Semi-supervised classification of characterized patterns for demand forecasting using smart electricity meters

Smart meters are being gradually adopted by energy providers for commercial use due to multiple benefits. The extraction of actionable knowledge from smart meter readings can lead to informed decision-making in demand forecasting and consumption analysis. This paper extends an incremental learning approach for pattern characterization in a smart meter data stream environment, with the incorporation of a semi-supervised classification feature. The incremental pattern characterization learning (IPCL) algorithm autonomously learns from a smart meter environment and accumulates patterns in a columnar structure. The introduction of semi-supervised classification improves the quality and usability of the learning outcomes. We report outcomes demonstrating the classification of incremental learning outcomes, separation of cyclic patterns from exceptions, and a capacity to interpose new dimensions from the problem domain.