Sensor-Assisted Machine Learning Models Approach to Equipoise Renewable Energy using Microgrids

Abstract

Sensor-assisted machine learning framework for renewable energy balancing in microgrids (MG). Integrating renewable energy sources into microgrid systems brings the challenge of managing renewable energy generation's intermittent and variable nature. The proposed framework leverages sensor technology to collect real-time data on energy generation, consumption, and grid conditions. Machine learning algorithms are then applied to analyze this data and optimize energy flow within the microgrid. The proposed machine learning models can use historical data to forecast renewable energy generation and demand, enabling proactive energy management (PEM). The framework also incorporates optimization techniques to allocate energy efficiently, considering storage capacity, load demand, and grid stability factors. The sensor-assisted machine learning algorithm enhances microgrid systems' reliability, precision, fi-score, recall, and support by dynamically adapting energy generation and consumption based on real-time conditions. This framework represents a significant step towards achieving sustainable and resilient microgrid operations by maximizing the utilization of renewable energy resources. The optimized results by sensing the physical quantity achieved an accuracy of 70%.