EcoVis: visual analysis of industrial-level spatio-temporal correlations in electricity consumption

Abstract

Closely related to the economy, the analysis and management of electricity consumption has been widely studied. Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption, which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry, weather etc.. In the meantime, the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis. In this paper, we introduce EcoVis, a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data. We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis, but also introduce a novel visual representation to display the distributions of multiple instances in a single map. We implement the system with the cooperation with domain experts. Experiments are conducted to demonstrate the effectiveness of our method.

Graphical abstract

Keywords

spatio-temporal data / electricity consumption / correlation analysis / visual analysis / visualization

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Acknowledgements

This work was supported by the Science and Technology Project of China Southern Power Grid Corporation (ZBKJXM20180157), and the National Natural Science Foundation of China (Grant Nos.61772456, 61761136020).

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