A Smart IoT Framework for Remote Monitoring and Management of Microgrid Power Supply Systems

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. 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 forecasting 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 a comparative analysis, the suggested method is assessed with various evaluation measures, such as R-squared (96.85%), MAE (3.01), and RMSE (4.81). The proposed model improves energy forecasting, allowing for better energy production planning and scheduling, decreasing reliance on external power sources, and increasing cost savings within the manufacturing industry. Additionally, the greater forecasting capabilities permit real-time adaptive energy management, which contributes to maintaining the grid, reducing peak load demand, and enhancing the utilization of RES and energy management in microgrids.