Affiliation:
1. a College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
2. b Smart Water Joint Innovation R&D Center, Tongji University, Shanghai 200092, China
Abstract
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.
Funder
National Natural Science Foundation of China
Shanghai Science and Technology Development Foundation
Subject
Management, Monitoring, Policy and Law,Pollution,Water Science and Technology,Ecology,Civil and Structural Engineering,Environmental Engineering