Abstract
Smart grids provide a key driver for smart city development. The smart city power supply data visualization can realize the power characteristic information of various attributes and operating states in the online monitoring data of massive power equipments in a graphical and visual presentation, which provides a powerful guarantee for timely and effective monitoring and analysis of equipment operating status. However, with the rapid development of smart cities, the complexity of urban power data and the ever-increasing amount of data hinder the power managers’ understanding and analysis of the power supply situation. Based on the smart city power supply data, a novel visual analysis system ElectricVis for urban power supply situation is proposed, which can interactively analyze large-scale urban power supply data. ElectricVis reduces the difficulty of understanding urban power supply situations by adopting novel visual graphic designs and time patterns that display power data in multiple scales. ElectricVis also provides different visual views and interaction methods for interrelated hierarchical data in urban power data, which is critical for detecting the cause of anomalous data. Finally, we evaluated our system through case studies and analysis by power experts.
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References
Ballal MS, Jaiswal GC, Tutkane DR, Venikar PA, Mishra MK, Suryawanshi HM (2017) Online condition monitoring system for substation and service transformers. IET Electr Power Appl 11(7):1187–1195
Gegner KM, Overbye TJ, Shetye KS, Weber JD (2016) Visualization of power system wide-area, time varying information. In: 2016 IEEE Power and Energy Conference at Illinois (PECI). IEEE, pp 1–4
Baba, KVS, Narasimhan SR, Jain NL, Singh A, Shukla R, Gupta A (2016) Synchrophasor based real time monitoring of grid events in Indian power system. In: 2016 IEEE International Conference on Power System Technology (POWERCON). IEEE, pp 1–5
Cabrera IR, Barocio E, Betancourt RJ, Messina AR (2017) A semi-distributed energy-based framework for the analysis and visualization of power system disturbances. Electr Power Syst Res 143:339–346
Li Y, Zhang H, Zhou G, Liu G, Feng Z, Meng Q (2017) Real-time synchronous data visualization for wide area power systems. In: 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, pp 1–6
Fang F, Yuan X, Wang L, Liu Y, Luo Z (2018) Urban land-use classification from photographs. IEEE Geosci Remote Sens Lett 99:1–5
Yuan X, Sarma V (2011) Automatic urban water-body detection and segmentation from sparse ALSM data via spatially constrained model-driven clustering. IEEE Geosci Remote Sens Lett 8(1):73–77
Wang L, Fang F, Yuan X, Luo Z, Liu Y, Wan B, Zhao Y (2017) Urban function zoning using geotagged photos and openstreetmap. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, pp 815–818
Ye Y, Zuo Z, Yuan X, Zhang S, Zeng X, An Y, Chen B (2017) Geographically weighted regression model for urban traffic black-spot analysis. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, pp 4866–4869
Frančula N (2009) International Journal of Geographical Information Science. Geodetski list: glasilo Hrvatskoga geodetskog društva 63(4):390
Zhang J, Yanli E, Ma J, Zhao Y, Xu B, Sun L, Yuan X (2014) Visual analysis of public utility service problems in a metropolis. IEEE Trans Vis Comput Graph 20(12):1843–1852
Aman H, Irani P, Amini F (2014) Revisiting crisis maps with geo-temporal tag visualization. In: 2014 IEEE Pacific Visualization Symposium. IEEE, pp 177–184
Liu D, Weng D, Li Y, Bao J, Zheng Y, Qu H, Wu Y (2016) Smartadp: visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Trans Vis Comput Graph 23(1):1–10
Zhang J, Ahlbrand B, Malik A, Chae J, Min Z, Ko S, Ebert DS (2016) A visual analytics framework for microblog data analysis at multiple scales of aggregation. In: Computer Graphics Forum. pp 441–450
Sun G, Liang R, Qu H, Wu Y (2016) Embedding spatio-temporal information into maps by route-zooming. IEEE Trans Vis Comput Graph 23(5):1506–1519
Zhao Y, Luo F, Chen M, Wang Y, Xia J, Zhou F, Chen W (2019) Evaluating multi-dimensional visualizations for understanding fuzzy clusters. IEEE Trans Vis Comput Graph 25(1):12–21
Shneiderman B, Bederson BB (2003) The eyes have it: A task by data type taxonomy for information visualizations. In: The craft of information visualization, Morgan Kaufmann, pp 364–371
D3.js. https://d3js.org
Li D, Mei H, Shen Y, Su S, Zhang W, Wang J, Chen W (2018) ECharts: a declarative framework for rapid construction of web-based visualization. Vis Inform 2(2):136–146
Chen SL, Zhang JY, Lu XG, Chou KC, Chang YA (2006) Application of Graham scan algorithm in binary phase diagram calculation. J Phase Equilibria Diffus 27(2):121–125
Park D, Drucker SM, Fernandez R, Elmqvist N (2018) Atom: a grammar for unit visualizations. IEEE Trans Vis Comput Graph 24(12):3032–3043
Wang Baldonado, MQ, Woodruff A, Kuchinsky A (2000) Guidelines for using multiple views in information visualization. In: Proceedings of the Working Conference on Advanced Visual Interfaces. ACM, pp 110–119
Zhao J, Chevalier F, Balakrishnan R (2011) KronoMiner: using multi-foci navigation for the visual exploration of time-series data. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp 1737–1746
Acknowledgements
This work was supported in part by the Natural Science Foundation of Anhui Province of China under Grant 1708085MF158, in part by the National Natural Science Foundation of China under Grants 61472115, 61672201, in part by the Visiting Scholar Researcher Program at North Texas University through the State Scholarship Fund of the China Scholarship Council under Grant 201706695044, and in part by the Key Project of Transformation and Industrialization of Scientific and Technological Achievements of Intelligent Manufacturing Technology Research Institute of Hefei University of Technology under Grant IMICZ2017010.
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Lu, Q., Xu, W., Zhang, H. et al. ElectricVIS: visual analysis system for power supply data of smart city. J Supercomput 76, 793–813 (2020). https://doi.org/10.1007/s11227-019-02924-4
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DOI: https://doi.org/10.1007/s11227-019-02924-4