Sensor-Assisted Machine Learning Models Approach to Equipoise Renewable Energy using Microgrids
| International Journal of Electronics and Communication Engineering |
| © 2023 by SSRG - IJECE Journal |
| Volume 10 Issue 7 |
| Year of Publication : 2023 |
| Authors : Shreenidhi H S, Narayana Swamy Ramaiah |
10.14445/23488549/IJECE-V10I7P107
How to Cite?
Shreenidhi H S, Narayana Swamy Ramaiah, "Sensor-Assisted Machine Learning Models Approach to Equipoise Renewable Energy using Microgrids," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 7, pp. 63-73, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I7P107
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 sensorassisted 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%.
Keywords:
Sensor, Machine learning, Proactive energy management, Microgrids, Renewable energy.
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