Novel Fractal-Based Features for Low-Power Appliances in Non-Intrusive Load Monitoring

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.

Conversion of adverse data corpus to shrewd output using sampling metrics

An imbalanced dataset is commonly found in at least one class,which are typically exceeded by the other ones.A machine learning algorithm(classifier)trained with an imbalanced dataset predicts the majority class(frequently occurring)more than the other minority classes(rarely occurring).Training with an imbalanced dataset poses challenges for classifiers;however,applying suitable techniques for reducing class imbalance issues can enhance classifiers’performance.In this study,we consider an imbalanced dataset from an educational context.Initially,we examine all shortcomings regarding the classification of an imbalanced dataset.Then,we apply data-level algorithms for class balancing and compare the performance of classifiers.The performance of the classifiers is measured using the underlying information in their confusion matrices,such as accuracy,precision,recall,and F measure.The results show that classification with an imbalanced dataset may produce high accuracy but low precision and recall for the minority class.The analysis confirms that undersampling and oversampling are effective for balancing datasets,but the latter dominates.