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

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中图分类号:

TU831.3

基金项目:

广东省自然科学基金资助项目(2017A030310162,2018A030313352)


Energy-Saving Optimization Control Strategy of Cold Source System Based on DDPG Algorithm
Author:
Affiliation:

South China University of Technology

Fund Project:

the Natural Science Foundation of Guangdong Province( 2017A030310162,2018A030313352)

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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,提出了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,设计了冷源马尔可夫决策过程模型,并采用源于策略梯度的DDPG算法解决维数灾难与避免控制动作离散化问题。本文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了节能优化,实现了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,本研究所提出的控制策略具备有效性与实用性,相比其它控制策略在节能优化方面有较明显的优势。

    Abstract:

    An energy-saving control strategy based on data-driven and self-learning mechanism was 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 system. The Markov decision process model of cold source was designed and the DDPG algorithm from policy gradient was 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 was selected as the research object and the control strategy of cold source system was 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 cold source system under DDPG control strategy is reduced by 6.47% and 14.42% than PSO control strategy and rule based control strategy, the average indoor thermal comfort is increased by 5.59% and 18.71%, the proportion of total uncomfortable time is decreased by 5.22% and 76.70%, respectively. The simulation results show that the control strategy we proposed has effectiveness and practicality, which has obvious advantage in energy-saving optimization compared with other control strategies.

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  • 收稿日期:2020-06-11
  • 最后修改日期:2020-09-25
  • 录用日期:2020-10-12
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