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EcoVis: visual analysis of industrial-level spatio-temporal correlations in electricity consumption |
Yong XIAO1, Kaihong ZHENG2( ), Supaporn LONAPALAWONG3, Wenjie LU3, Zexian CHEN3, Bin QIAN1, Tianye ZHANG3, Xin WANG4, Wei CHEN3 |
1. Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, China 2. Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510663, China 3. The State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China 4. School of Computer Science, Zhejiang University, Hangzhou 310058, China |
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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.
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Keywords
spatio-temporal data
electricity consumption
correlation analysis
visual analysis
visualization
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Corresponding Author(s):
Kaihong ZHENG
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Just Accepted Date: 13 May 2020
Issue Date: 18 September 2021
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