Research on data-driven identification and prediction of heat response time of urban centralized heating system

Wei Zhong, Wei Huang, Xiaojie Lin, Zhongbo Li and Yi Zhou

Energy, 2020, vol. 212, issue C

Abstract: Heat response time (HRT) is one of the key dynamic response characteristics of urban centralized heating system (UCHS). HRT is also critical to the operation dispatch of large-scale UCHS. This study proposes a data-driven approach to identify and predict HRT. Due to the diversity of operational data, this study applies correlation analysis and feature fusion (both linear and nonlinear) to generate feature sets. This study further develops the prediction models of HRT made up of three different feature sets and four machine learning models: Linear Regression (LR), Support Vector Machine Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). A UCHS in Zhengzhou is selected as the demo site to show the effectiveness of this approach. HRT of investigated substations range from 1000 to 3000 s. The correlation analysis indicates that the heating area’s square root is most relevant to HRT (correlation coefficient close to 0.68) compared to other features. Feature fusion is critical in HRT analysis. The performances of all four prediction models are improved with fused features added to sets. XGBoost model outperforms other models in terms of model accuracy. Quantification of HRT could be useful in the optimized operation control of large-scale UCHS.

Keywords: Heat response; Machine learning; Data-driven; District heating (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544220318491
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:212:y:2020:i:c:s0360544220318491

DOI: 10.1016/j.energy.2020.118742

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().