Abstract

Accurate identification of appliance operations is crucial for effective energy management in smart energy monitoring or energy disaggregation. This paper proposes a multi-label classification approach that utilizes adaptive feature integration of steady-state features (current ( I), active power (P), reactive power (Q), power factor (PF)) and steady-state change features (ΔI, ΔP, and ΔQ) obtained from low-frequency meter data. Recursive Feature Elimination with Cross-Validation (RFECV) is employed as the feature selection technique to determine the optimal subset of features for enhancing individual appliance identification. Experiments are conducted on two datasets, AMPds and ECO, consisting of high-power appliances that consume a significant amount of power when turned on (e.g., Wall Oven, Clothes Dryer, Kettle) and low-power appliances that consume less power (Fridge, Freezer). Results demonstrate significant performance improvements on the AMPds dataset with the additional selective features, increasing the F-score for the Wall Oven from 0.56 to 0.70, outperforming Forward Sequential Feature Selection (FSFS) as a comparator of feature selection method and a benchmarking Denoising Autoencoder (DAE) neural network model. In contrast, the ECO dataset yields marginal performance improvement for most appliances due to subtler steady-state change signatures. Additional sensitivity analyses indicate that the approach can achieve superior performance compared to using the baseline features under the data sampling frequencies of 5 and 10 minutes, noisy levels of 1% and 5%, and appliance concurrency up to 3 appliances. The proposed approach of deploying basic features and adapting to variations in measurement setups is suitable for use in resource-constrained environments.