Abstract: The information system of smart grid is the basis to ensure the normal operation of power industry, and the analysis results of various time series data in smart grid are the important basis to measure the stable operation of information system. Traditional time series data anomaly detection algorithm is difficult to take into account both accuracy and real-time. In this paper, an anomaly detection algorithm for smart grid time series data based on Isolation Forest and Random Forest is introduced. It combines the advantages of unsupervised learning algorithm and supervised learning algorithm, realizes automatic machine annotation and automatic learning threshold, labels a small number of eigenvalues manually, and improves the accuracy and real-time of time series data anomaly detection to a certain extent. The algorithm can meet the needs of anomaly detection of smart grid time series data, so as to improve the information security of smart grid.

Key words: Isolation Forest algorithms, Random Forest algorithms, anomaly detection algorithms, time series data, smart grid