Efficient Time-Series Feature Extraction and Ensemble Learning for Appliance Categorization Using Smart Meter Data

Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids. It offers substantial benefits across social, environmental, and economic dimensions. To effectively realize these advantages, a fine-grained collection and analysis of smart meter data is essential. However, the high dimensionality and volume of such time-series present significant challenges, including increased computational load, data transmission overhead, latency, and complexity in real-time analysis. This study proposes a novel, computationally efficient framework for feature extraction and selection tailored to smart meter time-series data. The approach begins with an extensive offline analysis, where features are derived from multiple domains—time, frequency, and statistical—to capture diverse signal characteristics. Various feature sets are fused and evaluated using robust machine learning classifiers to identify the most informative combinations for automated appliance categorization. The best-performing fused features set undergoes further refinement using Analysis of Variance (ANOVA) to identify the most discriminative features. The mathematical models, used to compute the selected features, are optimized to extract them with computational efficiency during online processing. Moreover, a notable dimension reduction is secured which facilitates data storage, transmission, and post processing. Onward, a specifically designed LogitBoost (LB) based ensemble of Random Forest base learners is used for an automated classification. The proposed solution demonstrates a high classification accuracy (97.93%) for the case of nine-class problem and dimension reduction (17.33-fold) with minimal front-end computational requirements, making it well-suited for real-world applications in smart grid environments.