Optimized Multi‐Scale Attention Convolutional Neural Network for Micro‐Grid Energy Management System Employing in Internet of Things

ABSTRACT The combination of micro‐grid energy management systems (EMSs) with the Internet of Things (IoT) offers a promising way to improve energy use and distribution. However, challenges such as device compatibility and the difficulty of managing energy efficiently make it hard to implement these systems effectively. This study offers a significant advancement in energy management by using IoT for microgrid systems. An Optimized Multi‐scale Attention Convolutional Neural Network for microgrid EMS employing IoT (OMACNN‐MGEMS‐IoT) is proposed in this study, which enables efficient monitoring and control of energy resources. The proposed model's input data are gathered from the MQTT dataset. This research employs a Regularized Bias‐aware Ensemble Kalman Filter (RBAEKF) for pre‐processing input data, ensuring the removal of outliers and updating missing values. The MACNN is then used for effective fault detection within the microgrid. To enhance its performance, the Sheep Flock Optimization Algorithm (SFOA) is introduced to optimize the MACNN parameters, ensuring accurate fault detection. Implemented on the MATLAB platform, the performance of the OMACNN‐MGEMS‐IoT method is assessed through various performance metrics, demonstrating significant improvements. Notably, the proposed method achieves higher cost reductions of 25%, 22%, and 26% compared to existing approaches such as the IoT platform for energy management in multi‐micro grid systems (IoT‐PEM‐MMS), a micro‐grid system infrastructure implementing IoT for efficient energy management in buildings (MSII‐IoT‐EEM) and a hybrid deep learning‐based online energy management scheme for industrial microgrids (HDL‐OEM‐IM). The findings highlight the impact of the proposed OMACNN‐MGEMS‐IoT method in enhancing energy efficiency and cost‐effectiveness in microgrid systems.