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

被引:3
作者
Xiao, Yong [1 ]
Zheng, Kaihong [2 ]
Lonapalawong, Supaporn [3 ]
Lu, Wenjie [3 ]
Chen, Zexian [3 ]
Qian, Bin [1 ]
Zhang, Tianye [3 ]
Wang, Xin [4 ]
Chen, Wei [3 ]
机构
[1] China Southern Power Grid, Elect Power Res Inst, Guangzhou 510663, Peoples R China
[2] China Southern Power Grid, Digital Grid Res Inst, Guangzhou 510663, Peoples R China
[3] Zhejiang Univ, State Key Lab CAD & CG, Hangzhou 310058, Peoples R China
[4] Zhejiang Univ, Sch Comp Sci, Hangzhou 310058, Peoples R China
基金
中国国家自然科学基金;
关键词
spatio-temporal data; electricity consumption; correlation analysis; visual analysis; visualization; ENERGY-CONSUMPTION; VISUALIZATION; EXPLORATION; COMPLEX; ANIMATION; ANALYTICS;
D O I
10.1007/s11704-020-0088-8
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:11
相关论文
共 57 条
[1]   A review on applications of ANN and SVM for building electrical energy consumption forecasting [J].
Ahmad, A. S. ;
Hassan, M. Y. ;
Abdullah, M. P. ;
Rahman, H. A. ;
Hussin, F. ;
Abdullah, H. ;
Saidur, R. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 33 :102-109
[2]  
[Anonymous], 2007, P 9 JOINT EUR IEEE V, DOI [10.5555/2384179.2384192, DOI 10.5555/2384179.2384192]
[3]   Visualizing Dynamic Networks with Matrix Cubes [J].
Bach, Benjamin ;
Pietriga, Emmanuel ;
Fekete, Jean-Daniel .
32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014), 2014, :877-886
[4]   Understanding Indirect Causal Relationships in Node-Link Graphs [J].
Bae, Juhee ;
Helldin, Tove ;
Riveiro, Maria .
COMPUTER GRAPHICS FORUM, 2017, 36 (03) :411-421
[5]  
Becker F., 2014, EUROVIS STARS, P1, DOI [10.2312/eurovisstar.20141174, DOI 10.2312/EUROVISSTAR.20141174]
[6]  
Bederson B. B., 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99), P28, DOI 10.1109/INFVIS.1999.801854
[7]   Electricity consumption forecasting in Italy using linear regression models [J].
Bianco, Vincenzo ;
Manca, Oronzio ;
Nardini, Sergio .
ENERGY, 2009, 34 (09) :1413-1421
[8]  
Boardman E, 2010, IEEE POW ENER SOC GE
[9]   A Qualitative Study on the Exploration of Temporal Changes in Flow Maps with Animation and Small-Multiples [J].
Boyandin, Ilya ;
Bertini, Enrico ;
Lalanne, Denis .
COMPUTER GRAPHICS FORUM, 2012, 31 (03) :1005-1014
[10]  
Buschel W., 2018, P CHI 2018 WORKSHOP, VVolume 18