Optimization of Energy-Saving Control for Pumping Station Water Supply System Based on DQN Algorithm
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.
pumping station water supplyoptimal controlingDQN algorithmMarkov decision processenergy saving and emission reduction
国家科技期刊平台
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维普
