基于Isolation Forest和Random Forest相结合的智能电网时间序列数据异常检测算法

智能电网的信息系统是保障电力行业正常运行的基础，而智能电网中各种时间序列数据的分析结果是衡量信息系统稳定运行的重要依据。传统的时间序列数据异常检测算法很难同时兼顾准确性和实时性。本文引入基于Isolation Forest和Random Forest相结合的智能电网时间序列数据异常检测算法，结合无监督学习算法和有监督学习算法的优点，实现机器自动标注和自动学习阈值，人工标注少量特征值，从一定程度上提高了时间序列数据异常检查准确性和实时性，可以满足智能电网时间序列数据异常检测需求，从而达到提升智能电网信息安全的目的。

An Anomaly Detection Algorithm for Smart Grid Time Series Data #br# Based on Combination of Isolation Forest and Random Forest

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.