Energy Efficiency Scheduling of Urban Water Supply Based on Reinforcement Learning and Algorithm Optimization

With the continuous expansion of urban water supply systems and the increasing requirements for energy efficiency, it has become very important to optimize energy efficiency while ensuring water supply safety and stability. To address this issue, this paper proposes an urban water supply energy efficiency scheduling method based on reinforcement learning and algorithm optimization. First, this paper constructs the state space and action space of the water supply system by designing a reinforcement learning model based on the Deep Q-Network (DQN). Then, through the reinforcement learning algorithm, the system can adjust the scheduling strategy in real time to adapt to the dynamically changing water supply demand and equipment status. Finally, combined with the Multi-Objective Particle Swarm Optimization (MPSO) algorithm, the decision results of the reinforcement learning model are subsequently optimized to further improve the economy and energy efficiency of system scheduling. The experimental results show that when the DQN + MPSO method is used for urban water supply energy efficiency scheduling, the total energy consumption is 830.14 kWh. At the same time, the water supply reliability of the DQN + MPSO method is 92.5%, which is significantly higher than 85.3 % of MPC (Model Predictive Control) and 88.1 % of GA (Genetic Algorithm). In the above data conclusions, the scheduling method based on the combination of reinforcement learning and optimization algorithm proposed in this paper has shown significant advantages in improving the energy efficiency, reliability and response speed of the water supply system, and has strong application potential.