Multi-Objective Optimal Scheduling of Reservoirs Integrating the Internet of Things and Deep Learning Models

Abstract

With the continuous upgrading and expansion of the concept and technology of the Internet of Things (IoT), the production methods of many traditional industries have undergone earth-shaking changes. IoT and Differential Evolution Algorithm (DEA) are integrated into the deep learning model for multi-objective optimal scheduling of reservoirs. Firstly, based on IoT, a multi-objective optimal dispatching system and a wireless sensor network module for the reservoir are designed, which are mainly used to collect and process data. Secondly, a constraint system that comprehensively considers the reservoir water balance and other aspects are proposed. A certain reservoir group is taken as the research object, combined with the collected information of the reservoir groups in previous years. The ecological dispatch is studied to ensure the ecological flow of 60m3/s, and the preparation and calculation of the monthly and ten-day water dispatch plans for normal and low water levels are realized. The dispatching results confirm that the ecological dispatching scheme of this reservoir group has high operability and reliability in practice.