Research on application of big data and machine learning in emergency power restoration in residential areas

This study focuses on the application of big data and machine learning technology in emergency power repair in residential communities. With the acceleration of urbanization, the stable operation of power system is very important for modern urban life, while the traditional power system repair methods face many challenges. Through empirical research, this paper shows how big data analysis and machine learning models, especially random forests and support vector machines (SVM), can be used to improve the accuracy and efficiency of fault diagnosis. Experimental results show that these methods are superior to traditional methods in key performance indicators such as accuracy, recall rate and F1 score. In addition, the paper also explores the role of big data in resource optimal allocation and user interaction enhancement, and finds that data-driven approaches can significantly improve the efficiency of resource allocation and user satisfaction. The study highlights the potential of machine learning techniques in predicting and dealing with power system failures, while also revealing the possibility of optimizing resource allocation and improving user service experiences. These improvements are critical to improving the reliability of the power supply and reducing outages.