中圖分類號(hào)： TP301.6；TK01+8
文獻(xiàn)標(biāo)識(shí)碼： A
DOI： 10.19358/j.issn.2096-5133.2021.05.008
引用格式： 黃家續(xù)，曾獻(xiàn)輝，施陳俊. 基于實(shí)時(shí)數(shù)據(jù)流特征提取的設(shè)備能耗異常識(shí)別算法研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40(5)：45-50.

Research on equipment energy consumption anomaly identification algorithm based on real-time data stream feature extraction

Abstract： Energy saving of energy consumption equipment is a very important part of the enterprise energy saving and emission reduction, timely discovery of abnormal energy consumption equipment operation, to reduce unnecessary energy consumption of enterprises is of great significance.In this paper, based on the collected real-time data stream of equipment energy consumption, a new method of equipment energy consumption anomaly recognition and classification based on multi-feature extraction was proposed.Firstly, the characteristic parameters such as low energy consumption time ratio, high energy consumption time quantity and DTW distance were extracted from the sample data. Then, the isolated forest algorithm and K-means clustering algorithm were used to carry out type calibration for each sample data. Finally, the abnormal classification algorithm of equipment energy consumption combining attention mechanism and LSTM was constructed.The experimental results show that the classification accuracy of the algorithm reaches 97.76%, which can effectively identify the abnormal energy consumption of different types of equipment, so as to provide a decision-making basis for enterprises to make timely treatment and reduce the loss of energy consumption.

Key words : feature extraction；attention mechanism；LSTM；classification algorithm