Neuro-Fuzzy-Based IoT Assisted Power Monitoring System for Smart Grid

The Internet of Things (IoT) is commonly utilized for intelligent energy control, industrial automation, and a host of other applications. IoT sensors are installed in various stages of the smart grid (SG) to track and manage network statistics for safe and efficient power delivery. The challenges in the integration of IoT-SG must be overcome for the network to function efficiently. An IoT-based smart grid energy monitoring system depending on neuro-fuzzy is proposed in this paper. At the core of the operator, a wireless sensor network (WSN) is employed to compute and transfer the necessary parameters for the prediction model. This project revolves around an IoT-based energy monitor, which can track and analyze electrical parameters, including current, voltage, active power, and load power consumption. For the collection of real-time electrical data from users, the IoT-based program is used. Based on this data, consumers and electric power companies in the SG model can better control their usage to minimize billing costs. The results obtained show that the performance of hybridized solar/wind power plants will be improved with the help of ANFIS controller to a great extent. Results indicate efficiency of 99.74% in the proposed ANFIS control system.