净水技术2024，Vol.43Issue(4)：60-67,127,9.DOI:10.15890/j.cnki.jsjs.2024.04.008
基于DQN算法的泵站供水系统节能控制优化
Optimization of Energy-Saving Control for Pumping Station Water Supply System Based on DQN Algorithm
摘要
Abstract
To address the issue of significant energy waste caused by manually adjusting the running speed and start-stop of the pumps in the pump station,the deep Q-learning network(DQN)algorithm was introduced to automatically optimize the operation of each pump during the operation of the pump unit by obtaining the current operating status.Operating parameters,on the premise that each pump was in the high-efficiency area,improved the overall efficiency of the pump unit.The state optimization problem of the pump unit was described mathematically and using the Markov decision process.At the same time,the state,action and reward value of the pump unit were defined,and a DQN network was established.As an example,Shenzhen M WTP was validated verified in a custom simulation environment built by Gym.Compared to manual regulation,DQN algorithm regulation reduced the loss of energy consumption by 8.84%,and could save electricity consumption per tons of water by 1.27×10-2 kW·h/t a year,which contributing to energy saving,emission reduction,and good economic.At the same time,the DQN algorithm,which had the advantages of autonomy,real-time,and generalizability,could adapt to changes in the water supply conditions through online learning.关键词
泵站供水/优化调度/DQN算法/马尔可夫决策过程/节能减排Key words
pumping station water supply/optimal controling/DQN algorithm/Markov decision process/energy saving and emission reduction分类
土木建筑引用本文复制引用
陈财会,张天宇,黄健康,金典,王卓悦,张小磊..基于DQN算法的泵站供水系统节能控制优化[J].净水技术,2024,43(4):60-67,127,9.基金项目
深圳市可持续发展科技专项:二次供水水质安全保障与新型消毒副产物末端控制技术及设备研发(KCXFZ20201221173602008) （KCXFZ20201221173602008）
哈尔滨工业大学(深圳)课程教学项目:"双碳"目标背景下专业课程教学设计与实践——以《泵与泵站》课程为例(HITSZERP21003) （深圳）
