Research on Scheduling Strategies Simulation for Building Air-conditioning Systems Based on Transfer Imitation Learning

Authors

Qiaochu Wang, School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China
Yan Ding, School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China
Chuanzhi Liang, Technology and Industrialization Development Center of the Ministry of Housing and Urban Rural Development, Beijing 100835, China
Haozheng Zhang, School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China
Chen Huang, School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China

Abstract

Abstract: To solve the problem of unstable performance and inefficient training process of low-quality data conditions at the initial stage of online deployment of air conditioner scheduling, we propose a migration-imitation learning-based air conditioning scheduling strategy simulation method. Reinforcement learning methods are used to generate building operation strategies. A standard building simulation model serves as the source domain, upon which migration learning is applied. An imitation learning loss function is incorporated into the intelligent loss function to enhance algorithm performance. The results indicate that, compared with the non-use of migration learning, the proposed method can improve the operational efficiency by 16.2%, effectively resolving the operational instability issues at the initial stage of reinforcement learning training. Compared to methods without imitation learning, operational efficiency is enhanced by 11.5%, significantly improving the training efficiency of reinforcement learning.

Recommended Citation

Wang, Qiaochu; Ding, Yan; Liang, Chuanzhi; Zhang, Haozheng; and Huang, Chen (2024) "Research on Scheduling Strategies Simulation for Building Air-conditioning Systems Based on Transfer Imitation Learning," Journal of System Simulation: Vol. 36: Iss. 12, Article 11.
DOI: 10.16182/j.issn1004731x.joss.24-FZ0761
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss12/11

First Page

2884

Last Page

2893

CLC

TP29

Recommended Citation

Wang Qiaochu, Ding Yan, Liang Chuanzhi, et al. Research on Scheduling Strategies Simulation for Building Air-conditioning Systems Based on Transfer Imitation Learning[J]. Journal of System Simulation, 2024, 36(12): 2884-2893.

Corresponding Author

Ding Yan

DOI

10.16182/j.issn1004731x.joss.24-FZ0761