Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction

Xuejing Zheng, Zhiyuan Shi, Yaran Wang, Huan Zhang and Zhiyun Tang

Energy, 2024, vol. 288, issue C

Abstract: As a vital infrastructure in modern cities, district heating (DH) systems provide stable high-quality heat sources. To satisfy the demands of energy-saving, emission-reduction policies, and user preferences, a high-precision digital simulation platform is essential for district heating network (DHN) scheduling. In this paper, a method for digital twin (DT) modeling based on hydraulic resistance identification using an online adaptive particle swarm optimization (APSO) algorithm is proposed. Additionally, Singular Spectrum Analysis and back propagation artificial neural network algorithms (SSA-BP) are proposed to achieve heat load prediction in order to provide sufficient conditions for DHN scheduling. The load prediction for the substation is completed by predicting the secondary return water temperature at the subsequent time step based on the historical outdoor temperature time series and secondary supply water temperature of the substation. Accordingly, simulation analysis has been conducted on its application to an existing DHN in Tianjin. With the proposed methods, the simulation errors are reduced for about 80 % substations in the case DHN compared to mechanistic models when simulating hydraulic conditions. Among these, 77.49 % of substations were reduced by 0 %–3 %, and 1.43 % of substations were reduced by 3 %–5 %. The prediction errors of secondary return water temperature for case substations A and B exhibit random variation trends, with maximum absolute errors within the range of 0.02 °C and 0.06 °C, respectively.

Keywords: District heating network; Digital twin; Hydraulic resistance identification; Adaptive particle swarm optimization; Back propagation artificial neural network algorithms (search for similar items in EconPapers)
Date: 2024
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:288:y:2024:i:c:s0360544223031201

DOI: 10.1016/j.energy.2023.129726

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