Volume 23: Sustainable Energy Solutions for a Post-COVID Recovery towards a Better Future: Part VI

Elevating Energy Data Analysis with M2GAF: Micro-Moment Driven Gramian Angular Field Visualizations Abdullah Alsalemi, Abbes Amira, Hossein Malekmohamadi, Kegong Diao, Faycal Bensaali



With global pollution and building power consumption on the rise, energy efficiency research has never been more necessary. Accordingly, data visualization is one of the most sought after 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 M2GAF, is described. The proposed tool can be used by energy efficiency researchers to yield deeper understanding of building energy consumption data and its environmental conditions. Current results show sample G2GAF representations for three power consumption datasets. In summary, the proposed tool can unveil novel energy time-series analysis possibilities as well as original data visualizations that can yield deeper insights, and in turn, improved energy efficiency.

Keywords Gramian angular fields, energy efficiency, artificial intelligence, data visualization, micro-moments, internet of energy.

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