Research on microgrid power supply equipment system based on intelligent IoT remote monitoring and management
Abstract
The incorporation of renewable energy sources (RES) into grid-connected microgrids, especially in the manufacturing industry, has added significant complications to power generation forecasting, control, and management. As microgrids rely on RES, such as solar and wind power, there is a crucial need for more accurate energy predictions and better management systems. Existing IoT-enabled microgrid monitoring systems still suffer from limited forecasting accuracy, inefficient hyperparameter optimization, and inadequate adaptation to dynamic renewable energy conditions in manufacturing environments.Research investigates the use of ML methods, particularly Adaptive tunicate swarm optimized random forests (ATSO-RF), to increase the efficacy and reliability of microgrid power systems in the manufacturing industry. It utilizes historical energy generation data, weather patterns, and dynamic grid conditions to create a sophisticated model for power generation prediction and management systems. Pre-processing with Z-score normalization and Linear Discriminant Analysis (LDA) was used to reduce the number of topographies by decreasing variance across different energy states or microgrid conditions, thereby allowing for more accurate energy forecasting classification. The ATSO-RF algorithm outperforms traditional models by providing better predictive accuracy and improved energy management. The proposed method is implemented by using Python 3.10.1. In comparative evaluation against LGBM (R2 = 84%, MAE = 3.93, RMSE = 5.77) and RF (R2 = 95.2%, MAE = 22.8, RMSE = 31.7), the proposed ATSO-RF achieves R2 = 96.85%, MAE = 3.01, and RMSE = 4.81. The model also attains energy efficiency of 86%, fault detection rate of 93%, and system uptime of 94.5%, demonstrating practical potential for real-time adaptive energy management and cost-effective integration of renewable energy sources in manufacturing microgrids.The simulation results suggest potential for real-time adaptive energy management in manufacturing microgrids; hardware validation on a physical testbed is identified as a priority for future work.