Integrating Machine Learning for Anomaly Detection and Pattern Recognition in Smart Grid Power Data
DOI:
https://doi.org/10.13052/dgaej2156-3306.4037Keywords:
Smart grids, machine learning, Anomaly Detection, Pattern Recognition, Power SystemsAbstract
The integration of machine learning (ML) for anomaly detection and pattern recognition in smart grid power data represents a significant advancement in the management and optimization of modern electrical power systems. This research explores how ML algorithms can process and analyze the enormous volumes of data generated by smart grids, focusing specifically on identifying anomalies and recognizing patterns that traditional methods might miss. By implementing ML techniques, this study aims to enhance the predictive capabilities, operational efficiency, and overall security of smart grid systems while addressing critical challenges such as data quality, cybersecurity threats, and scalability issues.
The transformative potential of ML in smart grid management is demonstrated through various applications, including load forecasting, fault detection, and intrusion prevention. The research examines both supervised and unsupervised learning approaches, evaluating their effectiveness in different scenarios. Additionally, the study highlights the importance of deep learning models in handling the complex, high-dimensional data characteristic of smart grid environments.
The findings indicate that ML integration significantly improves anomaly detection rates and pattern recognition accuracy, contributing to more stable and reliable power distribution systems. Furthermore, the research identifies key areas for future development, including the need for more sophisticated models capable of handling increasingly complex data landscapes and enhanced cybersecurity measures to protect against emerging threats.
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