当前位置: X-MOL 学术IEEE Internet Things J. › 论文详情
Data-Driven Solution for Optimal Pumping Units Scheduling of Smart Water Conservancy
IEEE Internet of Things Journal ( IF 9.515 ) Pub Date : 2019-12-31 , DOI: 10.1109/jiot.2019.2963250
Wei Dong; Qiang Yang

Internet of Things (IoT) technology provides the necessary foundation and support for smart city water management. To address the challenge of river pollution prevention and flood control requirements in the urban river system, this article proposes a data-driven model to carry out the optimal operation scheduling of water diversion and drainage pumping stations in the presence of the complex hydrometeorological constraints. The proposed solution in the model predictive control (MPC) framework first adopts the long short-term memory (LSTM) network through supervised learning from IoT data to simulate and predict the river flow dynamics and the water quality. Consequently, the optimal scheduling of controllable pumping stations to minimize the operational cost (e.g., the flocculant consumption) can be formulated as a stochastic optimization problem, while meeting the river flood control and water quality constraints. The particle swarm optimization (PSO) algorithm is further used to solve the above unit commitment (UC) optimization problem and obtain the optimal operational schedules of the water pumping units (e.g., startup time and working periods). The performance of the proposed optimal water pumping scheduling solution is evaluated through a field case study of the urban river diversion system and the numerical results clearly confirm its effectiveness and improved economic performance compared to the existing benchmark solution.
更新日期:2020-03-16

 

全部期刊列表>>
宅家赢大奖
向世界展示您的会议墙报和演示文稿
全球疫情及响应:BMC Medicine专题征稿
新版X-MOL期刊搜索和高级搜索功能介绍
化学材料学全球高引用
ACS材料视界
x-mol收录
自然科研论文编辑服务
南方科技大学
南方科技大学
舒伟
中国科学院长春应化所于聪-4-8
复旦大学
课题组网站
X-MOL
香港大学化学系刘俊治
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug