Choksi, Kushan A.Kushan A.ChoksiJain, Sachin K.Sachin K.Jain2025-08-312025-08-312018-11-27[9781538680032]10.1109/ICICSP.2018.85497732-s2.0-85060027145https://d8.irins.org/handle/IITG2025/23450This paper presents the development of novel extraction feature used for load disaggregation from aggregated demand profile. Non-intrusive load monitoring tackles the issue of appliances identification inside a residential building. New extraction features used for load identification are based on power, V-I mutual locus and wave-shape features for specific appliances which offers better or generally comparable results in comparison to popular methods. A novel dynamic computational index is developed to achieve a robust feature extraction using V-I mutual locus. Power patterns are used for identification of multi-state devices. Specific screening and classification of extraction features are so developed to get better load disaggregation and lesser computational time. Multi-label classifications approach has been used for the same. The methodology is compared based on precision, classification accuracy, and robustness using experimental data.falseappliances monitoring | clustering | load disaggregation | machine learning | non-intrusive load monitoring (NILM) | smart gridConference Paper44-4827 November 201838549773cpConference Proceeding2