Approach in Nonintrusive Type I Load Monitoring Using Subtractive Clustering

作者：Henao Nilson; Agbossou Kodjo; Kelouwani Sousso; Dube Yves; Fournier Michael

来源：IEEE Transactions on Smart Grid, 2017, 8(2): 812-821.

DOI：10.1109/TSG.2015.2462719

• Energy management
• home automation
• pattern recognition
• substractive clustering
• unsupervised learning

In this paper, a low-sampling-rate and nonintrusive appliance loads monitoring (NIALM), which required only few tuning parameters and which is little sensitive to the grid power noise, is presented. Using transformed active power transitions as features, the proposed approach is based on the subtractive clustering and the maximum likelihood classifier. In order to validate the NIALM, a 1 Hz sampling rate experimental data from the reference energy disaggregation dataset is selected. The validation results with six commonly found ON/OFF residential appliances indicate that the proposed approach is effective. In addition, the obtained results from a Monte Carlo simulation suggest that this approach is less sensitive to power grid noise than a K-mean-based NIALM method.

• 出版日期2017-3