A single point sensing approach for residential power monitoring with appliance recognition using random forest
This work utilized machine learning, specifically Random Forest, as a classifier to recognize appliance signal from an aggregate energy consumption signal obtained using a single point, nonintrusive load monitoring approach. Appliance level feedback allows energy consumers to make informed decisions and employ energy management strategies to reduce the use of electricity. A mixture-of-experts approach was applied and the appliance models were trained to recognize appliance signals both from pure and aggregate signals of up to three appliances at the same time. Consumption signals of appliances with highly differentiated and slightly differentiated wattages were considered in this study. The Random Forest algorithm resulted in high scores averaging between 97 % to 100 % for both precision and recall for the desired appliance signal.
Cabantac, S. E. P., Garcia, F. C. C., & Macabebe, E. Q. B. (2018, October). A Single Point Sensing Approach for Residential Power Monitoring with Appliance Recognition Using Random Forest. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 1154-1157). IEEE.