基于DDPG的冷源系统节能优化控制策略
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作者单位:

华南理工大学 机械与汽车工程学院,广州 510641

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E-mail: zhouxuan@scut.edu.cn.

中图分类号:

TU831.3

基金项目:

广东省自然科学基金项目(2017A030310162,2018A030313352).


Energy-saving optimization control strategy of cold source system based on DDPG algorithm
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Affiliation:

School of Mechanical & Automotive Engineering,South China University of Technology,Guangzhou 510641,China

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    摘要:

    针对传统冷源系统节能优化方式机理建模复杂,缺乏自我学习能力,优化速度较慢等问题,提出一种基于数据驱动和自我学习机制的冷源系统节能优化控制策略,设计冷源马尔可夫决策过程模型,并采用深度确定性策略梯度算法(DDPG)解决维数灾难与避免控制动作离散化问题.以夏热冬暖地区某大型办公建筑中央空调冷源系统为研究对象,对冷源系统控制策略进行节能优化,实现在满足室内热舒适性要求的前提下,减少系统能耗的目标.在对比实验中,DDPG控制策略下的冷源系统总能耗相比PSO控制策略和规则控制策略减少了6.47%和14.42%,平均室内热舒适性提升了5.59%和18.71%,非舒适性时间占比减少了5.22%和76.70%.仿真结果表明,所提出的控制策略具备有效性与实用性,相比其他控制策略在节能优化方面具有较明显的优势.

    Abstract:

    An energy-saving control strategy based on data-driven and self-learning mechanism is proposed to solve the problems of complex mechanism modeling, lack of self-learning ability and slow optimization speed of traditional energy-saving optimization methods for cold source systems. The Markov decision process model of cold source is designed and the deep deterministic policy gradient(DDPG) algorithm from policy gradient is used to solve the problem of dimensionality curse and can avoid discretization of control actions. In this paper, the central air conditioning cold source system of a large office building in the hot summer and warm winter area is selected as the research object, and the control strategy of the cold source system is optimized. The results show that under the premise of meeting the indoor thermal comfort requirement, the energy-saving control strategy of the system is realized with the goal of minimizing the energy consumption. In the comparison experiment, the total energy consumption of the cold source system under the DDPG control strategy is reduced by 6.47% and 14.42% compared with the PSO control strategy and the rule based control strategy, the average indoor thermal comfort is increased by 5.59% and 18.71%, and the proportion of total uncomfortable time is decreased by 5.22% and 76.70%, respectively. The simulation results show that the proposed control strategy has effectiveness and practicality, which has obvious advantage in energy-saving optimization compared with other control strategies.

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引用本文

闫军威,黄琪,周璇.基于DDPG的冷源系统节能优化控制策略[J].控制与决策,2021,36(12):2955-2963

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  • 在线发布日期: 2021-11-18
  • 出版日期: 2021-12-20