Intelligent real-time scheduling of water supply network based on deep learning

Abstract

Inappropriate scheduling plans can result in additional economic losses and the safety of water distribution network (WDN). Optimizing manual experience based scheduling plans can help water utilities rationally allocate water plants and pump stations, ensuring the safety, stability, and economy of the water supply system. However, there is a lack of real-time, rational, and optimized scheduling methods. To address this, we proposed a novel intelligent scheduling framework based on deep learning. In this framework, two neural network models, multi-heads convolutional gated recurrent unit network (MH-CGRU) and multi-head gated recurrent unit network (MH-GRU), can effectively extract key features from the WDNs. Operating data were used as decision variables to predict and generate scheduling orders for water plants and pump stations, respectively. The rationality of the orders is verified by combining a high precision online hydraulic model and the evaluation of the operational status of the WDNs. This system has been deployed in a real WDN and put into practical application. From June to November of 2022, the total adoption rate of all orders reached 96.29%, with the average deviation between predicted and actual control targets being less than 5%, and energy consumption decreased by 3.05% compared to the previous year. HIGHLIGHTS Proposed an optimized, real-time, and secure intelligent control method for water supply networks based on deep learning algorithms.; Presented a data evaluation approach for selecting high-quality samples from monitoring data in the water supply system.; Developed an intelligent verification mechanism that combines a high-precision hydraulic model with scheduling orders for improved control reliability.;

Keywords

• optimal pump scheduling of water supply networks
• data-driven models
• energy saving and emission reduction