New Appliance Signatures for NILM Based on Mono-Fractal Features and Multi-Fractal Formalism

Abstract

Smart energy management demands better ways to understand the energy consumption of buildings. Nonintrusive Load Monitoring (NILM) is an emerging technique that disaggregates total building consumption to individual appliances. However, low-power appliances pose a challenge as they exhibit similar power consumption patterns that are difficult to identify and distinguish from background noise. This research aims to bridge this gap by developing feature extraction and classification techniques specifically for low-power NILM. This may facilitate the development of targeted energy-saving strategies and result in more precise energy-use monitoring. To effectively identify and differentiate low-power appliances within a NILM system, this work proposes a novel feature extraction approach that combines mono-fractal and multifractal analysis of appliance startup current transients. Mono-fractal features, including fractal dimension and Hurst exponent, are extracted alongside Lacunarity and Multifractal features comprising the singularity spectrum and Hölder exponents. These features collectively create comprehensive appliance signatures that capture the unique characteristics of each appliance during its turn-on event. Building upon these extracted features, the work investigates the performance of three machine learning classifiers for appliance classification: deep neural network, support vector machine, and K-nearest neighbours. These classifiers are optimized using Bayesian optimization to achieve optimum performance. The proposed method demonstrates significant improvement over existing feature extraction approaches across all optimized classifiers, achieving an accuracy of up to 98.3% in classifying low-power appliances.

Keywords

• Non-intrusive load monitoring
• current transients
• multifractal formalism
• fractal dimension
• appliance classification
• feature extraction