Optimizing pipe network design and central plant positioning of district heating and cooling System: A Graph-Based Multi-Objective genetic algorithm approach

Su, Lingqi; Nie, Ting; Ho, Chi On; Zheng, Yuan; Calvez, Philippe; Jain, Rishee K.; Donat, Romain

doi:10.1016/j.apenergy.2022.119844

Cited by 13 publications

(1 citation statement)

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“…In [10], Multi-Objective Genetic Algorithm was used to drive reliable correlations for estimating the rate of heat losses from the twin-and triple-pipes. Su et al proposed a Graph-Based Multi-Objective genetic algorithm approach for DHS to optimize pipe network layout and central plant positioning [11]. To address the problem in district energy systems operating at near-ambient temperatures, a particle swarm optimization approach was adopted to design the optimization framework for DHS [12].…”

Section: Related Workmentioning

confidence: 99%

Application of reinforcement learning for energy consumption optimization of district heating system

Deng,

Eklund,

Sierla

et al. 2023

2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)

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Heating residential spaces consumed 64 percent of total household energy consumption in Finland. Considering the heat transfer and time delay in the district heating system, the calculation of setpoints of supply temperature requires a comprehensive understanding of the real system, and experienced operators need to manually determine the setpoints. To save energy, a more effective and accurate method is needed for setpoints calculation. In this paper, a reinforcement learning based method is proposed. Through interacting with an Aprosbased simulation model, the agents learn to calculate supply temperature parallelly for lowering energy costs. Simulation results show that the proposed method outperforms the existing method and has the potential to address the problem in real factories.

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Section: Related Workmentioning

confidence: 99%