Non-intrusive Industrial Load Identification Based on Random Forest Algorithm and Steady-State Waveform

doi:10.11930/j.issn.1004-9649.202109026

Abstract

Abstract: Non-intrusive industrial load identification can accurately acquire the operation situations of each load in the plant, which is beneficial to the demand-side intelligent power management. The identification method for industrial load is complicated and difficult to implement due to the difficulty in collecting transient data for modeling and the demand for high-precision measuring equipment. Aiming at this situation, a non-intrusive industrial load identification method is proposed using random forest algorithm and steady state waveform. Firstly, the steady state waveform is extracted by monitoring the power state change of the industrial load through the event, and the characteristic data of the steady state waveform of the single load current is constructed according to the difference of the current waveform caused by different performance of the industrial load. Then, by using the high-dimensional data of the steady-state waveform as the sample data, the bootstrap sampling method and the CART algorithm in the random forest algorithm are used to generate multiple decision trees. Finally, the industrial load types are identified by voting multiple decision trees. The actual operating load data of a factory is used as the sample data for simulation, and the effectiveness and rapidity of the proposed identification algorithm are verified with simulation. The simulation results show that the accuracy rate of the proposed identification algorithm is more than 99% with the identification time of 3.36 s, which is much higher than the accuracy rate of 63.8% and the identification time of 6.15 s of the Bayesian identification algorithm. Therefore, the proposed identification algorithm can effectively realize the non-intrusive industrial load identification.

Key words: non-intrusive load, random forest, industrial load, current waveform, load identification

WANG Jian, YI Shuhui, LIU Junjie, LIU Jian. Non-intrusive Industrial Load Identification Based on Random Forest Algorithm and Steady-State Waveform[J]. Electric Power, 2022, 55(2): 82-89.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202109026

https://www.electricpower.com.cn/EN/Y2022/V55/I2/82

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