods for ICTS cannot accurately evaluate their comprehensive characteristics. This study proposed a novel method to calculate the ICTS based on the entropy value and applied it to three actual DHSs in northern China. First, the variation trend and influencing factors of indoor temperature data were analyzed. Second, the ICTS was calculated and compared using various methods. Third, a correlation analysis of the ICTS and input features was performed. Finally, five typical machine learning models were used to predict the ICTS. The results demonstrated that the proposed method can better reflect the characteristics of the data and eliminate the impact of subjective preferences. The average mean absolute percentage errors of five models were less than 1%; the linear regression model performed best when denoising the original data, with a value of 0.12%. This study is helpful for better understanding the influencing factors on the ICTS and the strategies for adjusting feature values.

Keywords: District heating system (DHS); Indoor temperature; Data-driven; Machine learning (ML) (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223024179

DOI: 10.1016/j.energy.2023.129023

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