An Intelligent RL-Based Scheduler to Control Flooding in a Renewable Energy Powered Automatic Water Dam Control System

The use of non-renewable energy to operate large-scale systems is proving detrimental to the environment through pollution as well as leading to its depletion. As an alternative, the focus of industries, both private and public, is shifting towards the profitable use of renewable energy sources where it is available in abundance for such purposes. Water dams have been built above rivers to tap this natural resource to supply water to the nearby population. However, in the rainy season, excessive flooding wreaks havoc in the surrounding areas, leading to crop failures, damage to households and dam infrastructure. To counter this problem, this paper proposes an intelligent scheduler for an efficient renewable-energy powered automatic water dam control system which is modelled as a non-convex optimization problem. The scheduler uses the Soft Actor Critic with Emphasized Recent Experience and Prioritized Experience Replay Scheduler (SEPS), as the automatic Reinforcement Learning (RL) based scheduling algorithm to manage flooding in the control system through irrigation, hydroelectricity generation and reservoir storage such that it can sufficiently satisfy the needs of domestic households while also ensuring its efficient working. Simulated sensor readings record the current state of the environment and the proposed scheduler can make decisions based on these environmental variables with less human oversight and great accuracy. Experimental results clearly indicate that the SEPS scheduler outperforms its RL counterpart Proximal Policy Optimization Scheduler (PPOS) and the Natural Evolution Strategies Scheduler (NESS) in terms of average rewards obtained for managing floods efficiently and using renewable energy to run the control system.