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This paper presents a novel methodology for the incremental characterization and prediction of electricity consumption based on smart meter readings. A self-learning algorithm is developed to incrementally discover patterns in a data stream environment and sustain acquired knowledge for subsequent learning. It generates an evolving columnar structure composed of learning outcomes from each phase. This columnar structure characterizes electricity consumption and thus exposes significant patterns and continuity over time. The proposed technique is applied to smart meter data collected from RMIT University premises. Results show the potential for incremental pattern characterization learning in electricity consumption analysis and forecasting.