Elevating Energy Data Analysis with M2GAF: Micro-Moment Driven Gramian Angular Field Visualizations

open access proceedings With global pollution and buildings power consumption on the rise, energy efficiency research has never been more necessary. Accordingly, data visualization is one of the most sought challenges in data analysis, especially in energy efficiency applications. In this paper, a novel micro-moment Gramian angular fields time-series transformation of energy signals and ambient conditions, abbreviated as M2 GAF, is described. The proposed tool can be used by energy efficiency researchers to yield a deeper understanding of building energy consumption data and its environmental conditions. Current results show sample G2 GAF representation for three power consumption datasets. In summary, the proposed tool can unveil novel energy time-series analysis possibilities as well as original data visualization that can yield deeper insights, and in turn, improved energy efficiency.