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

Date

2021-11-29

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

International Conference on Applied Energy

Type

Conference

Peer reviewed

Yes

Abstract

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.

Description

open access proceedings

Keywords

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

Citation

Alsalemi, A., Amira, A., Malekmohamadi, H., Diao, K., Bensaali, F. (2021) Elevating Energy Data Analysis with M2GAF: Micro-Moment Driven Gramian Angular Field Visualizations. Proceedings of 13th International Conference on Applied Energy ICAE2021, Nov-Dec 2021, Online.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)