Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

The Internet of Things (IoT) technologies has gained significant interest in the design smart grids (SGs). increasing amount distributed generations, maturity existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly electrical stored methods are developing as diagnoses for its procedure was becoming further compelling. dynamic using Electric Vehicles (EVs) is comparatively standard because excellent property flexibility however chance damage to battery there event overcharging or deep discharging mass penetration deeply influences grids. This paper offers a new Hybridization Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) IoT Enabled systems. proposed HBFOA-SAE majorly intends effectually estimate state charge (SOC) values based system. To accomplish this, SAE technique executed proper determination SOC Next, improving performance estimation process, HBFOA employed. In addition, derived by integration hill climbing (HC) concepts BFOA improve overall efficiency. For ensuring better outcomes model, comprehensive set simulations were performed inspected under several aspects. experimental results reported supremacy over recent art approaches.