Application of artificial intelligence and communication technology to water and energy balance models

Abstract

The precise correction of water and energy balance is a significant difficulty in studying turbulence energy balance and water flow for agricultural purposes. The need for efficient water and energy management is growing as the world struggles to keep up with rising water and energy demands. This research examines artificial intelligence (AI)'s impact on the water flow and energy balance confluence subnetwork of sensing elements from all the original network's nodes. The study proposed an AI-based optimized sensor energy balance model (AI-SEBM) that uses pressure data to maintain energy balance in turbines and save water with the optimized energy source for agriculture usage. This research explores the potential for installing Kalpan hydraulic turbines, which are most effective during half-load operation, and to forecast all loads with little computing effort to balance energy in turbulence. To anticipate daily pressure readings and energy consumption across all nodes in the network, an AI-based optimization wireless sensor network is designed for communication and linked to an energy balance system. Sensors are strategically deployed at the network's nerve centres. The maximum flow algorithm is used for a grid representing the water and energy balance to determine the positions of the virtual nodes.