@Article{cmc.2024.051820,
AUTHOR = {Anam Mughees, Muhammad Kamran},
TITLE = {Novel Fractal-Based Features for Low-Power Appliances in Non-Intrusive Load Monitoring},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {80},
YEAR = {2024},
NUMBER = {1},
PAGES = {507--526},
URL = {http://www.techscience.com/cmc/v80n1/57401},
ISSN = {1546-2226},
ABSTRACT = {Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually. Prior studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power appliances. Low-power consumer appliances have comparable power consumption patterns, which can complicate the detection task and can be mistaken as noise. This research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures. A hybrid feature extraction method based on mono-fractal and multi-fractal analysis is proposed for identifying low-power appliances. Fractal dimension, Hurst exponent, multifractal spectrum and the Hölder exponents of switching current transient signals are extracted to develop various ‘turn-on’ appliance signatures for classification. Four classifiers, i.e., deep neural network, support vector machine, decision trees, and K-nearest neighbours have been optimized using Bayesian optimization and trained using the extracted features. The simulated results showed that the proposed method consistently outperforms state-of-the-art feature extraction methods across all optimized classifiers, achieving an accuracy of up to 96 % in classifying low-power appliances.},
DOI = {10.32604/cmc.2024.051820}
}