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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2022, Vol. 16 Issue (2) : 162604    https://doi.org/10.1007/s11704-020-0088-8
RESEARCH ARTICLE
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.

Keywords spatio-temporal data      electricity consumption      correlation analysis      visual analysis      visualization     
Corresponding Author(s): Kaihong ZHENG   
Just Accepted Date: 13 May 2020   Issue Date: 18 September 2021
 Cite this article:   
Yong XIAO,Kaihong ZHENG,Supaporn LONAPALAWONG, et al. EcoVis: visual analysis of industrial-level spatio-temporal correlations in electricity consumption[J]. Front. Comput. Sci., 2022, 16(2): 162604.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-0088-8
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I2/162604
Fig.1  The EcoVis interface: (a) the control panel to select the data source, (b) the industry map to show the distribution of different industries, (c) the correlation graph representing correlations across industries, (d) projection views to show the correlation between time windows, (e) the correlation matrix, and (f) the consumption curve
Fig.2  Our system contains three modules: a data module, a data analysis module and the visualization module
Fig.3  The construction process of the correlation graph. (a) Set of all industry; (b) electricity series; (c) matrix construction; (d) adjacency matrices; (e) graph construction
Fig.4  The pattern of projection view: a graph group and a turning point
Fig.5  The construction process of the projection
Correlation analysis method
Case MI TE PCC
Case1 (IY0100-IY0200) 1.25 0.06 0.98
Case2 (IJ6800-IK7000) 1.14 0.11 0.96
Case3 (IA0100-IH6100) 0.94 0.08 0.92
Case4 (IG5300-IG5400) 0.82 0.1 0.89
Case5 (IC3300-ID4420) 0.50 0.15 0.81
Case6 (IJ6800-IC1710) 0.47 0.14 0.51
Case7 (II6400-IM7903) 0.23 0.23 -0.08
Case8 (IB0620-II6500) 0.21 0.21 0.23
Tab.1  Comparison of correlation analysis methods
Fig.6  The first case study analyzes the spatial distribution of industries. (a) The locations of two industry groups are shown in the industry map; (b) the electricity use of two industries are represented in the timeline view
Fig.7  The second case study analyzes the temporal distribution of industries
Fig.8  Identifying the Connections Between the Spatial and the Temporal Correlations. (a) Spatial comparison; (b) Temporal comparison
Fig.9  Overview of the electricity usage of industrial groups. (a) Similar industry group; (b) Correlation view; (c) Electricity usage pattern
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