Improving Grid Resilience Using XG Boost Classifier in Smart Grid System
Keywords:
Smart systems, electricity power companies, smart grids, renewable energy, energy efficiency, Artificial Intelligence (AI), data analytics, smart meters, demandside management, sustainable energy, smart grid technology, grid resilience, energy transition, carbon emissionsAbstract
Smart grids now integrate new devices to monitor the real-time conditions under electricity power companies. These technological systems leverage Artificial Intelligence (AI) and data analytics to support power companies with possible new ways toward realtime monitoring, control, and optimization of electricity grids. Such grids can respond to variations in power demand, contain renewable generation sources, and solve issues like energy conservation and cost reduction. These grids thus allow for direct generation of data for faster identification of faults, enabling many aspects such as improved load management and predictive maintenance, which reduces downtime and service interruptions. The smart meter also gives detailed information to customers on energy consumption patterns and behaviors as well as enhances customer engagement toward energy conservation. Customers then receive handy electricity services, with reliability on billing and possible cost savings from programs that induce demand response for the use of energy during off-peak hours. Furthermore, these systems will contribute to reducing carbon emissions and making the transition to cleaner and sustainable energy systems much easier. Of course, the most outstanding contribution toward these smart systems will be by governments, energy providers, and technology developers because despite all the advantages, some of the disadvantages are of enormous investment that these technologies require, along with cybersecurity threats and highly complex integration considerations when introducing them into the existing infrastructure. All these would require concerted efforts by government bodies, energy providers, and technology developers to overcome hurdles and focus on regulatory frameworks that ensure fair distribution of the benefits. Adopting smart systems in the electricity generation industry is an integral part of an energy future that is efficient, resilient, and sustainable. Smart technologies have changed massively how electricity is produced, distributed, and consumed advantageously for both providers and end-users while also supporting everyone's global sustainability goals.
📊 Article Downloads
References
Aguida, M. E., Che, Y., Aouiche, E. M., & Mouhafid, M. (2025). Advanced filter-based feature selection for improved load identification in solar-powered aircraft distribution systems using XGBoost. Measurement, 246, 116764. https://doi.org/10.1016/J.MEASUREMENT.2025.116764
Dhaliwal, S. S., Nahid, A. Al, & Abbas, R. (2018). Effective Intrusion Detection System Using XGBoost. Information 2018, Vol. 9, Page 149, 9(7), 149. https://doi.org/10.3390/INFO9070149
GHOSH, A., & KOLE, A. (2023). A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction. Authorea Preprints. https://doi.org/10.36227/TECHRXIV.16863145.V1
Khan, I. U., Aslam, N., AlShedayed, R., AlFrayan, D., AlEssa, R., AlShuail, N. A., & Al Safwan, A. (2022). A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost). Sensors 2022, Vol. 22, Page 9235, 22(23), 9235. https://doi.org/10.3390/S22239235
Le, T. T. H., Oktian, Y. E., & Kim, H. (2022). XGBoost for Imbalanced Multiclass ClassificationBased Industrial Internet of Things Intrusion Detection Systems. Sustainability 2022, Vol. 14, Page 8707, 14(14), 8707. https://doi.org/10.3390/SU14148707
Mehdary, A., Chehri, A., Jakimi, A., & Saadane, R. (2024). Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection. Sensors 2024, Vol. 24, Page 1230, 24(4), 1230. https://doi.org/10.3390/S24041230
Nawaz, A., Ali, T., Mustafa, G., Rehman, S. U., & Rashid, M. R. (2023). A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost. Intelligent Systems with Applications, 17, 200168. https://doi.org/10.1016/J.ISWA.2022.200168
Pamir, Javaid, N., Akbar, M., Aldegheishem, A., Alrajeh, N., & Mohammed, E. A. (2022). Employing a Machine Learning Boosting Classifiers Based Stacking Ensemble Model for Detecting Non Technical Losses in Smart Grids. IEEE Access, 10, 121886–121899. https://doi.org/10.1109/ACCESS.2022.32228
Patnaik, B., Mishra, M., Bansal, R. C., & Jena, R. K. (2021). MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid. Applied Energy, 285, 116457. https://doi.org/10.1016/J.APENERGY.2021.116457
Sharma, A., & Tiwari, R. (2024). Anomaly detection in smart grid using optimized extreme gradient boosting with SCADA system. Electric Power Systems Research, 235, 110876. https://doi.org/10.1016/J.EPSR.2024.110876
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
