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

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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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Acknowledgements

This work was supported by the Science and Technology Project of China Southern Power Grid Corporation (ZBKJXM2018 0157), and the National Natural Science Foundation of China (Grant Nos.61772456, 61761136020).

Author information

Authors and Affiliations

  1. Electric Power Research Institute, China Southern Power Grid, Guangzhou, 510663, China

    Yong Xiao & Bin Qian

  2. Digital Grid Research Institute, China Southern Power Grid, Guangzhou, 510663, China

    Kaihong Zheng

  3. The State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058, China

    Supaporn Lonapalawong, Wenjie Lu, Zexian Chen, Tianye Zhang & Wei Chen

  4. School of Computer Science, Zhejiang University, Hangzhou, 310058, China

    Xin Wang

Authors
  1. Yong Xiao
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  2. Kaihong Zheng
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  3. Supaporn Lonapalawong
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  4. Wenjie Lu
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  5. Zexian Chen
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  6. Bin Qian
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  7. Tianye Zhang
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  8. Xin Wang
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  9. Wei Chen
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Corresponding author

Correspondence to Kaihong Zheng.

Additional information

Yong Xiao is a professor of engineering in Electric Power Research Institute at China Southern Power Grid, China. His main research interests include intelligent electric power metering technology.

Kaihong Zheng is member of China Computer Federation (CCF) in Digital Grid Research Institute at China Southern Power Grid, China. His main research interests include intelligent electricity consumption and metering technology.

Supaporn Lonapalawong is currently a PhD student in the College of Computer Science and Technology at the Zhejiang University, China. Her research interests include data mining and visual analytics.

Wenjie Lu is currently a master student in the College of software engineering at the Zhejiang University, China. His research interests include data mining and visual analytics.

Zexian Chen is currently a master student in the College of Computer Science and Technology at the Zhejiang University, China. His research interests include information visualization and visual analytics.

Bin Qian is a senior engineer in Electric Power Research Institute at China Southern Power Grid, China. His research interests include smart electricity technology.

Tianye Zhang is currently a PhD student in the College of Computer Science and Technology at the Zhejiang University, China. Her research interests include information visualization and visual analytics.

Xin Wang is an associate professor in School of Computer Science at Zhejiang University, China. His research interests is on artificial intelligence, computer vision, computer animation and AI design.

Wei Chen is a professor at the State Key Lab of CAD & CG, Zhejiang University, China. His research interests is on visualization and visual analysis.

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Xiao, Y., Zheng, K., Lonapalawong, S. et al. EcoVis: visual analysis of industrial-level spatio-temporal correlations in electricity consumption. Front. Comput. Sci. 16, 162604 (2022). https://doi.org/10.1007/s11704-020-0088-8

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