Article Title
Differential pressure reset strategy based on reinforcement learning for chilled water systems
Keywords
water system, differential pressure reset, reinforcement learning control, energy saving
Abstract
Air conditioning water systems account for a large proportion of building energy consumption. In a pressure-controlled water system, one of the key measures to save energy is to adjust the differential pressure setpoints during operation. Typically, such adjustments are based either on certain rules, which rely on operator experience, or on complicated models that are not easy to calibrate. In this paper, a data-driven control method based on reinforcement learning is proposed. The main idea is to construct an agent model that adapts to the researched problem. Instead of directly being told how to react, the agent must rely on its own experiences to learn. Compared with traditional control strategies, reinforcement learning control (RLC) exhibits more accurate and steady performances while maintaining indoor air temperature within a limited range. A case study shows that the RLC strategy is able to save substantial amounts of energy.
Recommended Citation
Zhang, Xinfang; Li, Zhenhai; Li, Zhengwei; Qiu, Shunian; and Wang, Hai
(2022)
"Differential pressure reset strategy based on reinforcement learning for chilled water systems,"
Building Simulation: An International Journal: Vol. 15:
Iss.
2, Article 15.
DOI: https://doi.org/10.1007/s12273-021-0808-5
Available at:
https://dc.tsinghuajournals.com/building-simulation/vol15/iss2/15
